├── LICENSE ├── README.md ├── config.py ├── data.py ├── dpp.yml ├── examples ├── run_dpp_download_data.ipynb ├── run_dpp_download_data.py ├── run_dpp_from_archive.ipynb ├── run_dpp_from_archive.py ├── run_dpp_from_directory.ipynb └── run_dpp_from_directory.py ├── model.py ├── models ├── detection │ └── 20201002 │ │ ├── dict_hyperopt_t733.pckl │ │ ├── model_hyperopt_t733.h5 │ │ └── trials_hyperopt_ntrials_1000.pckl └── picking │ ├── 20201002_1 │ ├── P │ │ ├── dict_hyperopt_t027.pckl │ │ ├── model_hyperopt_t027.h5 │ │ └── trials_hyperopt_ntrials_050.pckl │ └── S │ │ ├── dict_hyperopt_t009.pckl │ │ ├── model_hyperopt_t009.h5 │ │ └── trials_hyperopt_ntrials_050.pckl │ └── 20201002_2 │ ├── P │ ├── dict_hyperopt_t004.pckl │ ├── model_hyperopt_t004.h5 │ └── trials_hyperopt_ntrials_050.pckl │ └── S │ ├── dict_hyperopt_t023.pckl │ ├── model_hyperopt_t023.h5 │ └── trials_hyperopt_ntrials_050.pckl ├── requirements.txt ├── sample_data ├── CX_20140301 │ ├── CX.PB01..HH.mseed │ └── CX.PB02..HH.mseed ├── CX_20140401 │ ├── CX.PB01..HH.mseed │ └── CX.PB02..HH.mseed └── archive │ └── 2014 │ └── CX │ ├── PB01 │ ├── HHE.D │ │ └── CX.PB01..HHE.D.2014.121 │ ├── HHN.D │ │ └── CX.PB01..HHN.D.2014.121 │ └── HHZ.D │ │ └── CX.PB01..HHZ.D.2014.121 │ └── PB02 │ ├── HHE.D │ └── CX.PB02..HHE.D.2014.121 │ ├── HHN.D │ └── CX.PB02..HHN.D.2014.121 │ └── HHZ.D │ └── CX.PB02..HHZ.D.2014.121 └── util.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DeepPhasePick 2 | 3 | DeepPhasePick (DPP) is a method for automatically detecting and picking seismic phases from local earthquakes based on highly optimized deep neural networks. 4 | The method work in a pipeline, where in a first stage phase detection is performed by a Convolutional Neural Network (CNN) on three-component seismograms. 5 | Then P- and S-picking is conducted by two Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNN) on the vertical and the two-horizontal components, respectively. 6 | The CNN and LSTM networks have been trained using >30,000 seismic records extracted from manually-picked event waveforms originating from northern Chile. 7 | DPP additionally computes uncertainties of the predicted phase time onsets, based on the Monte Carlo Dropout (MCD) technique as an approximation of Bayesian inference. 8 | Predicted phase time onsets and associated uncertainties generated by DPP can be used to feed a phase associator algorithm as part of an automatic earthquake location procedure. 9 | 10 | ## 1. Install 11 | 12 | An easy and straightforward way to install DPP is first to directly clone the public repository: 13 | 14 | ~~~bash 15 | git clone https://github.com/hsotoparada/DeepPhasePick 16 | cd DeepPhasePick 17 | ~~~ 18 | 19 | Then, DPP requirements can be manually installed to a dedicated conda environment or by running: 20 | 21 | ~~~bash 22 | conda env create -f dpp.yml 23 | conda activate dpp 24 | ~~~ 25 | 26 | ## 2. DPP Worflow 27 | 28 | ### 1. Configuration 29 | 30 | Before running DPP, the method needs to be configured by creating an instance of the class `Config()`, for example using: 31 | 32 | import config, data, model, util 33 | dpp_config = config.Config() 34 | 35 | Then, parameters controlling different stages in the method can be configured as described below. 36 | 37 | **1.1 Parameters determining the selected waveform data on which DeepPhasePick is 38 | applied are defined using `dpp_config.set_data()`.** 39 | 40 | For example, to select the waveforms from stations `PB01` and `PB02` (network `CX`), and channel `HH` which are stored in 41 | the archive directory `archive`, and save the results in directory `out`, run: 42 | 43 | dpp_config.set_data(stas=['PB01', 'PB02'], net='CX', ch='HH', archive='archive', opath='out') 44 | 45 | **1.2 Parameters controlling how seismic waveforms are processed before the phase detection stage are defined using `dpp_config.set_data_params()`.** 46 | 47 | For example, the following will apply a highpass filter (> .5 Hz) and resample the data to 100 Hz (if it is not already sampled at that sampling rate): 48 | 49 | dpp_config.set_data_params(samp_freq=100., st_filter='highpass', filter_opts={'freq': .5}) 50 | 51 | Note that, since the models in DPP were trained using non-filtered data, this may cause numerous false positive predictions. 52 | 53 | **1.3 DPP will be applied on the selected seismic data (defined through `set_data()`) in the time windows defined using `dpp_config.set_time()`.** 54 | 55 | For example, to create 30-min (1800-seconds) time windows in the period between 56 | `2015-04-03T00:00:00` and `2015-04-03T02:00:00` (2 hours), use: 57 | 58 | dpp_config.set_time(dt_iter=1800., tstart="2015-04-03T00:00:00", tend="2015-04-03T02:00:00") 59 | 60 | Note that the windows created will have the same duration except for the last window, which will be filled with the remainder data until `tend` in case 61 | `dt_iter + tstart(last window) > tend`. 62 | 63 | **1.4 Parameters determining how predicted discrete probability time series are computed when running phase detection on seismic waveforms are defined using `dpp_config.set_trigger()`.** 64 | 65 | For example, the following will compute the discrete probability time series every 20 samples, using a probability threshold of 0.95 for P- and S-phases: 66 | 67 | dpp_config.set_trigger(n_shift=20, pthres_p=[0.95, 0.001], pthres_s=[0.95, 0.001]) 68 | 69 | **1.5 Parameters controlling the optional conditions applied for refining preliminary picks obtained from phase detection are defined using `dpp_config.set_picking()`.** 70 | 71 | For example, the following will remove preliminary picks which are presumed false positive, by applying all of the four optional conditions described in the 72 | Text S1 in the Supplementary Material of Soto and Schurr (2021). 73 | This is the default and recommended option, especially when dealing with very noise waveforms or filtered seismic waveforms, which may increase the number of presumed false positives. 74 | 75 | Then refined pick onsets and their time uncertainties will be computed by applying 20 iterations of Monte Carlo Dropout. 76 | 77 | dpp_config.set_picking(run_mcd=True, mcd_iter=20) 78 | 79 | More details on the arguments accepted by each of these configuration functions can be seen from the corresponding function documentation. 80 | 81 | Note that, instead of configuring DPP by using the functions describe above, each set of parameters can be passed as a dictionary to `config.Config()`. 82 | See the class `Config()` documentation to use this approach. 83 | 84 | ### 2. Seismic Data 85 | 86 | DPP method is applied on three-component MiniSEED seismic waveforms. 87 | 88 | To read the seismic waveforms into DPP an instance of the class Data() needs to be created, for example using: 89 | 90 | dpp_data = data.Data() 91 | 92 | Then, the data can be read into DPP for example from a local archive directory using: 93 | 94 | dpp_data.read_from_archive(dpp_config) 95 | 96 | The local archive needs to have the following commonly used structure: `archive/YY/NET/STA/CH` 97 | 98 | Here `YY` is year, `NET` is the network code, `STA` is the station code and `CH` is the channel code (e.g., HHZ.D) corresponding to the seismic streams. 99 | An example of archived data is included in `sample_data/archive`. 100 | 101 | Alternatively, waveforms can be read from a local directory with no specific structure. For example using: 102 | 103 | dpp_data.read_from_directory(dpp_config) 104 | 105 | 106 | ### 3. Phase Detection and Picking 107 | 108 | In order to run the phase detection and picking stages, an instance of the class `Model()` needs to be created, for example using: 109 | 110 | dpp_model = model.Model() 111 | 112 | When calling `Model()`, particular model versions can be specified by the string parameters `version_det`, `version_pick_P`, `version_pick_S`. 113 | 114 | Available model versions (more might be added in the future): 115 | 116 | * `version_det = "20201002"`: 117 |
best optimized phase detection model described in Soto and Schurr (2021). 118 | This is the default value for `version_det`. 119 | 120 | * `version_pick_P = version_pick_S = "20201002_1"`: 121 |
best optimized P- and S-phase picking models described in Soto and Schurr (2021). 122 | This is the default value for `version_pick_P` and `version_pick_S`. 123 | 124 | * `version_pick_P = version_pick_S = "20201002_2"`: 125 |
best optimized picking models, which were trained using 2x (for P phase) and 3x (for S phase) the number of shifted seismic records used in version `20201002_1`. 126 | Hence enhancing the performance of the phase picking. 127 | 128 | Once the models are read into DPP, model information can be retrieved for example by using: 129 | 130 | print(dpp_model.model_detection['best_model'].summary()) 131 | print(dpp_model.model_picking_P['best_model'].summary()) 132 | print(dpp_model.model_picking_S['best_model'].summary()) 133 | 134 | **3.1 To run the phase detection on the selected seismic waveforms use:** 135 | 136 | dpp_model.run_detection(dpp_config, dpp_data) 137 | 138 | This will compute discrete class probability time series from predictions, which are used to obtain preliminary phase picks. 139 | 140 | The optional parameter `save_dets = True` (default is `False`) will save a dictionary containing the class probabilities and preliminary picks to `opath/*/pick_stats` if needed for further use. 141 | Here `opath` is the output directory defined in the DPP configuration (see function `set_data()`). 142 | 143 | The optional parameter `save_data = True` (default is `False`) will save a dictionary containing the seismic waveform data used for phase detection to the same directory. 144 | 145 | **3.2 Next the phase picking can be run to refine the preliminary picks, using:** 146 | 147 | dpp_model.run_picking(dpp_config, dpp_data) 148 | 149 | The optional parameter `save_plots = True` (default is `True`) will save figures of individual predicted phase onsets to `opath/*/pick_plots` if `run_mcd=True`. 150 | These figures are similar to the subplots in Figure 3 of Soto and Schurr (2021). 151 | 152 | The optional parameter `save_picks = True` (default is `False`) will save a dictionary containing relevant information of preliminary and refined phase picks to `opath/*/pick_stats`. 153 | 154 | The optional parameter `save_stats = True` (default is `True`) will save statistics of predicted phase onsets to the output file `opath/*/pick_stats/pick_stats`. 155 |
If `run_mcd=False`, the ouput file will contain the following 4 columns: 156 | 157 | `station, phase (P or S), pick number, detection probability, tons (preliminary; UTC)` 158 | 159 | If `run_mcd=True`, the output file will contain the previous columns plus the following additional columns with the results from the MCD iterations: 160 | 161 | `tons (refined; UTC), tons (preliminary; within picking window) [s], tons (refined; within picking window) [s], 162 | tons_err (before onset) [s], tons_err (after onset) [s], pick class, pb, pb_std` 163 | 164 | Here `tons` is the predicted phase time onset with uncertainty `tons_err` and class `pick class`. 165 | These fields, as well as `pb` and `pb_std`, are described in Figure 3 of Soto and Schurr (2021). 166 | 167 | 168 | ### 4. Plotting predicted P and S phases 169 | 170 | Figures including continuous waveforms together with predicted P and S phases can be created using: 171 | 172 | util.plot_predicted_phases(dpp_config, dpp_data, dpp_model) 173 | 174 | Three additional optional parameters in this function allow to modify the figures layout (see function documentation). 175 | The parameter `plot_comps` defines which seismogram components are plotted. 176 | The parameter `plot_probs` defines which class probability time series are plotted. 177 | Finally, the parameter `shift_probs` controls if the plotted probability time series are shifted in time, 178 | according to the optimized hyperparameter values defining the picking window for each class (see Figura S1 in Soto and Schurr, 2021). 179 | 180 | For example, the following will plot the predicted picks on the vertical ('Z') and north ('N') seismogram components, 181 | and the probability time series for P- and S-phase classes shifted in time as described above. 182 | 183 | util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_comps=['Z','N'], plot_probs=['P','S'], shift_probs=True) 184 | 185 | 186 | ## Reference: 187 | 188 | - Soto, H., and Schurr, B. DeepPhasePick: A method for detecting and picking seismic phases from local earthquakes based on highly 189 | optimized convolutional and recurrent deep neural networks. Geophysical Journal International (2021). https://doi.org/10.1093/gji/ggab266 190 | 191 | 192 | ## Thanks: 193 | 194 | The development of DeepPhasePick method has received financial support from 195 | 196 | - The HAzard and Risk Team (HART) initiative of the GFZ German Research Centre for Geosciences in collaboration with the Institute of GeoSciences, Energy, Water 197 | and Environment of the Polytechnic University Tirana, Albania and the KIT Karlsruhe Institute of Technology. 198 | 199 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | """ 4 | This module contains a class and methods that help to configure the behavior of DeepPhasePick method. 5 | 6 | Author: Hugo Soto Parada (October, 2020) 7 | Contact: soto@gfz-potsdam.de, hugosotoparada@gmail.com 8 | 9 | """ 10 | 11 | import obspy.core as oc 12 | from datetime import datetime 13 | import re, sys, os, shutil, gc 14 | 15 | 16 | class Config(): 17 | """ 18 | Class that initiates user configuration for selecting seismic data and defining how this data is processed in DeepPhasePick. 19 | 20 | Parameters 21 | ---------- 22 | dct_data: dict, optional 23 | dictionary with parameters defining archived waveform data on which DeepPhasePick is applied. 24 | See parameters details in method set_data(). 25 | dct_data_params: dict, optional 26 | dictionary with parameters defining how seismic waveforms is processed before phase detection. 27 | See parameters details in method set_data_params(). 28 | dct_time: dict, optional 29 | dictionary with parameters defining time windows over which DeepPhasePick is applied. 30 | See parameters details in method set_time(). 31 | dct_trigger: dict, optional 32 | dictionary with parameters defining how predicted discrete probability time series are computed when running phase detection on seismic waveforms. 33 | See parameters details in method set_trigger(). 34 | dct_picking: dict, optional 35 | dictionary with parameters applied in optional conditions for improving preliminary picks obtained from phase detection. 36 | See parameters details in method set_picking(). 37 | """ 38 | 39 | def __init__(self, dct_data=None, dct_data_params=None, dct_time=None, dct_trigger=None, dct_picking=None): 40 | 41 | self.data = self._set_default_data(dct_data) 42 | self.data_params = self._set_default_data_params(dct_data_params) 43 | self.time = self._set_default_time(dct_time) 44 | self.trigger = self._set_default_trigger(dct_trigger) 45 | self.picking = self._set_default_picking(dct_picking) 46 | 47 | 48 | def _set_default_data(self, dct_data): 49 | """ 50 | Set default parameters defining archived waveform data on which DeepPhasePick is applied. 51 | 52 | Returns 53 | ------- 54 | dct: dict 55 | dictionary with defined parameters. See parameters details in method set_data(). 56 | """ 57 | 58 | dct = { 59 | 'stas': [], 60 | 'ch': 'HH', 61 | 'net': '', 62 | 'archive': 'archive', 63 | 'opath': 'out', 64 | } 65 | 66 | if dct_data is not None: 67 | for k in dct: 68 | if k in dct_data: 69 | dct[k] = dct_data[k] 70 | 71 | return dct 72 | 73 | 74 | def _set_default_data_params(self, dct_data_params): 75 | """ 76 | Set default parameters defining how seismic waveforms is processed before phase detection. 77 | 78 | Returns 79 | ------- 80 | dct: dict 81 | dictionary with defined parameters. See parameters details in method set_data_params(). 82 | """ 83 | 84 | dct = { 85 | 'samp_freq': 100., 86 | 'st_detrend': True, 87 | 'st_resample': True, 88 | 'st_filter': None, 89 | 'filter_opts': {}, 90 | } 91 | 92 | if dct_data_params is not None: 93 | for k in dct: 94 | if k in dct_data_params: 95 | dct[k] = dct_data_params[k] 96 | 97 | return dct 98 | 99 | 100 | def _set_default_time(self, dct_time): 101 | """ 102 | Set parameters defining time windows over which DeepPhasePick is applied. 103 | 104 | Returns 105 | ------- 106 | dct: dict 107 | dictionary with defined parameters. See parameters details in method set_time(). 108 | """ 109 | 110 | dct = { 111 | 'dt_iter': 3600., 112 | 'tstart': oc.UTCDateTime(0), 113 | 'tend': oc.UTCDateTime(3600), 114 | } 115 | 116 | if dct_time is not None: 117 | for k in dct: 118 | if k in dct_time: 119 | if k in ['tstart', 'tend']: 120 | dct[k] = oc.UTCDateTime(dct_time[k]) 121 | else: 122 | dct[k] = dct_time[k] 123 | 124 | return dct 125 | 126 | 127 | def _set_default_trigger(self, dct_trigger): 128 | """ 129 | Set default parameters defining how predicted discrete probability time series are computed when running phase detection on seismic waveforms. 130 | 131 | Returns 132 | ------- 133 | dct: dict 134 | dictionary with defined parameters. See parameters details in method set_trigger(). 135 | """ 136 | 137 | dct = { 138 | 'n_shift': 10, 'pthres_p': [0.9, .001], 'pthres_s': [0.9, .001], 'max_trig_len': [9e99, 9e99], 139 | } 140 | 141 | if dct_trigger is not None: 142 | for k in dct: 143 | if k in dct_trigger: 144 | dct[k] = dct_trigger[k] 145 | 146 | return dct 147 | 148 | 149 | def _set_default_picking(self, dct_picking): 150 | """ 151 | Set default parameters applied in optional conditions for improving preliminary picks obtained from phase detection. 152 | 153 | Returns 154 | ------- 155 | dct: dict 156 | dictionary with defined parameters. See parameters details in method set_trigger(). 157 | """ 158 | 159 | dct = { 160 | 'op_conds': ['1', '2', '3', '4'], 161 | 'tp_th_add': 1.5, 162 | 'dt_sp_near': 2., 163 | 'dt_ps_max': 35., 164 | 'dt_sdup_max': 2., 165 | # 166 | 'run_mcd': True, 167 | 'mcd_iter': 10, 168 | } 169 | 170 | if dct_picking is not None: 171 | for k in dct: 172 | if k in dct_picking: 173 | dct[k] = dct_picking[k] 174 | 175 | return dct 176 | 177 | 178 | def set_data(self, stas, ch, net, archive, opath='out'): 179 | """ 180 | Set parameters defining archived waveform data on which DeepPhasePick is applied. 181 | 182 | Parameters 183 | ---------- 184 | stas: list of str 185 | stations from which waveform data are used. 186 | ch: str 187 | channel code of selected waveforms. 188 | net: str 189 | network code of selected stations. 190 | archive: str 191 | path to the structured or unstructured archive where waveforms are read from. 192 | opath: str, optional 193 | output path where results are stored. 194 | """ 195 | self.data = { 196 | 'stas': stas, 197 | 'ch': ch, 198 | 'net': net, 199 | 'archive': archive, 200 | 'opath': opath, 201 | } 202 | 203 | 204 | def set_data_params(self, samp_freq=100., st_detrend=True, st_resample=True, st_filter=None, filter_opts={}): 205 | """ 206 | Set parameters defining how seismic waveforms is processed before phase detection. 207 | 208 | Parameters 209 | ---------- 210 | samp_freq: float, optional 211 | sampling rate [Hz] at which the seismic waveforms will be resampled. 212 | st_detrend: bool, optional 213 | If True, detrend (linear) waveforms on which phase detection is performed. 214 | st_resample: bool, optional 215 | If True, resample waveforms on which phase detection is performed at samp_freq. 216 | st_filter: str, optional 217 | type of filter applied to waveforms on which phase detection is performed. If None, no filter is applied. 218 | See obspy.core.stream.Stream.filter. 219 | filter_opts: dict, optional 220 | Necessary keyword arguments for the respective filter that will be passed on. (e.g. freqmin=1.0, freqmax=20.0 for filter_type="bandpass") 221 | See obspy.core.stream.Stream.filter. 222 | 223 | """ 224 | 225 | self.data_params = { 226 | 'samp_freq': samp_freq, 227 | 'st_detrend': st_detrend, 228 | 'st_resample': st_resample, 229 | 'st_filter': st_filter, 230 | 'filter_opts': filter_opts, 231 | } 232 | 233 | 234 | def set_time(self, dt_iter, tstart, tend): 235 | """ 236 | Set parameters defining time windows over which DeepPhasePick are applied. 237 | 238 | Parameters 239 | ---------- 240 | dt_iter: float 241 | time step (in seconds) between consecutive time windows. 242 | tstarts: str 243 | start time to define time windows, in format "YYYY-MM-DDTHH:MM:SS". 244 | tends: str 245 | end time to define time windows, in format "YYYY-MM-DDTHH:MM:SS". 246 | 247 | """ 248 | self.time = { 249 | 'dt_iter': dt_iter, 250 | 'tstart': oc.UTCDateTime(tstart), 251 | 'tend': oc.UTCDateTime(tend), 252 | } 253 | 254 | 255 | def set_trigger(self, n_shift=10, pthres_p=[0.9,.001], pthres_s=[0.9,.001], max_trig_len=[9e99, 9e99]): 256 | """ 257 | Set parameters defining how predicted discrete probability time series are computed when running phase detection on seismic waveforms 258 | 259 | Parameters 260 | ---------- 261 | n_shift: int, optional 262 | step size (in samples) defining discrete probability time series. 263 | pthres_p: list of float, optional 264 | probability thresholds defining P-phase trigger on (pthres_p[0]) and off (pthres_p[1]) times. 265 | See thres1 and thres2 parameters in obspy trigger_onset function. 266 | pthres_s: list of float, optional 267 | probability thresholds defining S-phase trigger on (pthres_s[0]) and off (pthres_s[1]) times. 268 | See thres1 and thres2 parameters in function obspy.signal.trigger.trigger_onset. 269 | max_trig_len: list of int, optional 270 | maximum lengths (in samples) of triggered P (max_trig_len[0]) and S (max_trig_len[1]) phase. 271 | See max_len parameter in function obspy.signal.trigger.trigger_onset. 272 | """ 273 | 274 | self.trigger = { 275 | 'n_shift': n_shift, 276 | 'pthres_p': pthres_p, 277 | 'pthres_s': pthres_s, 278 | 'max_trig_len': max_trig_len, 279 | } 280 | 281 | 282 | def set_picking(self, op_conds=['1','2','3','4'], tp_th_add=1.5, dt_sp_near=2., dt_ps_max=35., dt_sdup_max=2., run_mcd=True, mcd_iter=10): 283 | """ 284 | Set parameters applied in optional conditions for refining preliminary picks obtained from phase detection. 285 | 286 | Parameters 287 | ---------- 288 | op_conds: list of str, optional 289 | optional conditions that are applied on preliminary picks, in order to keep keep/remove presumed true/false preliminary onsets. 290 | These conditions are explained in Supplementary Information of the original publication (https://doi.org/10.31223/X5BC8B). 291 | For example ['1', '2'] indicates that only conditions (1) and (2) are applied. 292 | '1': resolves between P and S phases predicted close in time, with overlapping probability time series 293 | '2': resolves between P and S phases predicted close in time, with no overlapping probability distributions. 294 | '3': discards S picks for which there is no earlier P or P-S predicted picks. 295 | '4': resolves between possible duplicated S phases. 296 | tp_th_add: float, optional 297 | time (in seconds) added to define search time intervals in condition (1). 298 | dt_sp_near: float, optional 299 | time threshold (in seconds) used in condition (2). 300 | dt_ps_max: float, optional 301 | time (in seconds) used to define search time intervals in condition (3). 302 | dt_sdup_max: float, optional 303 | time threshold (in seconds) used in condition (4). 304 | run_mcd: bool, optional 305 | If True, run phase picking in order to refine preliminary picks from phase detection. 306 | mcd_iter: int, optional 307 | number of Monte Carlo Dropout iterations used in phase picking. 308 | """ 309 | 310 | self.picking = { 311 | 'op_conds': op_conds, 312 | 'tp_th_add': tp_th_add, 313 | 'dt_sp_near': dt_sp_near, 314 | 'dt_ps_max': dt_ps_max, 315 | 'dt_sdup_max': dt_sdup_max, 316 | 'run_mcd': run_mcd, 317 | 'mcd_iter': mcd_iter, 318 | } 319 | -------------------------------------------------------------------------------- /data.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | """ 4 | This module contains a class and methods related to the seismic data used by DeepPhasePick method. 5 | 6 | Author: Hugo Soto Parada (October, 2020) 7 | Contact: soto@gfz-potsdam.de, hugosotoparada@gmail.com 8 | 9 | """ 10 | 11 | import numpy as np 12 | import obspy.core as oc 13 | from obspy.signal.trigger import trigger_onset 14 | from obspy.io.mseed.core import InternalMSEEDError 15 | from glob import glob 16 | import re, sys, os, shutil, gc 17 | 18 | 19 | class Data(): 20 | """ 21 | Class defining seismic data-related methods. 22 | """ 23 | 24 | def read_from_archive(self, config): 25 | """ 26 | Reads seismic data on which DeepPhasePick is applied. 27 | Data must be stored in a archive directory structured as: archive/YY/NET/STA/CH 28 | Here YY is year, NET is the network code, STA is the station code and CH is the channel code (e.g., HH) of the seismic streams. 29 | 30 | Parameters 31 | ---------- 32 | config: instance of config.Config 33 | Contains user configuration defining which seismic waveform data is selected and how this data is processed in DeepPhasePick. 34 | """ 35 | # 36 | # set time windows for iteration over continuous waveforms 37 | # 38 | tstart, tend, dt_iter = [config.time['tstart'], config.time['tend'], config.time['dt_iter']] 39 | t_iters = [] 40 | tstart_iter = tstart 41 | tend_iter = tstart_iter + dt_iter 42 | if tend_iter > tend: 43 | tend_iter = tend 44 | # 45 | print("#") 46 | while tstart_iter < tend: 47 | t_iters.append([tstart_iter, tend_iter]) 48 | tstart_iter += dt_iter 49 | tend_iter += dt_iter 50 | if tend_iter > tend: 51 | tend_iter = tend 52 | # 53 | print(f"time windows ({len(t_iters)}) for iteration over continuous waveforms:") 54 | for t_iter in t_iters: 55 | print(t_iter) 56 | print("") 57 | # 58 | # iterate over time windows 59 | # 60 | self.data = {} 61 | stas = config.data['stas'] 62 | doy_tmp = '999' 63 | for i, t_iter in enumerate(t_iters): 64 | # 65 | tstart_iter, tend_iter = t_iter 66 | # 67 | twin_str = f"{tstart_iter.year}{tstart_iter.month:02}{tstart_iter.day:02}" 68 | twin_str += f"T{tstart_iter.hour:02}{tstart_iter.minute:02}{tstart_iter.second:02}" 69 | twin_str += f"_{tend_iter.year}{tend_iter.month:02}{tend_iter.day:02}" 70 | twin_str += f"T{(tend_iter).hour:02}{(tend_iter).minute:02}{(tend_iter).second:02}" 71 | opath = f"{config.data['opath']}/{twin_str}" 72 | yy = tstart_iter.year 73 | doy = '%03d' % (tstart_iter.julday) 74 | # 75 | if doy != doy_tmp: 76 | sts = [oc.Stream(), oc.Stream(), oc.Stream(), oc.Stream(), oc.Stream()] 77 | st = oc.Stream() 78 | # 79 | print("") 80 | print("retrieving seismic waveforms for stations:") 81 | print(stas) 82 | # 83 | st_arg = 0 84 | stas_remove = [] 85 | for j, sta in enumerate(stas): 86 | # 87 | # read seismic waveforms from archive 88 | # 89 | net = config.data['net'] 90 | ch = config.data['ch'] 91 | path = config.data['archive'] 92 | flist = glob(path+'/'+str(yy)+'/'+net+'/'+sta+'/'+ch+'?.D/*.'+doy) 93 | # 94 | if len(flist) > 0: 95 | outstr = f"seismic data found for: net = {net}, sta = {sta}, st_count = {len(sts[st_arg])}, st_arg = {st_arg}" 96 | print(outstr) 97 | outstr = str(flist) 98 | print(outstr) 99 | else: 100 | outstr = f"seismic data not found for: net = {net}, sta = {sta}" 101 | print(outstr) 102 | # 103 | if len(sts[st_arg]) >= 50: 104 | st_arg += 1 105 | # 106 | for f in flist: 107 | try: 108 | print(f) 109 | tr = oc.read(f) 110 | sts[st_arg] += tr 111 | # 112 | except InternalMSEEDError: 113 | stas_remove.append(sta) 114 | outstr = f"skipping {f} --> InternalMSEEDError exception" 115 | print(outstr) 116 | continue 117 | # 118 | for stt in sts: 119 | st += stt 120 | del sts 121 | # 122 | stas_remove = set(stas_remove) 123 | for s in stas_remove: 124 | for tr in st.select(station=s): 125 | st.remove(tr) 126 | print(st.__str__(extended=True)) 127 | # 128 | # process (detrend, filter, resample, ...) raw stream data 129 | # 130 | print("#") 131 | print("processing raw stream data...") 132 | # 133 | if config.data_params['st_detrend']: 134 | # 135 | print('detrend...') 136 | try: 137 | st.detrend(type='linear') 138 | except NotImplementedError: 139 | # 140 | # Catch exception NotImplementedError: Trace with masked values found. This is not supported for this operation. 141 | # Try the split() method on Stream to produce a Stream with unmasked Traces. 142 | # 143 | st = st.split() 144 | st.detrend(type='linear') 145 | except ValueError: 146 | # 147 | # Catch exception ValueError: array must not contain infs or NaNs. 148 | # Due to presence of e.g. nans in at least one trace data. 149 | # 150 | stas_remove = [] 151 | for tr in st: 152 | nnan = np.count_nonzero(np.isnan(tr.data)) 153 | ninf = np.count_nonzero(np.isinf(tr.data)) 154 | if nnan > 0: 155 | print(f"{tr} --> removed (due to presence of nans)") 156 | stas_remove.append(tr.stats.station) 157 | continue 158 | if ninf > 0: 159 | print(f"{tr} --> removed (due to presence of infs)") 160 | stas_remove.append(tr.stats.station) 161 | continue 162 | # 163 | stas_remove = set(stas_remove) 164 | for s in stas_remove: 165 | for tr in st.select(station=s): 166 | st.remove(tr) 167 | st.detrend(type='linear') 168 | # 169 | if config.data_params['st_filter'] is not None: 170 | # 171 | print('filter...') 172 | st.filter(type=config.data_params['st_filter'], **config.data_params['filter_opts']) 173 | # 174 | if config.data_params['st_resample']: 175 | # 176 | print('resampling...') 177 | for tr in st: 178 | # 179 | if tr.stats.sampling_rate == config.data_params['samp_freq']: 180 | outstr = f"{tr} --> skipped, already sampled at {tr.stats.sampling_rate} Hz" 181 | print(outstr) 182 | pass 183 | # 184 | if tr.stats.sampling_rate < config.data_params['samp_freq']: 185 | outstr = f"{tr} --> resampling from {tr.stats.sampling_rate} to {config.data_params['samp_freq']} Hz" 186 | print(outstr) 187 | tr.resample(config.data_params['samp_freq']) 188 | # 189 | if tr.stats.sampling_rate > config.data_params['samp_freq']: 190 | # 191 | if int(tr.stats.sampling_rate % config.data_params['samp_freq']) == 0: 192 | decim_factor = int(tr.stats.sampling_rate / config.data_params['samp_freq']) 193 | outstr = f"{tr} --> decimating from {tr.stats.sampling_rate} to {config.data_params['samp_freq']} Hz" 194 | print(outstr) 195 | tr.decimate(decim_factor) 196 | else: 197 | outstr = f"{tr} --> resampling from {tr.stats.sampling_rate} to {config.data_params['samp_freq']} Hz !!" 198 | print(outstr) 199 | tr.resample(config.data_params['samp_freq']) 200 | # 201 | print('merging...') 202 | try: 203 | st.merge() 204 | except: 205 | # 206 | outstr = f"Catch exception: can't merge traces with same ids but differing data types!" 207 | print(outstr) 208 | for tr in st: 209 | tr.data = tr.data.astype(np.int32) 210 | st.merge() 211 | # 212 | print('slicing...') 213 | stt = st.slice(tstart_iter, tend_iter) 214 | # 215 | # sort traces of same station by channel, so for each station traces will be shown in order (HHE,N,Z) 216 | stt.sort(['channel']) 217 | # 218 | self.data[i+1] = { 219 | 'st': {}, 220 | 'twin': [tstart_iter, tend_iter], 221 | 'opath': opath, 222 | } 223 | for t, tr in enumerate(stt): 224 | sta = tr.stats.station 225 | # 226 | st_check = stt.select(station=sta) 227 | if len(st_check) < 3: 228 | print(f"skipping trace of stream with less than 3 components: {tr}") 229 | continue 230 | # 231 | if sta not in self.data[i+1]['st'].keys(): 232 | self.data[i+1]['st'][sta] = oc.Stream() 233 | self.data[i+1]['st'][sta] += tr 234 | else: 235 | self.data[i+1]['st'][sta] += tr 236 | # 237 | doy_tmp = doy 238 | 239 | 240 | def read_from_directory(self, config): 241 | """ 242 | Reads seismic data on which DeepPhasePick is applied. 243 | All waveforms must be stored in an unstructured archive directory, e.g.: archive/ 244 | 245 | Parameters 246 | ---------- 247 | config: instance of config.Config 248 | Contains user configuration defining which seismic waveform data is selected and how this data is processed in DeepPhasePick. 249 | """ 250 | # 251 | # read seismic waveforms from directory 252 | # 253 | path = config.data['archive'] 254 | tstart, tend, dt_iter = [config.time['tstart'], config.time['tend'], config.time['dt_iter']] 255 | flist_all = sorted(glob(path+'/*'))[:] 256 | # 257 | flabels = [] 258 | sts = [oc.Stream(), oc.Stream(), oc.Stream(), oc.Stream(), oc.Stream()] 259 | st = oc.Stream() 260 | st_arg = 0 261 | for i, f in enumerate(flist_all[:]): 262 | # 263 | if len(sts[st_arg]) >= 50: 264 | st_arg += 1 265 | # 266 | print(f"reading seismic waveform: {f}") 267 | tr = oc.read(f) 268 | tr_net, tr_sta, tr_ch = tr[0].stats.network, tr[0].stats.station, tr[0].stats.channel[:-1] 269 | if (tr_net == config.data['net']) and (tr_sta in config.data['stas']) and (tr_ch == config.data['ch']): 270 | try: 271 | sts[st_arg] += tr 272 | except InternalMSEEDError: 273 | # stas_remove.append(sta) 274 | outstr = f"skipping {f} --> InternalMSEEDError exception" 275 | print(outstr) 276 | continue 277 | # 278 | for stt in sts: 279 | st += stt 280 | del sts 281 | # 282 | print(st.__str__(extended=True)) 283 | # 284 | # process (detrend, filter, resample, ...) raw stream data 285 | # 286 | print("#") 287 | print("processing raw stream data...") 288 | # 289 | if config.data_params['st_detrend']: 290 | # 291 | print('detrend...') 292 | try: 293 | st.detrend(type='linear') 294 | except NotImplementedError: 295 | # 296 | # Catch exception NotImplementedError: Trace with masked values found. This is not supported for this operation. 297 | # Try split() method on Stream to produce a Stream with unmasked Traces. 298 | # 299 | st = st.split() 300 | st.detrend(type='linear') 301 | except ValueError: 302 | # 303 | # Catch exception ValueError: array must not contain infs or NaNs. 304 | # Due to presence e.g. of NaNs in at least one trace data. 305 | # 306 | stas_remove = [] 307 | for tr in st: 308 | nnan = np.count_nonzero(np.isnan(tr.data)) 309 | ninf = np.count_nonzero(np.isinf(tr.data)) 310 | if nnan > 0: 311 | print(f"{tr} --> removed (due to presence of nans)") 312 | stas_remove.append(tr.stats.station) 313 | continue 314 | if ninf > 0: 315 | print(f"{tr} --> removed (due to presence of infs)") 316 | stas_remove.append(tr.stats.station) 317 | continue 318 | # 319 | stas_remove = set(stas_remove) 320 | for s in stas_remove: 321 | for tr in st.select(station=s): 322 | st.remove(tr) 323 | st.detrend(type='linear') 324 | # 325 | if config.data_params['st_filter'] is not None: 326 | # 327 | print('filter...') 328 | st.filter(type=config.data_params['st_filter'], **config.data_params['filter_opts']) 329 | # 330 | if config.data_params['st_resample']: 331 | # 332 | print('resampling...') 333 | for tr in st: 334 | # 335 | if tr.stats.sampling_rate == config.data_params['samp_freq']: 336 | outstr = f"{tr} --> skipped, already sampled at {tr.stats.sampling_rate} Hz" 337 | print(outstr) 338 | pass 339 | # 340 | if tr.stats.sampling_rate < config.data_params['samp_freq']: 341 | outstr = f"{tr} --> resampling from {tr.stats.sampling_rate} to {config.data_params['samp_freq']} Hz" 342 | print(outstr) 343 | tr.resample(config.data_params['samp_freq']) 344 | # 345 | if tr.stats.sampling_rate > config.data_params['samp_freq']: 346 | # 347 | if int(tr.stats.sampling_rate % config.data_params['samp_freq']) == 0: 348 | decim_factor = int(tr.stats.sampling_rate / config.data_params['samp_freq']) 349 | outstr = f"{tr} --> decimating from {tr.stats.sampling_rate} to {config.data_params['samp_freq']} Hz" 350 | print(outstr) 351 | tr.decimate(decim_factor) 352 | else: 353 | outstr = f"{tr} --> resampling from {tr.stats.sampling_rate} to {config.data_params['samp_freq']} Hz !!" 354 | print(outstr) 355 | tr.resample(config.data_params['samp_freq']) 356 | # 357 | print('merging...') 358 | try: 359 | st.merge() 360 | except: 361 | # 362 | outstr = f"Catch exception: can't merge traces with same ids but differing data types!" 363 | print(outstr) 364 | for tr in st: 365 | tr.data = tr.data.astype(np.int32) 366 | st.merge() 367 | # 368 | # set time windows for iteration over continuous waveforms 369 | # 370 | t_iters = [] 371 | tstart_iter = tstart 372 | tend_iter = tstart_iter + dt_iter 373 | if tend_iter > tend: 374 | tend_iter = tend 375 | # 376 | print("#") 377 | while tstart_iter < tend: 378 | t_iters.append([tstart_iter, tend_iter]) 379 | tstart_iter += dt_iter 380 | tend_iter += dt_iter 381 | if tend_iter > tend: 382 | tend_iter = tend 383 | # 384 | print(f"time windows ({len(t_iters)}) for iteration over continuous waveforms:") 385 | for t_iter in t_iters: 386 | print(t_iter) 387 | print("") 388 | # 389 | # iterate over time windows 390 | # 391 | self.data = {} 392 | for i, t_iter in enumerate(t_iters): 393 | # 394 | tstart_iter, tend_iter = t_iter 395 | # 396 | twin_str = f"{tstart_iter.year}{tstart_iter.month:02}{tstart_iter.day:02}" 397 | twin_str += f"T{tstart_iter.hour:02}{tstart_iter.minute:02}{tstart_iter.second:02}" 398 | twin_str += f"_{tend_iter.year}{tend_iter.month:02}{tend_iter.day:02}" 399 | twin_str += f"T{(tend_iter).hour:02}{(tend_iter).minute:02}{(tend_iter).second:02}" 400 | # 401 | opath = f"{config.data['opath']}/{twin_str}" 402 | yy = tstart_iter.year 403 | doy = '%03d' % (tstart_iter.julday) 404 | # 405 | print('slicing...') 406 | stt = st.slice(tstart_iter, tend_iter) 407 | # 408 | # sort traces of same station by channel, so for each station traces will be shown in order (HHE,N,Z) 409 | stt.sort(['channel']) 410 | # 411 | self.data[i+1] = { 412 | 'st': {}, 413 | 'twin': [tstart_iter, tend_iter], 414 | 'opath': opath, 415 | } 416 | for t, tr in enumerate(stt): 417 | sta = tr.stats.station 418 | # 419 | st_check = stt.select(station=sta) 420 | if len(st_check) < 3: 421 | print(f"skipping trace of stream with less than 3 components: {tr}") 422 | continue 423 | # 424 | if sta not in self.data[i+1]['st'].keys(): 425 | self.data[i+1]['st'][sta] = oc.Stream() 426 | self.data[i+1]['st'][sta] += tr 427 | else: 428 | self.data[i+1]['st'][sta] += tr 429 | -------------------------------------------------------------------------------- /dpp.yml: -------------------------------------------------------------------------------- 1 | name: dpp 2 | channels: 3 | - conda-forge 4 | - defaults 5 | dependencies: 6 | - python=3.6 7 | - obspy==1.2.2 8 | - numpy 9 | - matplotlib 10 | - tqdm 11 | - pip 12 | - pip: 13 | - tensorflow==2.2.0 14 | -------------------------------------------------------------------------------- /examples/run_dpp_download_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### This example applies DeepPhasePick on seismic data downloaded using FDSN web service client for ObsPy." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import os\n", 17 | "os.chdir('../')\n", 18 | "import config, data, model, util \n", 19 | "from obspy.clients.fdsn import Client \n", 20 | "import obspy.core as oc " 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "## 1. Configure DPP" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "name": "stdout", 37 | "output_type": "stream", 38 | "text": [ 39 | "__pycache__/ removed\n", 40 | "~/.nv/ not found, continuing...\n" 41 | ] 42 | } 43 | ], 44 | "source": [ 45 | "# config\n", 46 | "util.init_session()\n", 47 | "dpp_config = config.Config()\n", 48 | "dpp_config.set_trigger(pthres_p=[0.9, 0.001], pthres_s=[0.9, 0.001])\n", 49 | "dpp_config.set_picking(mcd_iter=10, run_mcd=True)\n", 50 | "# dpp_config.set_picking(run_mcd=False)\n", 51 | "#\n", 52 | "dpp_config.set_data(\n", 53 | " stas=['PB01', 'PB02'],\n", 54 | " net='CX',\n", 55 | " ch='HH',\n", 56 | " archive='sample_data/CX_20140401',\n", 57 | " opath='out_CX_20140401'\n", 58 | ")\n", 59 | "dpp_config.set_time(\n", 60 | " dt_iter=3600.,\n", 61 | " tstart = \"2014-04-01T02:00:00\",\n", 62 | " tend = \"2014-04-01T03:00:00\", \n", 63 | ")" 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "## 2. Download seismic data and read it into DPP" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 3, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "name": "stdout", 80 | "output_type": "stream", 81 | "text": [ 82 | "writing stream sample_data/CX_20140401/CX.PB01..HH.mseed...\n", 83 | "writing stream sample_data/CX_20140401/CX.PB02..HH.mseed...\n" 84 | ] 85 | } 86 | ], 87 | "source": [ 88 | "# download and archive seismic waveforms\n", 89 | "client = Client(\"GFZ\")\n", 90 | "os.makedirs(f\"{dpp_config.data['archive']}\", exist_ok=True)\n", 91 | "tstart = oc.UTCDateTime(dpp_config.time['tstart'])\n", 92 | "tend = oc.UTCDateTime(dpp_config.time['tend'])\n", 93 | "#\n", 94 | "for sta in dpp_config.data['stas']:\n", 95 | " st = client.get_waveforms(network=dpp_config.data['net'], station=sta, location=\"*\", channel=f\"{dpp_config.data['ch']}?\", starttime=tstart, endtime=tend)\n", 96 | " # print(st)\n", 97 | " st_name = f\"{dpp_config.data['archive']}/{st[0].stats.network}.{st[0].stats.station}..{st[0].stats.channel[:-1]}.mseed\"\n", 98 | " print(f\"writing stream {st_name}...\")\n", 99 | " st.write(st_name, format=\"MSEED\")" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 4, 105 | "metadata": { 106 | "scrolled": true 107 | }, 108 | "outputs": [ 109 | { 110 | "name": "stdout", 111 | "output_type": "stream", 112 | "text": [ 113 | "reading seismic waveform: sample_data/CX_20140401/CX.PB01..HH.mseed\n", 114 | "reading seismic waveform: sample_data/CX_20140401/CX.PB02..HH.mseed\n", 115 | "6 Trace(s) in Stream:\n", 116 | "CX.PB01..HHZ | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 117 | "CX.PB01..HHE | 2014-04-01T01:59:59.998394Z - 2014-04-01T02:59:59.998394Z | 100.0 Hz, 360001 samples\n", 118 | "CX.PB01..HHN | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 119 | "CX.PB02..HHZ | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 120 | "CX.PB02..HHE | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 121 | "CX.PB02..HHN | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 122 | "#\n", 123 | "processing raw stream data...\n", 124 | "detrend...\n", 125 | "resampling...\n", 126 | "CX.PB01..HHZ | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples --> skipped, already sampled at 100.0 Hz\n", 127 | "CX.PB01..HHE | 2014-04-01T01:59:59.998394Z - 2014-04-01T02:59:59.998394Z | 100.0 Hz, 360001 samples --> skipped, already sampled at 100.0 Hz\n", 128 | "CX.PB01..HHN | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples --> skipped, already sampled at 100.0 Hz\n", 129 | "CX.PB02..HHZ | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples --> skipped, already sampled at 100.0 Hz\n", 130 | "CX.PB02..HHE | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples --> skipped, already sampled at 100.0 Hz\n", 131 | "CX.PB02..HHN | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples --> skipped, already sampled at 100.0 Hz\n", 132 | "merging...\n", 133 | "#\n", 134 | "time windows (1) for iteration over continuous waveforms:\n", 135 | "[UTCDateTime(2014, 4, 1, 2, 0), UTCDateTime(2014, 4, 1, 3, 0)]\n", 136 | "\n", 137 | "slicing...\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "# data\n", 143 | "dpp_data = data.Data()\n", 144 | "dpp_data.read_from_directory(dpp_config)\n", 145 | "#\n", 146 | "# for k in dpp_data.data:\n", 147 | "# print(k, dpp_data.data[k])" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "## 3. Run phase detection and picking" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 5, 160 | "metadata": { 161 | "scrolled": true 162 | }, 163 | "outputs": [], 164 | "source": [ 165 | "# model\n", 166 | "# dpp_model = model.Model(verbose=False)\n", 167 | "dpp_model = model.Model(verbose=False, version_pick_P=\"20201002_2\", version_pick_S=\"20201002_2\")\n", 168 | "#\n", 169 | "# print(dpp_model.model_detection['best_model'].summary())\n", 170 | "# print(dpp_model.model_picking_P['best_model'].summary())\n", 171 | "# print(dpp_model.model_picking_S['best_model'].summary())" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 6, 177 | "metadata": { 178 | "scrolled": false 179 | }, 180 | "outputs": [ 181 | { 182 | "name": "stdout", 183 | "output_type": "stream", 184 | "text": [ 185 | "#\n", 186 | "Calculating predictions for stream: CX.PB01..HH?...\n", 187 | "strimming stream: 1, 1\n", 188 | "720/720 [==============================] - 28s 39ms/step\n", 189 | "3 Trace(s) in Stream:\n", 190 | "CX.PB01..HHE | 2014-04-01T01:59:59.998394Z - 2014-04-01T02:59:59.998394Z | 100.0 Hz, 360001 samples\n", 191 | "CX.PB01..HHN | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 192 | "CX.PB01..HHZ | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 193 | "p_picks = 13, s_picks = 8\n", 194 | "#\n", 195 | "Calculating predictions for stream: CX.PB02..HH?...\n", 196 | "720/720 [==============================] - 34s 48ms/step\n", 197 | "3 Trace(s) in Stream:\n", 198 | "CX.PB02..HHE | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 199 | "CX.PB02..HHN | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 200 | "CX.PB02..HHZ | 2014-04-01T01:59:59.998393Z - 2014-04-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 201 | "p_picks = 10, s_picks = 4\n" 202 | ] 203 | } 204 | ], 205 | "source": [ 206 | "# run phase detection\n", 207 | "dpp_model.run_detection(dpp_config, dpp_data, save_dets=False, save_data=False)" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 7, 213 | "metadata": { 214 | "scrolled": true 215 | }, 216 | "outputs": [ 217 | { 218 | "name": "stdout", 219 | "output_type": "stream", 220 | "text": [ 221 | "#\n", 222 | "1, 2014-04-01T02:00:00.000000Z, 2014-04-01T03:00:00.000000Z, PB01\n", 223 | "triggered picks (P, S): 13, 8\n", 224 | "selected picks (P, S): 10, 6\n", 225 | "#\n", 226 | "P pick: 1/10\n" 227 | ] 228 | }, 229 | { 230 | "name": "stderr", 231 | "output_type": "stream", 232 | "text": [ 233 | "100%|██████████| 10/10 [00:03<00:00, 2.97it/s]\n" 234 | ] 235 | }, 236 | { 237 | "name": "stdout", 238 | "output_type": "stream", 239 | "text": [ 240 | "3.36 3.47 3.41 3.77 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 241 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_01.png\n", 242 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_01.png\n", 243 | "tpred = 3.470\n", 244 | "terr(1 x pb_std) = (-0.060, +0.300)\n", 245 | "pick_class = 2\n", 246 | "pb, pb_std = (0.508, 0.233)\n", 247 | "#\n", 248 | "P pick: 2/10\n" 249 | ] 250 | }, 251 | { 252 | "name": "stderr", 253 | "output_type": "stream", 254 | "text": [ 255 | "100%|██████████| 10/10 [00:01<00:00, 5.11it/s]\n" 256 | ] 257 | }, 258 | { 259 | "name": "stdout", 260 | "output_type": "stream", 261 | "text": [ 262 | "3.36 3.62 3.47 3.71 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 263 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_02.png\n", 264 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_02.png\n", 265 | "tpred = 3.620\n", 266 | "terr(1 x pb_std) = (-0.150, +0.090)\n", 267 | "pick_class = 2\n", 268 | "pb, pb_std = (0.502, 0.199)\n", 269 | "#\n", 270 | "P pick: 3/10\n" 271 | ] 272 | }, 273 | { 274 | "name": "stderr", 275 | "output_type": "stream", 276 | "text": [ 277 | "100%|██████████| 10/10 [00:01<00:00, 5.44it/s]\n" 278 | ] 279 | }, 280 | { 281 | "name": "stdout", 282 | "output_type": "stream", 283 | "text": [ 284 | "3.36 3.31 3.3 3.32 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 285 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_03.png\n", 286 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_03.png\n", 287 | "tpred = 3.310\n", 288 | "terr(1 x pb_std) = (-0.010, +0.010)\n", 289 | "pick_class = 0\n", 290 | "pb, pb_std = (0.546, 0.182)\n", 291 | "#\n", 292 | "P pick: 4/10\n" 293 | ] 294 | }, 295 | { 296 | "name": "stderr", 297 | "output_type": "stream", 298 | "text": [ 299 | "100%|██████████| 10/10 [00:01<00:00, 6.46it/s]\n" 300 | ] 301 | }, 302 | { 303 | "name": "stdout", 304 | "output_type": "stream", 305 | "text": [ 306 | "3.36 3.33 3.31 3.35 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 307 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_04.png\n", 308 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_04.png\n", 309 | "tpred = 3.330\n", 310 | "terr(1 x pb_std) = (-0.020, +0.020)\n", 311 | "pick_class = 0\n", 312 | "pb, pb_std = (0.512, 0.132)\n", 313 | "#\n", 314 | "P pick: 5/10\n" 315 | ] 316 | }, 317 | { 318 | "name": "stderr", 319 | "output_type": "stream", 320 | "text": [ 321 | "100%|██████████| 10/10 [00:02<00:00, 4.94it/s]\n" 322 | ] 323 | }, 324 | { 325 | "name": "stdout", 326 | "output_type": "stream", 327 | "text": [ 328 | "3.36 3.25 3.09 3.52 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 329 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_05.png\n", 330 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_05.png\n", 331 | "tpred = 3.250\n", 332 | "terr(1 x pb_std) = (-0.160, +0.270)\n", 333 | "pick_class = 3\n", 334 | "pb, pb_std = (0.507, 0.318)\n", 335 | "#\n", 336 | "P pick: 6/10\n" 337 | ] 338 | }, 339 | { 340 | "name": "stderr", 341 | "output_type": "stream", 342 | "text": [ 343 | "100%|██████████| 10/10 [00:02<00:00, 4.56it/s]\n" 344 | ] 345 | }, 346 | { 347 | "name": "stdout", 348 | "output_type": "stream", 349 | "text": [ 350 | "3.36 3.36 3.35 3.37 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 351 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_06.png\n", 352 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_06.png\n", 353 | "tpred = 3.360\n", 354 | "terr(1 x pb_std) = (-0.010, +0.010)\n", 355 | "pick_class = 0\n", 356 | "pb, pb_std = (0.537, 0.097)\n", 357 | "#\n", 358 | "P pick: 7/10\n" 359 | ] 360 | }, 361 | { 362 | "name": "stderr", 363 | "output_type": "stream", 364 | "text": [ 365 | "100%|██████████| 10/10 [00:02<00:00, 4.14it/s]\n" 366 | ] 367 | }, 368 | { 369 | "name": "stdout", 370 | "output_type": "stream", 371 | "text": [ 372 | "3.36 3.5 3.49 3.5 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 373 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_07.png\n", 374 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_07.png\n", 375 | "tpred = 3.500\n", 376 | "terr(1 x pb_std) = (-0.010, +0.000)\n", 377 | "pick_class = 0\n", 378 | "pb, pb_std = (0.560, 0.124)\n", 379 | "#\n", 380 | "P pick: 8/10\n" 381 | ] 382 | }, 383 | { 384 | "name": "stderr", 385 | "output_type": "stream", 386 | "text": [ 387 | "100%|██████████| 10/10 [00:01<00:00, 5.13it/s]\n" 388 | ] 389 | }, 390 | { 391 | "name": "stdout", 392 | "output_type": "stream", 393 | "text": [ 394 | "3.36 3.26 3.23 3.27 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 395 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_08.png\n", 396 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_08.png\n", 397 | "tpred = 3.260\n", 398 | "terr(1 x pb_std) = (-0.030, +0.010)\n", 399 | "pick_class = 0\n", 400 | "pb, pb_std = (0.512, 0.164)\n", 401 | "#\n", 402 | "P pick: 9/10\n" 403 | ] 404 | }, 405 | { 406 | "name": "stderr", 407 | "output_type": "stream", 408 | "text": [ 409 | "100%|██████████| 10/10 [00:01<00:00, 5.06it/s]\n" 410 | ] 411 | }, 412 | { 413 | "name": "stdout", 414 | "output_type": "stream", 415 | "text": [ 416 | "3.36 3.54 3.5 3.59 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 417 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_09.png\n", 418 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_09.png\n", 419 | "tpred = 3.540\n", 420 | "terr(1 x pb_std) = (-0.040, +0.050)\n", 421 | "pick_class = 0\n", 422 | "pb, pb_std = (0.515, 0.170)\n", 423 | "#\n", 424 | "P pick: 10/10\n" 425 | ] 426 | }, 427 | { 428 | "name": "stderr", 429 | "output_type": "stream", 430 | "text": [ 431 | "100%|██████████| 10/10 [00:02<00:00, 4.34it/s]\n" 432 | ] 433 | }, 434 | { 435 | "name": "stdout", 436 | "output_type": "stream", 437 | "text": [ 438 | "3.36 3.07 3.03 3.21 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 439 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_10.png\n", 440 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_P_mc_10.png\n", 441 | "tpred = 3.070\n", 442 | "terr(1 x pb_std) = (-0.040, +0.140)\n", 443 | "pick_class = 1\n", 444 | "pb, pb_std = (0.530, 0.293)\n", 445 | "#\n", 446 | "S pick: 1/6\n" 447 | ] 448 | }, 449 | { 450 | "name": "stderr", 451 | "output_type": "stream", 452 | "text": [ 453 | "100%|██████████| 10/10 [00:03<00:00, 3.00it/s]\n" 454 | ] 455 | }, 456 | { 457 | "name": "stdout", 458 | "output_type": "stream", 459 | "text": [ 460 | "2.4 2.67 2.65 2.7 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 461 | "(480,) (480,)\n", 462 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_01.png\n", 463 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_01.png\n", 464 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_mc_01.png\n", 465 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_mc_01.png\n", 466 | "tpred = 2.670\n", 467 | "terr(1 x pb_std) = (-0.020, +0.030)\n", 468 | "pick_class = 0\n", 469 | "pb, pb_std = (0.522, 0.072)\n", 470 | "#\n", 471 | "S pick: 2/6\n" 472 | ] 473 | }, 474 | { 475 | "name": "stderr", 476 | "output_type": "stream", 477 | "text": [ 478 | "100%|██████████| 10/10 [00:01<00:00, 6.96it/s]\n" 479 | ] 480 | }, 481 | { 482 | "name": "stdout", 483 | "output_type": "stream", 484 | "text": [ 485 | "2.4 2.42 2.4 2.44 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 486 | "(480,) (480,)\n", 487 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_02.png\n", 488 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_02.png\n", 489 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_mc_02.png\n", 490 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_mc_02.png\n", 491 | "tpred = 2.420\n", 492 | "terr(1 x pb_std) = (-0.020, +0.020)\n", 493 | "pick_class = 0\n", 494 | "pb, pb_std = (0.509, 0.078)\n", 495 | "#\n", 496 | "S pick: 3/6\n" 497 | ] 498 | }, 499 | { 500 | "name": "stderr", 501 | "output_type": "stream", 502 | "text": [ 503 | "100%|██████████| 10/10 [00:01<00:00, 7.14it/s]\n" 504 | ] 505 | }, 506 | { 507 | "name": "stdout", 508 | "output_type": "stream", 509 | "text": [ 510 | "2.4 2.36 2.27 2.55 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 511 | "(480,) (480,)\n", 512 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_03.png\n", 513 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_03.png\n", 514 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_mc_03.png\n", 515 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_mc_03.png\n", 516 | "tpred = 2.360\n", 517 | "terr(1 x pb_std) = (-0.090, +0.190)\n", 518 | "pick_class = 2\n", 519 | "pb, pb_std = (0.503, 0.132)\n", 520 | "#\n", 521 | "S pick: 4/6\n" 522 | ] 523 | }, 524 | { 525 | "name": "stderr", 526 | "output_type": "stream", 527 | "text": [ 528 | "100%|██████████| 10/10 [00:01<00:00, 7.46it/s]\n" 529 | ] 530 | }, 531 | { 532 | "name": "stdout", 533 | "output_type": "stream", 534 | "text": [ 535 | "2.4 2.44 2.27 2.51 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 536 | "(480,) (480,)\n", 537 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_04.png\n", 538 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_04.png\n", 539 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_mc_04.png\n", 540 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_mc_04.png\n", 541 | "tpred = 2.440\n", 542 | "terr(1 x pb_std) = (-0.170, +0.070)\n", 543 | "pick_class = 2\n", 544 | "pb, pb_std = (0.512, 0.171)\n", 545 | "#\n", 546 | "S pick: 5/6\n" 547 | ] 548 | }, 549 | { 550 | "name": "stderr", 551 | "output_type": "stream", 552 | "text": [ 553 | "100%|██████████| 10/10 [00:01<00:00, 8.04it/s]\n" 554 | ] 555 | }, 556 | { 557 | "name": "stdout", 558 | "output_type": "stream", 559 | "text": [ 560 | "2.4 2.4 2.29 2.5 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 561 | "(480,) (480,)\n", 562 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_05.png\n", 563 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_05.png\n", 564 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_mc_05.png\n", 565 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_mc_05.png\n", 566 | "tpred = 2.400\n", 567 | "terr(1 x pb_std) = (-0.110, +0.100)\n", 568 | "pick_class = 2\n", 569 | "pb, pb_std = (0.505, 0.101)\n", 570 | "#\n", 571 | "S pick: 6/6\n" 572 | ] 573 | }, 574 | { 575 | "name": "stderr", 576 | "output_type": "stream", 577 | "text": [ 578 | "100%|██████████| 10/10 [00:01<00:00, 7.39it/s]\n" 579 | ] 580 | }, 581 | { 582 | "name": "stdout", 583 | "output_type": "stream", 584 | "text": [ 585 | "2.4 2.48 2.39 2.65 out_CX_20140401/20140401T020000_20140401T030000 PB01\n", 586 | "(480,) (480,)\n", 587 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_06.png\n", 588 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_06.png\n", 589 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_E_mc_06.png\n", 590 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB01_S_N_mc_06.png\n", 591 | "tpred = 2.480\n", 592 | "terr(1 x pb_std) = (-0.090, +0.170)\n", 593 | "pick_class = 2\n", 594 | "pb, pb_std = (0.506, 0.248)\n", 595 | "#\n", 596 | "1, 2014-04-01T02:00:00.000000Z, 2014-04-01T03:00:00.000000Z, PB02\n", 597 | "triggered picks (P, S): 10, 4\n", 598 | "selected picks (P, S): 10, 3\n", 599 | "#\n", 600 | "P pick: 1/10\n" 601 | ] 602 | }, 603 | { 604 | "name": "stderr", 605 | "output_type": "stream", 606 | "text": [ 607 | "100%|██████████| 10/10 [00:01<00:00, 5.90it/s]\n" 608 | ] 609 | }, 610 | { 611 | "name": "stdout", 612 | "output_type": "stream", 613 | "text": [ 614 | "3.36 3.21 3.2 3.22 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 615 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_01.png\n", 616 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_01.png\n", 617 | "tpred = 3.210\n", 618 | "terr(1 x pb_std) = (-0.010, +0.010)\n", 619 | "pick_class = 0\n", 620 | "pb, pb_std = (0.580, 0.125)\n", 621 | "#\n", 622 | "P pick: 2/10\n" 623 | ] 624 | }, 625 | { 626 | "name": "stderr", 627 | "output_type": "stream", 628 | "text": [ 629 | "100%|██████████| 10/10 [00:01<00:00, 6.49it/s]\n" 630 | ] 631 | }, 632 | { 633 | "name": "stdout", 634 | "output_type": "stream", 635 | "text": [ 636 | "3.36 3.37 3.36 3.38 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 637 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_02.png\n", 638 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_02.png\n", 639 | "tpred = 3.370\n", 640 | "terr(1 x pb_std) = (-0.010, +0.010)\n", 641 | "pick_class = 0\n", 642 | "pb, pb_std = (0.574, 0.156)\n", 643 | "#\n", 644 | "P pick: 3/10\n" 645 | ] 646 | }, 647 | { 648 | "name": "stderr", 649 | "output_type": "stream", 650 | "text": [ 651 | "100%|██████████| 10/10 [00:02<00:00, 3.62it/s]\n" 652 | ] 653 | }, 654 | { 655 | "name": "stdout", 656 | "output_type": "stream", 657 | "text": [ 658 | "3.36 3.29 3.24 3.37 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 659 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_03.png\n", 660 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_03.png\n", 661 | "tpred = 3.290\n", 662 | "terr(1 x pb_std) = (-0.050, +0.080)\n", 663 | "pick_class = 1\n", 664 | "pb, pb_std = (0.503, 0.209)\n", 665 | "#\n", 666 | "P pick: 4/10\n" 667 | ] 668 | }, 669 | { 670 | "name": "stderr", 671 | "output_type": "stream", 672 | "text": [ 673 | "100%|██████████| 10/10 [00:01<00:00, 5.29it/s]\n" 674 | ] 675 | }, 676 | { 677 | "name": "stdout", 678 | "output_type": "stream", 679 | "text": [ 680 | "3.36 3.13 3.13 3.14 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 681 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_04.png\n", 682 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_04.png\n", 683 | "tpred = 3.130\n", 684 | "terr(1 x pb_std) = (-0.000, +0.010)\n", 685 | "pick_class = 0\n", 686 | "pb, pb_std = (0.601, 0.132)\n", 687 | "#\n", 688 | "P pick: 5/10\n" 689 | ] 690 | }, 691 | { 692 | "name": "stderr", 693 | "output_type": "stream", 694 | "text": [ 695 | "100%|██████████| 10/10 [00:01<00:00, 5.20it/s]\n" 696 | ] 697 | }, 698 | { 699 | "name": "stdout", 700 | "output_type": "stream", 701 | "text": [ 702 | "3.36 3.4 3.34 3.42 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 703 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_05.png\n", 704 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_05.png\n", 705 | "tpred = 3.400\n", 706 | "terr(1 x pb_std) = (-0.060, +0.020)\n", 707 | "pick_class = 0\n", 708 | "pb, pb_std = (0.542, 0.147)\n", 709 | "#\n", 710 | "P pick: 6/10\n" 711 | ] 712 | }, 713 | { 714 | "name": "stderr", 715 | "output_type": "stream", 716 | "text": [ 717 | "100%|██████████| 10/10 [00:01<00:00, 5.85it/s]\n" 718 | ] 719 | }, 720 | { 721 | "name": "stdout", 722 | "output_type": "stream", 723 | "text": [ 724 | "3.36 3.25 3.16 3.4 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 725 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_06.png\n", 726 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_06.png\n", 727 | "tpred = 3.250\n", 728 | "terr(1 x pb_std) = (-0.090, +0.150)\n", 729 | "pick_class = 2\n", 730 | "pb, pb_std = (0.504, 0.311)\n", 731 | "#\n", 732 | "P pick: 7/10\n" 733 | ] 734 | }, 735 | { 736 | "name": "stderr", 737 | "output_type": "stream", 738 | "text": [ 739 | "100%|██████████| 10/10 [00:01<00:00, 6.52it/s]\n" 740 | ] 741 | }, 742 | { 743 | "name": "stdout", 744 | "output_type": "stream", 745 | "text": [ 746 | "3.36 3.28 3.27 3.3 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 747 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_07.png\n", 748 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_07.png\n", 749 | "tpred = 3.280\n", 750 | "terr(1 x pb_std) = (-0.010, +0.020)\n", 751 | "pick_class = 0\n", 752 | "pb, pb_std = (0.564, 0.156)\n", 753 | "#\n", 754 | "P pick: 8/10\n" 755 | ] 756 | }, 757 | { 758 | "name": "stderr", 759 | "output_type": "stream", 760 | "text": [ 761 | "100%|██████████| 10/10 [00:01<00:00, 6.17it/s]\n" 762 | ] 763 | }, 764 | { 765 | "name": "stdout", 766 | "output_type": "stream", 767 | "text": [ 768 | "3.36 3.53 3.52 3.54 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 769 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_08.png\n", 770 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_08.png\n", 771 | "tpred = 3.530\n", 772 | "terr(1 x pb_std) = (-0.010, +0.010)\n", 773 | "pick_class = 0\n", 774 | "pb, pb_std = (0.521, 0.131)\n", 775 | "#\n", 776 | "P pick: 9/10\n" 777 | ] 778 | }, 779 | { 780 | "name": "stderr", 781 | "output_type": "stream", 782 | "text": [ 783 | "100%|██████████| 10/10 [00:01<00:00, 6.31it/s]\n" 784 | ] 785 | }, 786 | { 787 | "name": "stdout", 788 | "output_type": "stream", 789 | "text": [ 790 | "3.36 3.37 3.35 3.38 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 791 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_09.png\n", 792 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_09.png\n", 793 | "tpred = 3.370\n", 794 | "terr(1 x pb_std) = (-0.020, +0.010)\n", 795 | "pick_class = 0\n", 796 | "pb, pb_std = (0.518, 0.194)\n", 797 | "#\n", 798 | "P pick: 10/10\n" 799 | ] 800 | }, 801 | { 802 | "name": "stderr", 803 | "output_type": "stream", 804 | "text": [ 805 | "100%|██████████| 10/10 [00:02<00:00, 4.44it/s]\n" 806 | ] 807 | }, 808 | { 809 | "name": "stdout", 810 | "output_type": "stream", 811 | "text": [ 812 | "3.36 3.66 3.62 3.73 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 813 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_10.png\n", 814 | "plotting predicted phase P: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_P_mc_10.png\n", 815 | "tpred = 3.660\n", 816 | "terr(1 x pb_std) = (-0.040, +0.070)\n", 817 | "pick_class = 1\n", 818 | "pb, pb_std = (0.517, 0.158)\n", 819 | "#\n", 820 | "S pick: 1/3\n" 821 | ] 822 | }, 823 | { 824 | "name": "stderr", 825 | "output_type": "stream", 826 | "text": [ 827 | "100%|██████████| 10/10 [00:01<00:00, 6.24it/s]\n" 828 | ] 829 | }, 830 | { 831 | "name": "stdout", 832 | "output_type": "stream", 833 | "text": [ 834 | "2.4 2.19 2.18 2.2 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 835 | "(480,) (480,)\n", 836 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_E_01.png\n", 837 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_N_01.png\n", 838 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_E_mc_01.png\n", 839 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_N_mc_01.png\n", 840 | "tpred = 2.190\n", 841 | "terr(1 x pb_std) = (-0.010, +0.010)\n", 842 | "pick_class = 0\n", 843 | "pb, pb_std = (0.504, 0.071)\n", 844 | "#\n", 845 | "S pick: 2/3\n" 846 | ] 847 | }, 848 | { 849 | "name": "stderr", 850 | "output_type": "stream", 851 | "text": [ 852 | "100%|██████████| 10/10 [00:01<00:00, 7.11it/s]\n" 853 | ] 854 | }, 855 | { 856 | "name": "stdout", 857 | "output_type": "stream", 858 | "text": [ 859 | "2.4 2.21 2.18 2.26 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 860 | "(480,) (480,)\n", 861 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_E_02.png\n", 862 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_N_02.png\n", 863 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_E_mc_02.png\n", 864 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_N_mc_02.png\n", 865 | "tpred = 2.210\n", 866 | "terr(1 x pb_std) = (-0.030, +0.050)\n", 867 | "pick_class = 0\n", 868 | "pb, pb_std = (0.511, 0.144)\n", 869 | "#\n", 870 | "S pick: 3/3\n" 871 | ] 872 | }, 873 | { 874 | "name": "stderr", 875 | "output_type": "stream", 876 | "text": [ 877 | "100%|██████████| 10/10 [00:01<00:00, 8.20it/s]\n" 878 | ] 879 | }, 880 | { 881 | "name": "stdout", 882 | "output_type": "stream", 883 | "text": [ 884 | "2.4 2.47 2.45 2.49 out_CX_20140401/20140401T020000_20140401T030000 PB02\n", 885 | "(480,) (480,)\n", 886 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_E_03.png\n", 887 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_N_03.png\n", 888 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_E_mc_03.png\n", 889 | "plotting predicted phase S: out_CX_20140401/20140401T020000_20140401T030000/pick_plots/PB02_S_N_mc_03.png\n", 890 | "tpred = 2.470\n", 891 | "terr(1 x pb_std) = (-0.020, +0.020)\n", 892 | "pick_class = 0\n", 893 | "pb, pb_std = (0.505, 0.135)\n" 894 | ] 895 | } 896 | ], 897 | "source": [ 898 | "# run phase picking\n", 899 | "dpp_model.run_picking(dpp_config, dpp_data, save_plots=True, save_stats=True, save_picks=False)" 900 | ] 901 | }, 902 | { 903 | "cell_type": "markdown", 904 | "metadata": {}, 905 | "source": [ 906 | "## 4. Plot predicted phases" 907 | ] 908 | }, 909 | { 910 | "cell_type": "code", 911 | "execution_count": 8, 912 | "metadata": {}, 913 | "outputs": [ 914 | { 915 | "name": "stdout", 916 | "output_type": "stream", 917 | "text": [ 918 | "creating plots...\n", 919 | "1 PB01 Z 2014-04-01T01:59:59.998393Z 2014-04-01T02:59:59.998393Z\n", 920 | "1 PB01 E 2014-04-01T01:59:59.998394Z 2014-04-01T02:59:59.998394Z\n", 921 | "1 PB02 Z 2014-04-01T01:59:59.998393Z 2014-04-01T02:59:59.998393Z\n", 922 | "1 PB02 E 2014-04-01T01:59:59.998393Z 2014-04-01T02:59:59.998393Z\n" 923 | ] 924 | } 925 | ], 926 | "source": [ 927 | "# plots\n", 928 | "util.plot_predicted_phases(dpp_config, dpp_data, dpp_model)\n", 929 | "# util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_probs=['P','S'], shift_probs=True)" 930 | ] 931 | } 932 | ], 933 | "metadata": { 934 | "jupytext": { 935 | "text_representation": { 936 | "extension": ".py", 937 | "format_name": "light", 938 | "format_version": "1.4", 939 | "jupytext_version": "1.2.4" 940 | } 941 | }, 942 | "kernelspec": { 943 | "display_name": "Python 3", 944 | "language": "python", 945 | "name": "python3" 946 | }, 947 | "language_info": { 948 | "codemirror_mode": { 949 | "name": "ipython", 950 | "version": 3 951 | }, 952 | "file_extension": ".py", 953 | "mimetype": "text/x-python", 954 | "name": "python", 955 | "nbconvert_exporter": "python", 956 | "pygments_lexer": "ipython3", 957 | "version": "3.6.13" 958 | } 959 | }, 960 | "nbformat": 4, 961 | "nbformat_minor": 2 962 | } 963 | -------------------------------------------------------------------------------- /examples/run_dpp_download_data.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # 3 | # This script applies DeepPhasePick on seismic data downloaded using FDSN web service client for ObsPy. 4 | # 5 | # Author: Hugo Soto Parada (June, 2021) 6 | # Contact: soto@gfz-potsdam.de, hugosotoparada@gmail.com 7 | # 8 | ######################################################################################################################################## 9 | 10 | import os 11 | import config, data, model, util 12 | from obspy.clients.fdsn import Client 13 | import obspy.core as oc 14 | 15 | # 1. Configure DPP 16 | # 17 | # config 18 | util.init_session() 19 | dpp_config = config.Config() 20 | dpp_config.set_trigger(pthres_p=[0.9, 0.001], pthres_s=[0.9, 0.001]) 21 | dpp_config.set_picking(mcd_iter=10, run_mcd=True) 22 | # dpp_config.set_picking(run_mcd=False) 23 | # 24 | dpp_config.set_data( 25 | stas=['PB01', 'PB02'], 26 | net='CX', 27 | ch='HH', 28 | archive='sample_data/CX_20140401', 29 | opath='out_CX_20140401' 30 | ) 31 | dpp_config.set_time( 32 | dt_iter=3600., 33 | tstart="2014-04-01T02:00:00", 34 | tend="2014-04-01T03:00:00", 35 | ) 36 | 37 | # 2. Download seismic data and read it into DPP 38 | # 39 | # download and archive seismic waveforms 40 | client = Client("GFZ") 41 | os.makedirs(f"{dpp_config.data['archive']}", exist_ok=True) 42 | tstart = oc.UTCDateTime(dpp_config.time['tstart']) 43 | tend = oc.UTCDateTime(dpp_config.time['tend']) 44 | # 45 | for sta in dpp_config.data['stas']: 46 | st = client.get_waveforms(network=dpp_config.data['net'], station=sta, location="*", channel=f"{dpp_config.data['ch']}?", starttime=tstart, endtime=tend) 47 | # print(st) 48 | st_name = f"{dpp_config.data['archive']}/{st[0].stats.network}.{st[0].stats.station}..{st[0].stats.channel[:-1]}.mseed" 49 | print(f"writing stream {st_name}...") 50 | st.write(st_name, format="MSEED") 51 | # 52 | # data 53 | dpp_data = data.Data() 54 | dpp_data.read_from_directory(dpp_config) 55 | # 56 | # for k in dpp_data.data: 57 | # print(k, dpp_data.data[k]) 58 | 59 | # 3. Run phase detection and picking 60 | # 61 | # model 62 | # dpp_model = model.Model(verbose=False) 63 | dpp_model = model.Model(verbose=False, version_pick_P="20201002_2", version_pick_S="20201002_2") 64 | # 65 | # print(dpp_model.model_detection['best_model'].summary()) 66 | # print(dpp_model.model_picking_P['best_model'].summary()) 67 | # print(dpp_model.model_picking_S['best_model'].summary()) 68 | # 69 | # run phase detection 70 | dpp_model.run_detection(dpp_config, dpp_data, save_dets=False, save_data=False) 71 | # 72 | # run phase picking 73 | dpp_model.run_picking(dpp_config, dpp_data, save_plots=True, save_stats=True, save_picks=False) 74 | 75 | # 4. Plot predicted phases 76 | # 77 | # plots 78 | util.plot_predicted_phases(dpp_config, dpp_data, dpp_model) 79 | # util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_probs=['P','S'], shift_probs=True) 80 | 81 | -------------------------------------------------------------------------------- /examples/run_dpp_from_archive.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "### This example applies DeepPhasePick on seismic data stored in a archive directory structured as: archive/YY/NET/STA/CH.

Here YY is year, NET is the network code, STA is the station code and CH is the channel code (e.g., HH) of the seismic streams." 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import os\n", 17 | "os.chdir('../')\n", 18 | "import config, data, model, util " 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "## 1. Configure DPP" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [ 33 | { 34 | "name": "stdout", 35 | "output_type": "stream", 36 | "text": [ 37 | "__pycache__/ removed\n", 38 | "~/.nv/ not found, continuing...\n" 39 | ] 40 | } 41 | ], 42 | "source": [ 43 | "# config\n", 44 | "util.init_session()\n", 45 | "dpp_config = config.Config()\n", 46 | "dpp_config.set_trigger(pthres_p=[0.9, 0.001], pthres_s=[0.9, 0.001])\n", 47 | "# dpp_config.set_picking(mcd_iter=10, run_mcd=True)\n", 48 | "dpp_config.set_picking(run_mcd=False)\n", 49 | "#\n", 50 | "dpp_config.set_data(\n", 51 | " stas=['PB01', 'PB02'],\n", 52 | " net='CX',\n", 53 | " ch='HH',\n", 54 | " archive='sample_data/archive',\n", 55 | " opath='out_archive'\n", 56 | ")\n", 57 | "dpp_config.set_time(\n", 58 | " dt_iter=3600.,\n", 59 | " #\n", 60 | " tstart = \"2014-05-01T00:00:00\",\n", 61 | " tend = \"2014-05-01T12:00:00\", \n", 62 | ")" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "## 2. Read seismic data into DPP" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 3, 75 | "metadata": { 76 | "scrolled": true 77 | }, 78 | "outputs": [ 79 | { 80 | "name": "stdout", 81 | "output_type": "stream", 82 | "text": [ 83 | "#\n", 84 | "time windows (12) for iteration over continuous waveforms:\n", 85 | "[UTCDateTime(2014, 5, 1, 0, 0), UTCDateTime(2014, 5, 1, 1, 0)]\n", 86 | "[UTCDateTime(2014, 5, 1, 1, 0), UTCDateTime(2014, 5, 1, 2, 0)]\n", 87 | "[UTCDateTime(2014, 5, 1, 2, 0), UTCDateTime(2014, 5, 1, 3, 0)]\n", 88 | "[UTCDateTime(2014, 5, 1, 3, 0), UTCDateTime(2014, 5, 1, 4, 0)]\n", 89 | "[UTCDateTime(2014, 5, 1, 4, 0), UTCDateTime(2014, 5, 1, 5, 0)]\n", 90 | "[UTCDateTime(2014, 5, 1, 5, 0), UTCDateTime(2014, 5, 1, 6, 0)]\n", 91 | "[UTCDateTime(2014, 5, 1, 6, 0), UTCDateTime(2014, 5, 1, 7, 0)]\n", 92 | "[UTCDateTime(2014, 5, 1, 7, 0), UTCDateTime(2014, 5, 1, 8, 0)]\n", 93 | "[UTCDateTime(2014, 5, 1, 8, 0), UTCDateTime(2014, 5, 1, 9, 0)]\n", 94 | "[UTCDateTime(2014, 5, 1, 9, 0), UTCDateTime(2014, 5, 1, 10, 0)]\n", 95 | "[UTCDateTime(2014, 5, 1, 10, 0), UTCDateTime(2014, 5, 1, 11, 0)]\n", 96 | "[UTCDateTime(2014, 5, 1, 11, 0), UTCDateTime(2014, 5, 1, 12, 0)]\n", 97 | "\n", 98 | "\n", 99 | "retrieving seismic waveforms for stations:\n", 100 | "['PB01', 'PB02']\n", 101 | "seismic data found for: net = CX, sta = PB01, st_count = 0, st_arg = 0\n", 102 | "['sample_data/archive/2014/CX/PB01/HHE.D/CX.PB01..HHE.D.2014.121', 'sample_data/archive/2014/CX/PB01/HHZ.D/CX.PB01..HHZ.D.2014.121', 'sample_data/archive/2014/CX/PB01/HHN.D/CX.PB01..HHN.D.2014.121']\n", 103 | "sample_data/archive/2014/CX/PB01/HHE.D/CX.PB01..HHE.D.2014.121\n", 104 | "sample_data/archive/2014/CX/PB01/HHZ.D/CX.PB01..HHZ.D.2014.121\n", 105 | "sample_data/archive/2014/CX/PB01/HHN.D/CX.PB01..HHN.D.2014.121\n", 106 | "seismic data found for: net = CX, sta = PB02, st_count = 3, st_arg = 0\n", 107 | "['sample_data/archive/2014/CX/PB02/HHE.D/CX.PB02..HHE.D.2014.121', 'sample_data/archive/2014/CX/PB02/HHZ.D/CX.PB02..HHZ.D.2014.121', 'sample_data/archive/2014/CX/PB02/HHN.D/CX.PB02..HHN.D.2014.121']\n", 108 | "sample_data/archive/2014/CX/PB02/HHE.D/CX.PB02..HHE.D.2014.121\n", 109 | "sample_data/archive/2014/CX/PB02/HHZ.D/CX.PB02..HHZ.D.2014.121\n", 110 | "sample_data/archive/2014/CX/PB02/HHN.D/CX.PB02..HHN.D.2014.121\n", 111 | "6 Trace(s) in Stream:\n", 112 | "CX.PB01..HHE | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples\n", 113 | "CX.PB01..HHZ | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples\n", 114 | "CX.PB01..HHN | 2014-04-30T23:59:59.998391Z - 2014-05-01T11:59:59.998391Z | 100.0 Hz, 4320001 samples\n", 115 | "CX.PB02..HHE | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples\n", 116 | "CX.PB02..HHZ | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples\n", 117 | "CX.PB02..HHN | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples\n", 118 | "#\n", 119 | "processing raw stream data...\n", 120 | "detrend...\n", 121 | "resampling...\n", 122 | "CX.PB01..HHE | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples --> skipped, already sampled at 100.0 Hz\n", 123 | "CX.PB01..HHZ | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples --> skipped, already sampled at 100.0 Hz\n", 124 | "CX.PB01..HHN | 2014-04-30T23:59:59.998391Z - 2014-05-01T11:59:59.998391Z | 100.0 Hz, 4320001 samples --> skipped, already sampled at 100.0 Hz\n", 125 | "CX.PB02..HHE | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples --> skipped, already sampled at 100.0 Hz\n", 126 | "CX.PB02..HHZ | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples --> skipped, already sampled at 100.0 Hz\n", 127 | "CX.PB02..HHN | 2014-04-30T23:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 4320001 samples --> skipped, already sampled at 100.0 Hz\n", 128 | "merging...\n", 129 | "slicing...\n", 130 | "slicing...\n", 131 | "slicing...\n", 132 | "slicing...\n", 133 | "slicing...\n", 134 | "slicing...\n", 135 | "slicing...\n", 136 | "slicing...\n", 137 | "slicing...\n", 138 | "slicing...\n", 139 | "slicing...\n", 140 | "slicing...\n", 141 | "1 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 0, 0), UTCDateTime(2014, 5, 1, 1, 0)], 'opath': 'out_archive/20140501T000000_20140501T010000'}\n", 142 | "2 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 1, 0), UTCDateTime(2014, 5, 1, 2, 0)], 'opath': 'out_archive/20140501T010000_20140501T020000'}\n", 143 | "3 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 2, 0), UTCDateTime(2014, 5, 1, 3, 0)], 'opath': 'out_archive/20140501T020000_20140501T030000'}\n", 144 | "4 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 3, 0), UTCDateTime(2014, 5, 1, 4, 0)], 'opath': 'out_archive/20140501T030000_20140501T040000'}\n", 145 | "5 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 4, 0), UTCDateTime(2014, 5, 1, 5, 0)], 'opath': 'out_archive/20140501T040000_20140501T050000'}\n", 146 | "6 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 5, 0), UTCDateTime(2014, 5, 1, 6, 0)], 'opath': 'out_archive/20140501T050000_20140501T060000'}\n", 147 | "7 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 6, 0), UTCDateTime(2014, 5, 1, 7, 0)], 'opath': 'out_archive/20140501T060000_20140501T070000'}\n", 148 | "8 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 7, 0), UTCDateTime(2014, 5, 1, 8, 0)], 'opath': 'out_archive/20140501T070000_20140501T080000'}\n", 149 | "9 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 8, 0), UTCDateTime(2014, 5, 1, 9, 0)], 'opath': 'out_archive/20140501T080000_20140501T090000'}\n", 150 | "10 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 9, 0), UTCDateTime(2014, 5, 1, 10, 0)], 'opath': 'out_archive/20140501T090000_20140501T100000'}\n", 151 | "11 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 10, 0), UTCDateTime(2014, 5, 1, 11, 0)], 'opath': 'out_archive/20140501T100000_20140501T110000'}\n", 152 | "12 {'st': {'PB01': , 'PB02': }, 'twin': [UTCDateTime(2014, 5, 1, 11, 0), UTCDateTime(2014, 5, 1, 12, 0)], 'opath': 'out_archive/20140501T110000_20140501T120000'}\n" 153 | ] 154 | } 155 | ], 156 | "source": [ 157 | "# data\n", 158 | "dpp_data = data.Data()\n", 159 | "dpp_data.read_from_archive(dpp_config)\n", 160 | "#\n", 161 | "for k in dpp_data.data:\n", 162 | " print(k, dpp_data.data[k])" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "## 3. Run phase detection and picking" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 4, 175 | "metadata": { 176 | "scrolled": true 177 | }, 178 | "outputs": [], 179 | "source": [ 180 | "# model\n", 181 | "dpp_model = model.Model(verbose=False)\n", 182 | "# dpp_model = model.Model(verbose=False, version_pick_P=\"20201002_2\", version_pick_S=\"20201002_2\")\n", 183 | "#\n", 184 | "# print(dpp_model.model_detection['best_model'].summary())\n", 185 | "# print(dpp_model.model_picking_P['best_model'].summary())\n", 186 | "# print(dpp_model.model_picking_S['best_model'].summary())" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": 5, 192 | "metadata": { 193 | "scrolled": true 194 | }, 195 | "outputs": [ 196 | { 197 | "name": "stdout", 198 | "output_type": "stream", 199 | "text": [ 200 | "#\n", 201 | "Calculating predictions for stream: CX.PB01..HH?...\n", 202 | "strimming stream: 2, 2\n", 203 | "720/720 [==============================] - 28s 39ms/step\n", 204 | "3 Trace(s) in Stream:\n", 205 | "CX.PB01..HHE | 2014-04-30T23:59:59.998393Z - 2014-05-01T00:59:59.998393Z | 100.0 Hz, 360001 samples\n", 206 | "CX.PB01..HHN | 2014-04-30T23:59:59.998391Z - 2014-05-01T00:59:59.998391Z | 100.0 Hz, 360001 samples\n", 207 | "CX.PB01..HHZ | 2014-04-30T23:59:59.998393Z - 2014-05-01T00:59:59.998393Z | 100.0 Hz, 360001 samples\n", 208 | "p_picks = 23, s_picks = 12\n", 209 | "#\n", 210 | "Calculating predictions for stream: CX.PB02..HH?...\n", 211 | "720/720 [==============================] - 35s 48ms/step\n", 212 | "3 Trace(s) in Stream:\n", 213 | "CX.PB02..HHE | 2014-04-30T23:59:59.998393Z - 2014-05-01T00:59:59.998393Z | 100.0 Hz, 360001 samples\n", 214 | "CX.PB02..HHN | 2014-04-30T23:59:59.998393Z - 2014-05-01T00:59:59.998393Z | 100.0 Hz, 360001 samples\n", 215 | "CX.PB02..HHZ | 2014-04-30T23:59:59.998393Z - 2014-05-01T00:59:59.998393Z | 100.0 Hz, 360001 samples\n", 216 | "p_picks = 12, s_picks = 11\n", 217 | "#\n", 218 | "Calculating predictions for stream: CX.PB01..HH?...\n", 219 | "strimming stream: 2, 2\n", 220 | "720/720 [==============================] - 37s 51ms/step\n", 221 | "3 Trace(s) in Stream:\n", 222 | "CX.PB01..HHE | 2014-05-01T00:59:59.998393Z - 2014-05-01T01:59:59.998393Z | 100.0 Hz, 360001 samples\n", 223 | "CX.PB01..HHN | 2014-05-01T00:59:59.998391Z - 2014-05-01T01:59:59.998391Z | 100.0 Hz, 360001 samples\n", 224 | "CX.PB01..HHZ | 2014-05-01T00:59:59.998393Z - 2014-05-01T01:59:59.998393Z | 100.0 Hz, 360001 samples\n", 225 | "p_picks = 25, s_picks = 19\n", 226 | "#\n", 227 | "Calculating predictions for stream: CX.PB02..HH?...\n", 228 | "720/720 [==============================] - 40s 56ms/step\n", 229 | "3 Trace(s) in Stream:\n", 230 | "CX.PB02..HHE | 2014-05-01T00:59:59.998393Z - 2014-05-01T01:59:59.998393Z | 100.0 Hz, 360001 samples\n", 231 | "CX.PB02..HHN | 2014-05-01T00:59:59.998393Z - 2014-05-01T01:59:59.998393Z | 100.0 Hz, 360001 samples\n", 232 | "CX.PB02..HHZ | 2014-05-01T00:59:59.998393Z - 2014-05-01T01:59:59.998393Z | 100.0 Hz, 360001 samples\n", 233 | "p_picks = 21, s_picks = 21\n", 234 | "#\n", 235 | "Calculating predictions for stream: CX.PB01..HH?...\n", 236 | "strimming stream: 2, 2\n", 237 | "720/720 [==============================] - 39s 54ms/step\n", 238 | "3 Trace(s) in Stream:\n", 239 | "CX.PB01..HHE | 2014-05-01T01:59:59.998393Z - 2014-05-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 240 | "CX.PB01..HHN | 2014-05-01T01:59:59.998391Z - 2014-05-01T02:59:59.998391Z | 100.0 Hz, 360001 samples\n", 241 | "CX.PB01..HHZ | 2014-05-01T01:59:59.998393Z - 2014-05-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 242 | "p_picks = 18, s_picks = 6\n", 243 | "#\n", 244 | "Calculating predictions for stream: CX.PB02..HH?...\n", 245 | "720/720 [==============================] - 38s 53ms/step\n", 246 | "3 Trace(s) in Stream:\n", 247 | "CX.PB02..HHE | 2014-05-01T01:59:59.998393Z - 2014-05-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 248 | "CX.PB02..HHN | 2014-05-01T01:59:59.998393Z - 2014-05-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 249 | "CX.PB02..HHZ | 2014-05-01T01:59:59.998393Z - 2014-05-01T02:59:59.998393Z | 100.0 Hz, 360001 samples\n", 250 | "p_picks = 13, s_picks = 6\n", 251 | "#\n", 252 | "Calculating predictions for stream: CX.PB01..HH?...\n", 253 | "strimming stream: 2, 2\n", 254 | "720/720 [==============================] - 41s 57ms/step\n", 255 | "3 Trace(s) in Stream:\n", 256 | "CX.PB01..HHE | 2014-05-01T02:59:59.998393Z - 2014-05-01T03:59:59.998393Z | 100.0 Hz, 360001 samples\n", 257 | "CX.PB01..HHN | 2014-05-01T02:59:59.998391Z - 2014-05-01T03:59:59.998391Z | 100.0 Hz, 360001 samples\n", 258 | "CX.PB01..HHZ | 2014-05-01T02:59:59.998393Z - 2014-05-01T03:59:59.998393Z | 100.0 Hz, 360001 samples\n", 259 | "p_picks = 14, s_picks = 4\n", 260 | "#\n", 261 | "Calculating predictions for stream: CX.PB02..HH?...\n", 262 | "720/720 [==============================] - 39s 54ms/step\n", 263 | "3 Trace(s) in Stream:\n", 264 | "CX.PB02..HHE | 2014-05-01T02:59:59.998393Z - 2014-05-01T03:59:59.998393Z | 100.0 Hz, 360001 samples\n", 265 | "CX.PB02..HHN | 2014-05-01T02:59:59.998393Z - 2014-05-01T03:59:59.998393Z | 100.0 Hz, 360001 samples\n", 266 | "CX.PB02..HHZ | 2014-05-01T02:59:59.998393Z - 2014-05-01T03:59:59.998393Z | 100.0 Hz, 360001 samples\n", 267 | "p_picks = 7, s_picks = 3\n", 268 | "#\n", 269 | "Calculating predictions for stream: CX.PB01..HH?...\n", 270 | "strimming stream: 2, 2\n", 271 | "720/720 [==============================] - 39s 55ms/step\n", 272 | "3 Trace(s) in Stream:\n", 273 | "CX.PB01..HHE | 2014-05-01T03:59:59.998393Z - 2014-05-01T04:59:59.998393Z | 100.0 Hz, 360001 samples\n", 274 | "CX.PB01..HHN | 2014-05-01T03:59:59.998391Z - 2014-05-01T04:59:59.998391Z | 100.0 Hz, 360001 samples\n", 275 | "CX.PB01..HHZ | 2014-05-01T03:59:59.998393Z - 2014-05-01T04:59:59.998393Z | 100.0 Hz, 360001 samples\n", 276 | "p_picks = 21, s_picks = 14\n", 277 | "#\n", 278 | "Calculating predictions for stream: CX.PB02..HH?...\n", 279 | "720/720 [==============================] - 32s 44ms/step\n", 280 | "3 Trace(s) in Stream:\n", 281 | "CX.PB02..HHE | 2014-05-01T03:59:59.998393Z - 2014-05-01T04:59:59.998393Z | 100.0 Hz, 360001 samples\n", 282 | "CX.PB02..HHN | 2014-05-01T03:59:59.998393Z - 2014-05-01T04:59:59.998393Z | 100.0 Hz, 360001 samples\n", 283 | "CX.PB02..HHZ | 2014-05-01T03:59:59.998393Z - 2014-05-01T04:59:59.998393Z | 100.0 Hz, 360001 samples\n", 284 | "p_picks = 19, s_picks = 10\n", 285 | "#\n", 286 | "Calculating predictions for stream: CX.PB01..HH?...\n", 287 | "strimming stream: 2, 2\n", 288 | "720/720 [==============================] - 40s 56ms/step\n", 289 | "3 Trace(s) in Stream:\n", 290 | "CX.PB01..HHE | 2014-05-01T04:59:59.998393Z - 2014-05-01T05:59:59.998393Z | 100.0 Hz, 360001 samples\n", 291 | "CX.PB01..HHN | 2014-05-01T04:59:59.998391Z - 2014-05-01T05:59:59.998391Z | 100.0 Hz, 360001 samples\n", 292 | "CX.PB01..HHZ | 2014-05-01T04:59:59.998393Z - 2014-05-01T05:59:59.998393Z | 100.0 Hz, 360001 samples\n", 293 | "p_picks = 19, s_picks = 10\n", 294 | "#\n", 295 | "Calculating predictions for stream: CX.PB02..HH?...\n", 296 | "720/720 [==============================] - 38s 52ms/step\n", 297 | "3 Trace(s) in Stream:\n", 298 | "CX.PB02..HHE | 2014-05-01T04:59:59.998393Z - 2014-05-01T05:59:59.998393Z | 100.0 Hz, 360001 samples\n", 299 | "CX.PB02..HHN | 2014-05-01T04:59:59.998393Z - 2014-05-01T05:59:59.998393Z | 100.0 Hz, 360001 samples\n", 300 | "CX.PB02..HHZ | 2014-05-01T04:59:59.998393Z - 2014-05-01T05:59:59.998393Z | 100.0 Hz, 360001 samples\n", 301 | "p_picks = 8, s_picks = 7\n", 302 | "#\n", 303 | "Calculating predictions for stream: CX.PB01..HH?...\n", 304 | "strimming stream: 2, 2\n", 305 | "720/720 [==============================] - 34s 48ms/step\n", 306 | "3 Trace(s) in Stream:\n", 307 | "CX.PB01..HHE | 2014-05-01T05:59:59.998393Z - 2014-05-01T06:59:59.998393Z | 100.0 Hz, 360001 samples\n", 308 | "CX.PB01..HHN | 2014-05-01T05:59:59.998391Z - 2014-05-01T06:59:59.998391Z | 100.0 Hz, 360001 samples\n", 309 | "CX.PB01..HHZ | 2014-05-01T05:59:59.998393Z - 2014-05-01T06:59:59.998393Z | 100.0 Hz, 360001 samples\n", 310 | "p_picks = 21, s_picks = 10\n", 311 | "#\n", 312 | "Calculating predictions for stream: CX.PB02..HH?...\n", 313 | "720/720 [==============================] - 36s 50ms/step\n", 314 | "3 Trace(s) in Stream:\n", 315 | "CX.PB02..HHE | 2014-05-01T05:59:59.998393Z - 2014-05-01T06:59:59.998393Z | 100.0 Hz, 360001 samples\n", 316 | "CX.PB02..HHN | 2014-05-01T05:59:59.998393Z - 2014-05-01T06:59:59.998393Z | 100.0 Hz, 360001 samples\n", 317 | "CX.PB02..HHZ | 2014-05-01T05:59:59.998393Z - 2014-05-01T06:59:59.998393Z | 100.0 Hz, 360001 samples\n", 318 | "p_picks = 8, s_picks = 5\n", 319 | "#\n", 320 | "Calculating predictions for stream: CX.PB01..HH?...\n", 321 | "strimming stream: 2, 2\n", 322 | "720/720 [==============================] - 37s 51ms/step\n", 323 | "3 Trace(s) in Stream:\n", 324 | "CX.PB01..HHE | 2014-05-01T06:59:59.998393Z - 2014-05-01T07:59:59.998393Z | 100.0 Hz, 360001 samples\n", 325 | "CX.PB01..HHN | 2014-05-01T06:59:59.998391Z - 2014-05-01T07:59:59.998391Z | 100.0 Hz, 360001 samples\n", 326 | "CX.PB01..HHZ | 2014-05-01T06:59:59.998393Z - 2014-05-01T07:59:59.998393Z | 100.0 Hz, 360001 samples\n", 327 | "p_picks = 18, s_picks = 13\n", 328 | "#\n", 329 | "Calculating predictions for stream: CX.PB02..HH?...\n", 330 | "720/720 [==============================] - 38s 53ms/step\n", 331 | "3 Trace(s) in Stream:\n", 332 | "CX.PB02..HHE | 2014-05-01T06:59:59.998393Z - 2014-05-01T07:59:59.998393Z | 100.0 Hz, 360001 samples\n", 333 | "CX.PB02..HHN | 2014-05-01T06:59:59.998393Z - 2014-05-01T07:59:59.998393Z | 100.0 Hz, 360001 samples\n", 334 | "CX.PB02..HHZ | 2014-05-01T06:59:59.998393Z - 2014-05-01T07:59:59.998393Z | 100.0 Hz, 360001 samples\n", 335 | "p_picks = 14, s_picks = 8\n", 336 | "#\n", 337 | "Calculating predictions for stream: CX.PB01..HH?...\n", 338 | "strimming stream: 2, 2\n", 339 | "720/720 [==============================] - 43s 60ms/step\n", 340 | "3 Trace(s) in Stream:\n", 341 | "CX.PB01..HHE | 2014-05-01T07:59:59.998393Z - 2014-05-01T08:59:59.998393Z | 100.0 Hz, 360001 samples\n", 342 | "CX.PB01..HHN | 2014-05-01T07:59:59.998391Z - 2014-05-01T08:59:59.998391Z | 100.0 Hz, 360001 samples\n", 343 | "CX.PB01..HHZ | 2014-05-01T07:59:59.998393Z - 2014-05-01T08:59:59.998393Z | 100.0 Hz, 360001 samples\n", 344 | "p_picks = 9, s_picks = 6\n", 345 | "#\n", 346 | "Calculating predictions for stream: CX.PB02..HH?...\n", 347 | "720/720 [==============================] - 36s 49ms/step\n", 348 | "3 Trace(s) in Stream:\n", 349 | "CX.PB02..HHE | 2014-05-01T07:59:59.998393Z - 2014-05-01T08:59:59.998393Z | 100.0 Hz, 360001 samples\n", 350 | "CX.PB02..HHN | 2014-05-01T07:59:59.998393Z - 2014-05-01T08:59:59.998393Z | 100.0 Hz, 360001 samples\n", 351 | "CX.PB02..HHZ | 2014-05-01T07:59:59.998393Z - 2014-05-01T08:59:59.998393Z | 100.0 Hz, 360001 samples\n", 352 | "p_picks = 8, s_picks = 4\n", 353 | "#\n", 354 | "Calculating predictions for stream: CX.PB01..HH?...\n", 355 | "strimming stream: 2, 2\n" 356 | ] 357 | }, 358 | { 359 | "name": "stdout", 360 | "output_type": "stream", 361 | "text": [ 362 | "720/720 [==============================] - 34s 48ms/step\n", 363 | "3 Trace(s) in Stream:\n", 364 | "CX.PB01..HHE | 2014-05-01T08:59:59.998393Z - 2014-05-01T09:59:59.998393Z | 100.0 Hz, 360001 samples\n", 365 | "CX.PB01..HHN | 2014-05-01T08:59:59.998391Z - 2014-05-01T09:59:59.998391Z | 100.0 Hz, 360001 samples\n", 366 | "CX.PB01..HHZ | 2014-05-01T08:59:59.998393Z - 2014-05-01T09:59:59.998393Z | 100.0 Hz, 360001 samples\n", 367 | "p_picks = 20, s_picks = 10\n", 368 | "#\n", 369 | "Calculating predictions for stream: CX.PB02..HH?...\n", 370 | "720/720 [==============================] - 30s 42ms/step\n", 371 | "3 Trace(s) in Stream:\n", 372 | "CX.PB02..HHE | 2014-05-01T08:59:59.998393Z - 2014-05-01T09:59:59.998393Z | 100.0 Hz, 360001 samples\n", 373 | "CX.PB02..HHN | 2014-05-01T08:59:59.998393Z - 2014-05-01T09:59:59.998393Z | 100.0 Hz, 360001 samples\n", 374 | "CX.PB02..HHZ | 2014-05-01T08:59:59.998393Z - 2014-05-01T09:59:59.998393Z | 100.0 Hz, 360001 samples\n", 375 | "p_picks = 14, s_picks = 9\n", 376 | "#\n", 377 | "Calculating predictions for stream: CX.PB01..HH?...\n", 378 | "strimming stream: 2, 2\n", 379 | "720/720 [==============================] - 32s 44ms/step\n", 380 | "3 Trace(s) in Stream:\n", 381 | "CX.PB01..HHE | 2014-05-01T09:59:59.998393Z - 2014-05-01T10:59:59.998393Z | 100.0 Hz, 360001 samples\n", 382 | "CX.PB01..HHN | 2014-05-01T09:59:59.998391Z - 2014-05-01T10:59:59.998391Z | 100.0 Hz, 360001 samples\n", 383 | "CX.PB01..HHZ | 2014-05-01T09:59:59.998393Z - 2014-05-01T10:59:59.998393Z | 100.0 Hz, 360001 samples\n", 384 | "p_picks = 11, s_picks = 5\n", 385 | "#\n", 386 | "Calculating predictions for stream: CX.PB02..HH?...\n", 387 | "720/720 [==============================] - 29s 40ms/step\n", 388 | "3 Trace(s) in Stream:\n", 389 | "CX.PB02..HHE | 2014-05-01T09:59:59.998393Z - 2014-05-01T10:59:59.998393Z | 100.0 Hz, 360001 samples\n", 390 | "CX.PB02..HHN | 2014-05-01T09:59:59.998393Z - 2014-05-01T10:59:59.998393Z | 100.0 Hz, 360001 samples\n", 391 | "CX.PB02..HHZ | 2014-05-01T09:59:59.998393Z - 2014-05-01T10:59:59.998393Z | 100.0 Hz, 360001 samples\n", 392 | "p_picks = 7, s_picks = 5\n", 393 | "#\n", 394 | "Calculating predictions for stream: CX.PB01..HH?...\n", 395 | "strimming stream: 2, 2\n", 396 | "720/720 [==============================] - 34s 47ms/step\n", 397 | "3 Trace(s) in Stream:\n", 398 | "CX.PB01..HHE | 2014-05-01T10:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 360001 samples\n", 399 | "CX.PB01..HHN | 2014-05-01T10:59:59.998391Z - 2014-05-01T11:59:59.998391Z | 100.0 Hz, 360001 samples\n", 400 | "CX.PB01..HHZ | 2014-05-01T10:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 360001 samples\n", 401 | "p_picks = 18, s_picks = 11\n", 402 | "#\n", 403 | "Calculating predictions for stream: CX.PB02..HH?...\n", 404 | "720/720 [==============================] - 32s 44ms/step\n", 405 | "3 Trace(s) in Stream:\n", 406 | "CX.PB02..HHE | 2014-05-01T10:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 360001 samples\n", 407 | "CX.PB02..HHN | 2014-05-01T10:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 360001 samples\n", 408 | "CX.PB02..HHZ | 2014-05-01T10:59:59.998393Z - 2014-05-01T11:59:59.998393Z | 100.0 Hz, 360001 samples\n", 409 | "p_picks = 17, s_picks = 11\n" 410 | ] 411 | } 412 | ], 413 | "source": [ 414 | "# run phase detection\n", 415 | "dpp_model.run_detection(dpp_config, dpp_data, save_dets=False, save_data=False)" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": 6, 421 | "metadata": { 422 | "scrolled": true 423 | }, 424 | "outputs": [ 425 | { 426 | "name": "stdout", 427 | "output_type": "stream", 428 | "text": [ 429 | "#\n", 430 | "1, 2014-05-01T00:00:00.000000Z, 2014-05-01T01:00:00.000000Z, PB01\n", 431 | "triggered picks (P, S): 23, 12\n", 432 | "selected picks (P, S): 21, 9\n", 433 | "#\n", 434 | "1, 2014-05-01T00:00:00.000000Z, 2014-05-01T01:00:00.000000Z, PB02\n", 435 | "triggered picks (P, S): 12, 11\n", 436 | "selected picks (P, S): 8, 5\n", 437 | "#\n", 438 | "2, 2014-05-01T01:00:00.000000Z, 2014-05-01T02:00:00.000000Z, PB01\n", 439 | "triggered picks (P, S): 25, 19\n", 440 | "selected picks (P, S): 21, 12\n", 441 | "#\n", 442 | "2, 2014-05-01T01:00:00.000000Z, 2014-05-01T02:00:00.000000Z, PB02\n", 443 | "triggered picks (P, S): 21, 21\n", 444 | "selected picks (P, S): 18, 7\n", 445 | "#\n", 446 | "3, 2014-05-01T02:00:00.000000Z, 2014-05-01T03:00:00.000000Z, PB01\n", 447 | "triggered picks (P, S): 18, 6\n", 448 | "selected picks (P, S): 16, 4\n", 449 | "#\n", 450 | "3, 2014-05-01T02:00:00.000000Z, 2014-05-01T03:00:00.000000Z, PB02\n", 451 | "triggered picks (P, S): 13, 6\n", 452 | "selected picks (P, S): 10, 2\n", 453 | "#\n", 454 | "4, 2014-05-01T03:00:00.000000Z, 2014-05-01T04:00:00.000000Z, PB01\n", 455 | "triggered picks (P, S): 14, 4\n", 456 | "selected picks (P, S): 13, 3\n", 457 | "#\n", 458 | "4, 2014-05-01T03:00:00.000000Z, 2014-05-01T04:00:00.000000Z, PB02\n", 459 | "triggered picks (P, S): 7, 3\n", 460 | "selected picks (P, S): 7, 1\n", 461 | "#\n", 462 | "5, 2014-05-01T04:00:00.000000Z, 2014-05-01T05:00:00.000000Z, PB01\n", 463 | "triggered picks (P, S): 21, 14\n", 464 | "selected picks (P, S): 20, 9\n", 465 | "#\n", 466 | "5, 2014-05-01T04:00:00.000000Z, 2014-05-01T05:00:00.000000Z, PB02\n", 467 | "triggered picks (P, S): 19, 10\n", 468 | "selected picks (P, S): 17, 5\n", 469 | "#\n", 470 | "6, 2014-05-01T05:00:00.000000Z, 2014-05-01T06:00:00.000000Z, PB01\n", 471 | "triggered picks (P, S): 19, 10\n", 472 | "selected picks (P, S): 18, 4\n", 473 | "#\n", 474 | "6, 2014-05-01T05:00:00.000000Z, 2014-05-01T06:00:00.000000Z, PB02\n", 475 | "triggered picks (P, S): 8, 7\n", 476 | "selected picks (P, S): 7, 3\n", 477 | "#\n", 478 | "7, 2014-05-01T06:00:00.000000Z, 2014-05-01T07:00:00.000000Z, PB01\n", 479 | "triggered picks (P, S): 21, 10\n", 480 | "selected picks (P, S): 18, 5\n", 481 | "#\n", 482 | "7, 2014-05-01T06:00:00.000000Z, 2014-05-01T07:00:00.000000Z, PB02\n", 483 | "triggered picks (P, S): 8, 5\n", 484 | "selected picks (P, S): 6, 3\n", 485 | "#\n", 486 | "8, 2014-05-01T07:00:00.000000Z, 2014-05-01T08:00:00.000000Z, PB01\n", 487 | "triggered picks (P, S): 18, 13\n", 488 | "selected picks (P, S): 16, 7\n", 489 | "#\n", 490 | "8, 2014-05-01T07:00:00.000000Z, 2014-05-01T08:00:00.000000Z, PB02\n", 491 | "triggered picks (P, S): 14, 8\n", 492 | "selected picks (P, S): 10, 3\n", 493 | "#\n", 494 | "9, 2014-05-01T08:00:00.000000Z, 2014-05-01T09:00:00.000000Z, PB01\n", 495 | "triggered picks (P, S): 9, 6\n", 496 | "selected picks (P, S): 9, 1\n", 497 | "#\n", 498 | "9, 2014-05-01T08:00:00.000000Z, 2014-05-01T09:00:00.000000Z, PB02\n", 499 | "triggered picks (P, S): 8, 4\n", 500 | "selected picks (P, S): 8, 1\n", 501 | "#\n", 502 | "10, 2014-05-01T09:00:00.000000Z, 2014-05-01T10:00:00.000000Z, PB01\n", 503 | "triggered picks (P, S): 20, 10\n", 504 | "selected picks (P, S): 19, 5\n", 505 | "#\n", 506 | "10, 2014-05-01T09:00:00.000000Z, 2014-05-01T10:00:00.000000Z, PB02\n", 507 | "triggered picks (P, S): 14, 9\n", 508 | "selected picks (P, S): 12, 4\n", 509 | "#\n", 510 | "11, 2014-05-01T10:00:00.000000Z, 2014-05-01T11:00:00.000000Z, PB01\n", 511 | "triggered picks (P, S): 11, 5\n", 512 | "selected picks (P, S): 10, 2\n", 513 | "#\n", 514 | "11, 2014-05-01T10:00:00.000000Z, 2014-05-01T11:00:00.000000Z, PB02\n", 515 | "triggered picks (P, S): 7, 5\n", 516 | "selected picks (P, S): 5, 1\n", 517 | "#\n", 518 | "12, 2014-05-01T11:00:00.000000Z, 2014-05-01T12:00:00.000000Z, PB01\n", 519 | "triggered picks (P, S): 18, 11\n", 520 | "selected picks (P, S): 14, 6\n", 521 | "#\n", 522 | "12, 2014-05-01T11:00:00.000000Z, 2014-05-01T12:00:00.000000Z, PB02\n", 523 | "triggered picks (P, S): 17, 11\n", 524 | "selected picks (P, S): 12, 5\n" 525 | ] 526 | } 527 | ], 528 | "source": [ 529 | "# run phase picking\n", 530 | "dpp_model.run_picking(dpp_config, dpp_data, save_plots=False, save_stats=True, save_picks=False)" 531 | ] 532 | }, 533 | { 534 | "cell_type": "code", 535 | "execution_count": 7, 536 | "metadata": { 537 | "scrolled": true 538 | }, 539 | "outputs": [ 540 | { 541 | "name": "stdout", 542 | "output_type": "stream", 543 | "text": [ 544 | "creating plots...\n", 545 | "1 PB01 Z 2014-04-30T23:59:59.998393Z 2014-05-01T00:59:59.998393Z\n", 546 | "1 PB01 E 2014-04-30T23:59:59.998393Z 2014-05-01T00:59:59.998393Z\n", 547 | "1 PB02 Z 2014-04-30T23:59:59.998393Z 2014-05-01T00:59:59.998393Z\n", 548 | "1 PB02 E 2014-04-30T23:59:59.998393Z 2014-05-01T00:59:59.998393Z\n", 549 | "2 PB01 Z 2014-05-01T00:59:59.998393Z 2014-05-01T01:59:59.998393Z\n", 550 | "2 PB01 E 2014-05-01T00:59:59.998393Z 2014-05-01T01:59:59.998393Z\n", 551 | "2 PB02 Z 2014-05-01T00:59:59.998393Z 2014-05-01T01:59:59.998393Z\n", 552 | "2 PB02 E 2014-05-01T00:59:59.998393Z 2014-05-01T01:59:59.998393Z\n", 553 | "3 PB01 Z 2014-05-01T01:59:59.998393Z 2014-05-01T02:59:59.998393Z\n", 554 | "3 PB01 E 2014-05-01T01:59:59.998393Z 2014-05-01T02:59:59.998393Z\n", 555 | "3 PB02 Z 2014-05-01T01:59:59.998393Z 2014-05-01T02:59:59.998393Z\n", 556 | "3 PB02 E 2014-05-01T01:59:59.998393Z 2014-05-01T02:59:59.998393Z\n", 557 | "4 PB01 Z 2014-05-01T02:59:59.998393Z 2014-05-01T03:59:59.998393Z\n", 558 | "4 PB01 E 2014-05-01T02:59:59.998393Z 2014-05-01T03:59:59.998393Z\n", 559 | "4 PB02 Z 2014-05-01T02:59:59.998393Z 2014-05-01T03:59:59.998393Z\n", 560 | "4 PB02 E 2014-05-01T02:59:59.998393Z 2014-05-01T03:59:59.998393Z\n", 561 | "5 PB01 Z 2014-05-01T03:59:59.998393Z 2014-05-01T04:59:59.998393Z\n", 562 | "5 PB01 E 2014-05-01T03:59:59.998393Z 2014-05-01T04:59:59.998393Z\n", 563 | "5 PB02 Z 2014-05-01T03:59:59.998393Z 2014-05-01T04:59:59.998393Z\n", 564 | "5 PB02 E 2014-05-01T03:59:59.998393Z 2014-05-01T04:59:59.998393Z\n", 565 | "6 PB01 Z 2014-05-01T04:59:59.998393Z 2014-05-01T05:59:59.998393Z\n", 566 | "6 PB01 E 2014-05-01T04:59:59.998393Z 2014-05-01T05:59:59.998393Z\n", 567 | "6 PB02 Z 2014-05-01T04:59:59.998393Z 2014-05-01T05:59:59.998393Z\n", 568 | "6 PB02 E 2014-05-01T04:59:59.998393Z 2014-05-01T05:59:59.998393Z\n", 569 | "7 PB01 Z 2014-05-01T05:59:59.998393Z 2014-05-01T06:59:59.998393Z\n", 570 | "7 PB01 E 2014-05-01T05:59:59.998393Z 2014-05-01T06:59:59.998393Z\n", 571 | "7 PB02 Z 2014-05-01T05:59:59.998393Z 2014-05-01T06:59:59.998393Z\n", 572 | "7 PB02 E 2014-05-01T05:59:59.998393Z 2014-05-01T06:59:59.998393Z\n", 573 | "8 PB01 Z 2014-05-01T06:59:59.998393Z 2014-05-01T07:59:59.998393Z\n", 574 | "8 PB01 E 2014-05-01T06:59:59.998393Z 2014-05-01T07:59:59.998393Z\n", 575 | "8 PB02 Z 2014-05-01T06:59:59.998393Z 2014-05-01T07:59:59.998393Z\n", 576 | "8 PB02 E 2014-05-01T06:59:59.998393Z 2014-05-01T07:59:59.998393Z\n", 577 | "9 PB01 Z 2014-05-01T07:59:59.998393Z 2014-05-01T08:59:59.998393Z\n", 578 | "9 PB01 E 2014-05-01T07:59:59.998393Z 2014-05-01T08:59:59.998393Z\n", 579 | "9 PB02 Z 2014-05-01T07:59:59.998393Z 2014-05-01T08:59:59.998393Z\n", 580 | "9 PB02 E 2014-05-01T07:59:59.998393Z 2014-05-01T08:59:59.998393Z\n", 581 | "10 PB01 Z 2014-05-01T08:59:59.998393Z 2014-05-01T09:59:59.998393Z\n", 582 | "10 PB01 E 2014-05-01T08:59:59.998393Z 2014-05-01T09:59:59.998393Z\n", 583 | "10 PB02 Z 2014-05-01T08:59:59.998393Z 2014-05-01T09:59:59.998393Z\n", 584 | "10 PB02 E 2014-05-01T08:59:59.998393Z 2014-05-01T09:59:59.998393Z\n", 585 | "11 PB01 Z 2014-05-01T09:59:59.998393Z 2014-05-01T10:59:59.998393Z\n", 586 | "11 PB01 E 2014-05-01T09:59:59.998393Z 2014-05-01T10:59:59.998393Z\n", 587 | "11 PB02 Z 2014-05-01T09:59:59.998393Z 2014-05-01T10:59:59.998393Z\n", 588 | "11 PB02 E 2014-05-01T09:59:59.998393Z 2014-05-01T10:59:59.998393Z\n", 589 | "12 PB01 Z 2014-05-01T10:59:59.998393Z 2014-05-01T11:59:59.998393Z\n", 590 | "12 PB01 E 2014-05-01T10:59:59.998393Z 2014-05-01T11:59:59.998393Z\n", 591 | "12 PB02 Z 2014-05-01T10:59:59.998393Z 2014-05-01T11:59:59.998393Z\n", 592 | "12 PB02 E 2014-05-01T10:59:59.998393Z 2014-05-01T11:59:59.998393Z\n" 593 | ] 594 | } 595 | ], 596 | "source": [ 597 | "# plots\n", 598 | "# util.plot_predicted_phases(dpp_config, dpp_data, dpp_model)\n", 599 | "util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_probs=['P','S'], shift_probs=True)" 600 | ] 601 | } 602 | ], 603 | "metadata": { 604 | "jupytext": { 605 | "text_representation": { 606 | "extension": ".py", 607 | "format_name": "light", 608 | "format_version": "1.4", 609 | "jupytext_version": "1.2.4" 610 | } 611 | }, 612 | "kernelspec": { 613 | "display_name": "Python 3", 614 | "language": "python", 615 | "name": "python3" 616 | }, 617 | "language_info": { 618 | "codemirror_mode": { 619 | "name": "ipython", 620 | "version": 3 621 | }, 622 | "file_extension": ".py", 623 | "mimetype": "text/x-python", 624 | "name": "python", 625 | "nbconvert_exporter": "python", 626 | "pygments_lexer": "ipython3", 627 | "version": "3.6.13" 628 | } 629 | }, 630 | "nbformat": 4, 631 | "nbformat_minor": 2 632 | } 633 | -------------------------------------------------------------------------------- /examples/run_dpp_from_archive.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # 3 | # This script applies DeepPhasePick on seismic data stored in a archive directory structured as: archive/YY/NET/STA/CH 4 | # Here YY is year, NET is the network code, STA is the station code and CH is the channel code (e.g., HH) of the seismic streams. 5 | # 6 | # Author: Hugo Soto Parada (June, 2021) 7 | # Contact: soto@gfz-potsdam.de, hugosotoparada@gmail.com 8 | # 9 | ######################################################################################################################################## 10 | 11 | import os 12 | import config, data, model, util 13 | 14 | # 1. Configure DPP 15 | # 16 | # config 17 | util.init_session() 18 | dpp_config = config.Config() 19 | dpp_config.set_trigger(pthres_p=[0.9, 0.001], pthres_s=[0.9, 0.001]) 20 | # dpp_config.set_picking(mcd_iter=10, run_mcd=True) 21 | dpp_config.set_picking(run_mcd=False) 22 | # 23 | dpp_config.set_data( 24 | stas=['PB01', 'PB02'], 25 | net='CX', 26 | ch='HH', 27 | archive='sample_data/archive', 28 | opath='out_archive' 29 | ) 30 | dpp_config.set_time( 31 | dt_iter=3600., 32 | tstart="2014-05-01T00:00:00", 33 | tend="2014-05-01T12:00:00", 34 | ) 35 | 36 | # 2. Read seismic data into DPP 37 | # 38 | # data 39 | dpp_data = data.Data() 40 | dpp_data.read_from_archive(dpp_config) 41 | # 42 | for k in dpp_data.data: 43 | print(k, dpp_data.data[k]) 44 | 45 | # 3. Run phase detection and picking 46 | # 47 | # model 48 | dpp_model = model.Model(verbose=False) 49 | # dpp_model = model.Model(verbose=False, version_pick_P="20201002_2", version_pick_S="20201002_2") 50 | # 51 | print(dpp_model.model_detection['best_model'].summary()) 52 | print(dpp_model.model_picking_P['best_model'].summary()) 53 | print(dpp_model.model_picking_S['best_model'].summary()) 54 | # 55 | # run phase detection 56 | dpp_model.run_detection(dpp_config, dpp_data, save_dets=False, save_data=False) 57 | # 58 | # run phase picking 59 | dpp_model.run_picking(dpp_config, dpp_data, save_plots=False, save_stats=True, save_picks=False) 60 | 61 | # 4. Plot predicted phases 62 | # 63 | # plots 64 | # util.plot_predicted_phases(dpp_config, dpp_data, dpp_model) 65 | util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_probs=['P','S'], shift_probs=True) 66 | 67 | -------------------------------------------------------------------------------- /examples/run_dpp_from_directory.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # 3 | # This script applies DeepPhasePick on seismic data stored in an unstructured archive directory. 4 | # 5 | # Author: Hugo Soto Parada (June, 2021) 6 | # Contact: soto@gfz-potsdam.de, hugosotoparada@gmail.com 7 | # 8 | ######################################################################################################################################## 9 | 10 | import os 11 | import config, data, model, util 12 | 13 | # 1. Configure DPP 14 | # 15 | # config 16 | util.init_session() 17 | dpp_config = config.Config() 18 | dpp_config.set_trigger(pthres_p=[0.9, 0.001], pthres_s=[0.9, 0.001]) 19 | dpp_config.set_picking(mcd_iter=10, run_mcd=True) 20 | # dpp_config.set_picking(run_mcd=False) 21 | # 22 | dpp_config.set_data( 23 | stas=['PB01', 'PB02'], 24 | net='CX', 25 | ch='HH', 26 | archive='sample_data/CX_20140301', 27 | opath='out_CX_20140301' 28 | ) 29 | dpp_config.set_time( 30 | dt_iter=3600., 31 | tstart="2014-03-01T02:00:00", 32 | tend="2014-03-01T03:00:00", 33 | ) 34 | 35 | # 2. Read seismic data into DPP 36 | # 37 | # data 38 | dpp_data = data.Data() 39 | dpp_data.read_from_directory(dpp_config) 40 | # 41 | # for k in dpp_data.data: 42 | # print(k, dpp_data.data[k]) 43 | 44 | # 3. Run phase detection and picking 45 | # 46 | # model 47 | # dpp_model = model.Model(verbose=False) 48 | dpp_model = model.Model(verbose=False, version_pick_P="20201002_2", version_pick_S="20201002_2") 49 | # 50 | print(dpp_model.model_detection['best_model'].summary()) 51 | print(dpp_model.model_picking_P['best_model'].summary()) 52 | print(dpp_model.model_picking_S['best_model'].summary()) 53 | # 54 | # run phase detection 55 | dpp_model.run_detection(dpp_config, dpp_data, save_dets=False, save_data=False) 56 | # 57 | # run phase picking 58 | dpp_model.run_picking(dpp_config, dpp_data, save_plots=True, save_stats=True, save_picks=False) 59 | 60 | # 4. Plot predicted phases 61 | # 62 | # plots 63 | util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_comps=['Z','N']) 64 | # util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_probs=['P','S'], shift_probs=True) 65 | 66 | -------------------------------------------------------------------------------- /models/detection/20201002/dict_hyperopt_t733.pckl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hsotoparada/DeepPhasePick/95a30f8acf6700f9a2a5738c248149903a152d1c/models/detection/20201002/dict_hyperopt_t733.pckl -------------------------------------------------------------------------------- /models/detection/20201002/model_hyperopt_t733.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hsotoparada/DeepPhasePick/95a30f8acf6700f9a2a5738c248149903a152d1c/models/detection/20201002/model_hyperopt_t733.h5 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17 | import pickle 18 | 19 | 20 | def export_dict2pckl(dct, opath): 21 | """ 22 | Exports dictionary as pickle file. 23 | 24 | Parameters 25 | ---------- 26 | dct: dict 27 | Input dictionary. 28 | opath: str 29 | Output path to export pickle file. 30 | """ 31 | with open(opath, 'wb') as pout: 32 | pickle.dump(dct, pout) 33 | 34 | 35 | def import_pckl2dict(ipath): 36 | """ 37 | Imports pickle file to dictionary and returns this dictionary. 38 | 39 | Parameters 40 | ---------- 41 | ipath: str 42 | Path to pickle file. 43 | """ 44 | with open(ipath, 'rb') as pin: 45 | dct = pickle.load(pin) 46 | return dct 47 | 48 | 49 | def init_session(): 50 | """ 51 | Sets up tensorflow v2.x / keras session. 52 | """ 53 | # 54 | physical_devices = tf.config.list_physical_devices('GPU') 55 | tf.config.experimental.set_memory_growth(physical_devices[0], enable=True) 56 | # 57 | # remove previously generated files or directories 58 | dirs_remove = ['__pycache__/', '~/.nv/'] 59 | for dir_remove in dirs_remove: 60 | try: 61 | shutil.rmtree(dir_remove) 62 | print(f"{dir_remove} removed") 63 | except FileNotFoundError: 64 | print(f"{dir_remove} not found, continuing...") 65 | pass 66 | 67 | 68 | def get_arg_best_trial(trials): 69 | """ 70 | Returns index of best trial (trial at which loss in minimum). 71 | 72 | Parameters 73 | ---------- 74 | trials: list 75 | List of hyperopt trials results in hyperparameter optimization. 76 | 77 | Returns 78 | ------- 79 | arg_min_loss: int 80 | Index corresponding to best trial (trial at which loss in minimum) in trials object. 81 | """ 82 | losses = [float(trial['result']['loss']) for trial in trials] 83 | arg_min_loss = np.argmin(losses) 84 | return arg_min_loss 85 | 86 | 87 | def plot_predicted_phase_P(config, dct_mcd, data, sta, opath, plot_num): 88 | """ 89 | Creates plots for predicted P-phase time onsets. 90 | Two types of plots are created, showing: 91 | i) refined phase pick in picking window, ii) zoom centered on refined phase pick and Monte Carlo Dropout (MCD) results. 92 | 93 | Parameters 94 | ---------- 95 | config: instance of config.Config 96 | Contains user configuration of seismic waveform data and how this data is processed in DeepPhasePick. 97 | dct_mcd: dict 98 | Dictionary containing MCD statistics of the predicted phase pick. 99 | data: ndarray 100 | 3D array containing seismic stream amplitudes on which MCD is applied. 101 | sta: str 102 | Station code of seismic stream. 103 | opath: str 104 | Output path for saving figure of predicted phase onsets. 105 | plot_num: int 106 | Index of processed phase onset, used for figure names of predicted phase onsets. 107 | """ 108 | # 109 | mpl.rcParams['xtick.major.size'] = 8 110 | mpl.rcParams['xtick.major.width'] = 1.5 111 | mpl.rcParams['xtick.minor.size'] = 4 112 | mpl.rcParams['xtick.minor.width'] = 1.5 113 | mpl.rcParams['ytick.major.size'] = 8 114 | mpl.rcParams['ytick.major.width'] = 1.5 115 | mpl.rcParams['ytick.minor.size'] = 4 116 | mpl.rcParams['ytick.minor.width'] = 1.5 117 | mpl.rcParams['xtick.labelsize'] = 14 118 | mpl.rcParams['ytick.labelsize'] = 14 119 | mpl.rcParams['axes.titlesize'] = 14 120 | mpl.rcParams['axes.labelsize'] = 14 121 | # 122 | opath_fig = f"{opath}/pick_plots" 123 | os.makedirs(opath_fig, exist_ok=True) 124 | # 125 | tpick_det = dct_mcd['pick']['tpick_det'] 126 | tpick_pred = dct_mcd['pick']['tpick'] 127 | tpick_pred_th1 = dct_mcd['pick']['tpick_th1'] 128 | tpick_pred_th2 = dct_mcd['pick']['tpick_th2'] 129 | terr_pre = dct_mcd['pick']['terr_pre'] 130 | terr_pos = dct_mcd['pick']['terr_pos'] 131 | pick_class = dct_mcd['pick']['pick_class'] 132 | mc_pred = dct_mcd['mcd']['mc_pred'] 133 | mc_pred_mean = dct_mcd['mcd']['mc_pred_mean'] 134 | mc_pred_mean_arg_pick = dct_mcd['mcd']['mc_pred_mean_arg_pick'] 135 | mc_pred_std_pick = dct_mcd['mcd']['mc_pred_std_pick'] 136 | prob_th1 = dct_mcd['mcd']['prob_th1'] 137 | prob_th2 = dct_mcd['mcd']['prob_th2'] 138 | # 139 | # plot - phase window input for RNN 140 | # 141 | fig = plt.figure(figsize=(7*1, 3*1)) 142 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 143 | ax = [] 144 | ax.append(fig.add_subplot(1, 1, 1)) 145 | # 146 | # plot trace 147 | tr_win_y = data[0,:,0] 148 | tr_win_x = np.arange(tr_win_y.shape[0]) / config.data_params['samp_freq'] 149 | # 150 | ax[-1].plot(tr_win_x, tr_win_y, c='gray', lw=1.) 151 | ax[-1].vlines(x=tpick_pred, ymin=-1.1, ymax=1., color='r', lw=1.5, ls='-', clip_on=False) 152 | ax[-1].vlines(x=tpick_det, ymin=-1., ymax=1.1, color='r', lw=1.5, ls='--', clip_on=False) 153 | # tr_label_1 = f"comp Z" 154 | # ax[-1].text(0.02, .95, tr_label_1, size=12., ha='left', va='center', transform=ax[-1].transAxes) 155 | # 156 | xmin = 0. 157 | xmax = tr_win_x.max() 158 | ax[-1].set_xlim([xmin, xmax]) 159 | ax[-1].xaxis.set_ticks(np.arange(xmin, xmax + .1, .5)) 160 | ax[-1].xaxis.set_minor_locator(ticker.MultipleLocator(.1)) 161 | ax[-1].set_ylim([-1., 1.]) 162 | ax[-1].set_xlabel(f"Time [s]") 163 | # 164 | plt.tight_layout() 165 | print(f"plotting predicted phase P: {opath_fig}/{sta}_P_{plot_num+1:02}.png") 166 | ofig = f"{opath_fig}/{sta}_P_Z_{plot_num+1:02}" 167 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 168 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 169 | plt.close() 170 | # 171 | # plot - phase window input for RNN (zoom around predicted time pick and MCD results) 172 | # 173 | fig = plt.figure(figsize=(7*1, 3*1)) 174 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 175 | ax = [] 176 | ax.append(fig.add_subplot(1, 1, 1)) 177 | # 178 | # plot trace 179 | ax[-1].plot(tr_win_x, tr_win_y, c='gray', lw=2., zorder=1) 180 | # 181 | # plot output binary probs 182 | ax_tmp = ax[-1].twinx() 183 | for l in range(len(mc_pred)): 184 | ax_tmp.plot(tr_win_x, mc_pred[l,:,0], c='magenta', lw=.2, ls='--', zorder=1) 185 | ax_tmp.plot(tr_win_x, mc_pred_mean[:,0], c='magenta', lw=1., zorder=1) 186 | ax_tmp.set_ylim([0., 1.]) 187 | ax_tmp.set_ylabel("Probability") 188 | ax_tmp.yaxis.set_ticks(np.arange(0.,1.1,.1)[:]) 189 | ax_tmp.yaxis.set_minor_locator(ticker.MultipleLocator(.05)) 190 | ax_tmp.axhline(mc_pred_mean[mc_pred_mean_arg_pick,0], c='magenta', lw=1., ls='--', zorder=2) 191 | ax_tmp.axhline(prob_th1, c='magenta', lw=1., ls='--', zorder=2) 192 | ax_tmp.axhline(prob_th2, c='magenta', lw=1., ls='--', zorder=2) 193 | # 194 | ax[-1].vlines(x=tpick_pred, ymin=-1.1, ymax=1., color='r', lw=1.5, ls='-', clip_on=False, zorder=3) 195 | ax[-1].vlines(x=tpick_det, ymin=-1., ymax=1.1, color='r', lw=1.5, ls='--', clip_on=False, zorder=3) 196 | ax[-1].vlines(x=tpick_pred_th1, ymin=-1., ymax=1., color='r', lw=1.5, ls=':', clip_on=False, zorder=3) 197 | ax[-1].vlines(x=tpick_pred_th2, ymin=-1., ymax=1., color='r', lw=1.5, ls=':', clip_on=False, zorder=3) 198 | # ax[-1].vlines(x=tpick_pred-tpick_pred_std, ymin=-1., ymax=1.05, color='r', lw=1.5, ls='--', clip_on=False) 199 | # ax[-1].vlines(x=tpick_pred+tpick_pred_std, ymin=-1., ymax=1.05, color='r', lw=1.5, ls='--', clip_on=False) 200 | # arg_pred = mc_pred_mean_arg_pick 201 | tr_label_1 = f"tpred = {tpick_pred:.3f}" 202 | tr_label_2 = f"terr(1 x pb_std) = (-{terr_pre:.3f}, +{terr_pos:.3f})" 203 | tr_label_3 = f"pick_class = {pick_class}" 204 | tr_label_4 = f"pb, pb_std = ({mc_pred_mean[mc_pred_mean_arg_pick,0]:.3f}, {mc_pred_std_pick:.3f})" 205 | # ax[-1].text(0.01, .975, tr_label_1, size=12., ha='left', va='center', transform=ax[-1].transAxes) 206 | # ax[-1].text(0.01, .935, tr_label_2, size=12., ha='left', va='center', transform=ax[-1].transAxes) 207 | # ax[-1].text(0.01, .895, tr_label_3, size=12., ha='left', va='center', transform=ax[-1].transAxes) 208 | # ax[-1].text(0.01, .855, tr_label_4, size=12., ha='left', va='center', transform=ax[-1].transAxes) 209 | # 210 | xmin = tpick_pred - .5 211 | xmax = tpick_pred + .5 212 | ax[-1].set_xlim([xmin, xmax]) 213 | tick_major = np.arange(xmin, xmax + .1, .1) 214 | tick_minor = np.arange(xmin, xmax + .01, .02) 215 | ax[-1].xaxis.set_major_locator(ticker.FixedLocator(tick_major)) 216 | ax[-1].xaxis.set_minor_locator(ticker.FixedLocator(tick_minor)) 217 | ax[-1].set_ylim([-1., 1.]) 218 | ax[-1].set_xlabel("Time [s]") 219 | # 220 | plt.tight_layout() 221 | print(f"plotting predicted phase P: {opath_fig}/{sta}_P_mc_{plot_num+1:02}.png") 222 | print(tr_label_1) 223 | print(tr_label_2) 224 | print(tr_label_3) 225 | print(tr_label_4) 226 | ofig = f"{opath_fig}/{sta}_P_Z_mcd_{plot_num+1:02}" 227 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 228 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 229 | plt.close() 230 | 231 | 232 | def plot_predicted_phase_S(config, dct_mcd, data, sta, opath, plot_num): 233 | """ 234 | Creates plots for predicted S-phase time onsets. 235 | Two types of plots are created, showing: 236 | i) refined phase pick in picking window, ii) zoom centered on refined phase pick and Monte Carlo Dropout (MCD) results. 237 | 238 | Parameters 239 | ---------- 240 | config: instance of config.Config 241 | Contains user configuration of seismic waveform data and how this data is processed in DeepPhasePick. 242 | dct_mcd: dict 243 | Dictionary containing MCD statistics of the predicted phase pick. 244 | data: ndarray 245 | 3D array containing seismic stream amplitudes on which MCD is applied. 246 | sta: str 247 | Station code of seismic stream. 248 | opath: str 249 | Output path for saving figure of predicted phase onsets. 250 | plot_num: int 251 | Index of processed phase onset, used for figure names of predicted phase onsets. 252 | """ 253 | # 254 | mpl.rcParams['xtick.major.size'] = 8 255 | mpl.rcParams['xtick.major.width'] = 1.5 256 | mpl.rcParams['xtick.minor.size'] = 4 257 | mpl.rcParams['xtick.minor.width'] = 1.5 258 | mpl.rcParams['ytick.major.size'] = 8 259 | mpl.rcParams['ytick.major.width'] = 1.5 260 | mpl.rcParams['ytick.minor.size'] = 4 261 | mpl.rcParams['ytick.minor.width'] = 1.5 262 | mpl.rcParams['xtick.labelsize'] = 14 263 | mpl.rcParams['ytick.labelsize'] = 14 264 | mpl.rcParams['axes.titlesize'] = 14 265 | mpl.rcParams['axes.labelsize'] = 14 266 | # 267 | opath_fig = f"{opath}/pick_plots" 268 | os.makedirs(opath_fig, exist_ok=True) 269 | # 270 | tpick_det = dct_mcd['pick']['tpick_det'] 271 | tpick_pred = dct_mcd['pick']['tpick'] 272 | tpick_pred_th1 = dct_mcd['pick']['tpick_th1'] 273 | tpick_pred_th2 = dct_mcd['pick']['tpick_th2'] 274 | terr_pre = dct_mcd['pick']['terr_pre'] 275 | terr_pos = dct_mcd['pick']['terr_pos'] 276 | pick_class = dct_mcd['pick']['pick_class'] 277 | mc_pred = dct_mcd['mcd']['mc_pred'] 278 | mc_pred_mean = dct_mcd['mcd']['mc_pred_mean'] 279 | mc_pred_mean_arg_pick = dct_mcd['mcd']['mc_pred_mean_arg_pick'] 280 | mc_pred_std_pick = dct_mcd['mcd']['mc_pred_std_pick'] 281 | prob_th1 = dct_mcd['mcd']['prob_th1'] 282 | prob_th2 = dct_mcd['mcd']['prob_th2'] 283 | # 284 | # plot - phase window input for RNN (comp E) 285 | # 286 | fig = plt.figure(figsize=(7*1, 3*1)) 287 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 288 | ax = [] 289 | ax.append(fig.add_subplot(1, 1, 1)) 290 | # 291 | # plot trace 292 | tr_win_y = data[0,:,0] 293 | tr_win_x = np.arange(tr_win_y.shape[0]) / config.data_params['samp_freq'] 294 | # 295 | ax[-1].plot(tr_win_x, tr_win_y, c='gray', lw=1.) 296 | ax[-1].vlines(x=tpick_pred, ymin=-1.1, ymax=1., color='b', lw=1.5, ls='-', clip_on=False) 297 | ax[-1].vlines(x=tpick_det, ymin=-1., ymax=1.1, color='b', lw=1.5, ls='--', clip_on=False) 298 | # tr_label_1 = f"comp E" 299 | # ax[-1].text(0.02, .95, tr_label_1, size=12., ha='left', va='center', transform=ax[-1].transAxes) 300 | # 301 | xmin = 0. 302 | xmax = tr_win_x.max() 303 | ax[-1].set_xlim([xmin, xmax]) 304 | ax[-1].xaxis.set_ticks(np.arange(xmin, xmax + .1, .5)) 305 | ax[-1].xaxis.set_minor_locator(ticker.MultipleLocator(.1)) 306 | ax[-1].set_ylim([-1., 1.]) 307 | ax[-1].set_xlabel("Time [s]") 308 | # 309 | plt.tight_layout() 310 | print(f"plotting predicted phase S: {opath_fig}/{sta}_S_E_{plot_num+1:02}.png") 311 | ofig = f"{opath_fig}/{sta}_S_E_{plot_num+1:02}" 312 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 313 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 314 | plt.close() 315 | # 316 | # plot - phase window input for RNN (comp N) 317 | # 318 | fig = plt.figure(figsize=(7*1, 3*1)) 319 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 320 | ax = [] 321 | ax.append(fig.add_subplot(1, 1, 1)) 322 | # 323 | # plot trace 324 | tr_win_y = data[0,:,1] 325 | tr_win_x = np.arange(tr_win_y.shape[0]) / config.data_params['samp_freq'] 326 | # 327 | ax[-1].plot(tr_win_x, tr_win_y, c='gray', lw=1.) 328 | ax[-1].vlines(x=tpick_pred, ymin=-1.1, ymax=1., color='b', lw=1.5, ls='-', clip_on=False) 329 | ax[-1].vlines(x=tpick_det, ymin=-1., ymax=1.1, color='b', lw=1.5, ls='--', clip_on=False) 330 | # tr_label_1 = f"comp N" 331 | # ax[-1].text(0.02, .95, tr_label_1, size=12., ha='left', va='center', transform=ax[-1].transAxes) 332 | # 333 | xmin = 0. 334 | xmax = tr_win_x.max() 335 | ax[-1].set_xlim([xmin, xmax]) 336 | ax[-1].xaxis.set_ticks(np.arange(xmin, xmax + .1, .5)) 337 | ax[-1].xaxis.set_minor_locator(ticker.MultipleLocator(.1)) 338 | ax[-1].set_ylim([-1., 1.]) 339 | ax[-1].set_xlabel("Time [s]") 340 | # 341 | plt.tight_layout() 342 | print(f"plotting predicted phase S: {opath_fig}/{sta}_S_N_{plot_num+1:02}.png") 343 | ofig = f"{opath_fig}/{sta}_S_N_{plot_num+1:02}" 344 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 345 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 346 | plt.close() 347 | # 348 | # plot - phase window input for RNN (zoom around predicted time pick and MCD results, comp E) 349 | # 350 | fig = plt.figure(figsize=(7*1, 3*1)) 351 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 352 | ax = [] 353 | ax.append(fig.add_subplot(1, 1, 1)) 354 | # 355 | # plot trace + label 356 | tr_win_y = data[0,:,0] 357 | tr_win_x = np.arange(tr_win_y.shape[0]) / config.data_params['samp_freq'] 358 | ax[-1].plot(tr_win_x, tr_win_y, c='gray', lw=2.) 359 | # 360 | # plot output binary probs 361 | ax_tmp = ax[-1].twinx() 362 | for l in range(len(mc_pred)): 363 | ax_tmp.plot(tr_win_x, mc_pred[l,:,0], c='magenta', lw=.2, ls='--') 364 | ax_tmp.plot(tr_win_x, mc_pred_mean[:,0], c='magenta', lw=1.) 365 | ax_tmp.set_ylim([0., 1.]) 366 | ax_tmp.set_ylabel("Probability") 367 | ax_tmp.yaxis.set_ticks(np.arange(0.,1.1,.1)[:]) 368 | ax_tmp.yaxis.set_minor_locator(ticker.MultipleLocator(.05)) 369 | ax_tmp.axhline(mc_pred_mean[mc_pred_mean_arg_pick,0], c='magenta', lw=1., ls='--') 370 | ax_tmp.axhline(prob_th1, c='magenta', lw=1., ls='--') 371 | ax_tmp.axhline(prob_th2, c='magenta', lw=1., ls='--') 372 | # 373 | ax[-1].vlines(x=tpick_pred, ymin=-1.1, ymax=1., color='b', lw=1.5, ls='-', clip_on=False) 374 | ax[-1].vlines(x=tpick_det, ymin=-1., ymax=1.1, color='b', lw=1.5, ls='--', clip_on=False) 375 | ax[-1].vlines(x=tpick_pred_th1, ymin=-1., ymax=1., color='b', lw=1.5, ls=':', clip_on=False) 376 | ax[-1].vlines(x=tpick_pred_th2, ymin=-1., ymax=1., color='b', lw=1.5, ls=':', clip_on=False) 377 | # ax[-1].vlines(x=tpick_pred-tpick_pred_std, ymin=-1., ymax=1.05, color='r', lw=1.5, ls='--', clip_on=False) 378 | # ax[-1].vlines(x=tpick_pred+tpick_pred_std, ymin=-1., ymax=1.05, color='r', lw=1.5, ls='--', clip_on=False) 379 | # arg_pred = mc_pred_mean_arg_pick 380 | tr_label_1 = f"tpred = {tpick_pred:.3f}" 381 | tr_label_2 = f"terr(1 x pb_std) = (-{terr_pre:.3f}, +{terr_pos:.3f})" 382 | tr_label_3 = f"pick_class = {pick_class}" 383 | tr_label_4 = f"pb, pb_std = ({mc_pred_mean[mc_pred_mean_arg_pick,0]:.3f}, {mc_pred_std_pick:.3f})" 384 | # ax[-1].text(0.01, .975, tr_label_1, size=12., ha='left', va='center', transform=ax[-1].transAxes) 385 | # ax[-1].text(0.01, .935, tr_label_2, size=12., ha='left', va='center', transform=ax[-1].transAxes) 386 | # ax[-1].text(0.01, .895, tr_label_3, size=12., ha='left', va='center', transform=ax[-1].transAxes) 387 | # ax[-1].text(0.01, .855, tr_label_4, size=12., ha='left', va='center', transform=ax[-1].transAxes) 388 | # 389 | xmin = tpick_pred - .5 390 | xmax = tpick_pred + .5 391 | ax[-1].set_xlim([xmin, xmax]) 392 | tick_major = np.arange(xmin, xmax + .1, .1) 393 | tick_minor = np.arange(xmin, xmax + .01, .02) 394 | ax[-1].xaxis.set_major_locator(ticker.FixedLocator(tick_major)) 395 | ax[-1].xaxis.set_minor_locator(ticker.FixedLocator(tick_minor)) 396 | ax[-1].set_ylim([-1., 1.]) 397 | ax[-1].set_xlabel("Time [s]") 398 | # 399 | plt.tight_layout() 400 | print(f"plotting predicted phase S: {opath_fig}/{sta}_S_E_mc_{plot_num+1:02}.png") 401 | ofig = f"{opath_fig}/{sta}_S_E_mcd_{plot_num+1:02}" 402 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 403 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 404 | plt.close() 405 | # 406 | # plot - phase window input for RNN (zoom around predicted time pick and MCD results, comp N) 407 | # 408 | fig = plt.figure(figsize=(7*1, 3*1)) 409 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 410 | ax = [] 411 | ax.append(fig.add_subplot(1, 1, 1)) 412 | # 413 | # plot trace + label 414 | tr_win_y = data[0,:,1] 415 | tr_win_x = np.arange(tr_win_y.shape[0]) / config.data_params['samp_freq'] 416 | ax[-1].plot(tr_win_x, tr_win_y, c='gray', lw=2.) 417 | # 418 | # plot output binary probs 419 | ax_tmp = ax[-1].twinx() 420 | for l in range(len(mc_pred)): 421 | ax_tmp.plot(tr_win_x, mc_pred[l,:,0], c='magenta', lw=.2, ls='--') 422 | ax_tmp.plot(tr_win_x, mc_pred_mean[:,0], c='magenta', lw=1.) 423 | ax_tmp.set_ylim([0., 1.]) 424 | ax_tmp.set_ylabel("Probability") 425 | ax_tmp.yaxis.set_ticks(np.arange(0.,1.1,.1)[:]) 426 | ax_tmp.yaxis.set_minor_locator(ticker.MultipleLocator(.05)) 427 | ax_tmp.axhline(mc_pred_mean[mc_pred_mean_arg_pick,0], c='magenta', lw=1., ls='--') 428 | ax_tmp.axhline(prob_th1, c='magenta', lw=1., ls='--') 429 | ax_tmp.axhline(prob_th2, c='magenta', lw=1., ls='--') 430 | # 431 | ax[-1].vlines(x=tpick_pred, ymin=-1.1, ymax=1., color='b', lw=1.5, ls='-', clip_on=False) 432 | ax[-1].vlines(x=tpick_det, ymin=-1., ymax=1.1, color='b', lw=1.5, ls='--', clip_on=False) 433 | ax[-1].vlines(x=tpick_pred_th1, ymin=-1., ymax=1., color='b', lw=1.5, ls=':', clip_on=False) 434 | ax[-1].vlines(x=tpick_pred_th2, ymin=-1., ymax=1., color='b', lw=1.5, ls=':', clip_on=False) 435 | # ax[-1].vlines(x=tpick_pred-tpick_pred_std, ymin=-1., ymax=1.05, color='r', lw=1.5, ls='--', clip_on=False) 436 | # ax[-1].vlines(x=tpick_pred+tpick_pred_std, ymin=-1., ymax=1.05, color='r', lw=1.5, ls='--', clip_on=False) 437 | # ax[-1].text(0.02, .975, tr_label_1, size=10., ha='left', va='center', transform=ax[-1].transAxes) 438 | # ax[-1].text(0.02, .935, tr_label_2, size=10., ha='left', va='center', transform=ax[-1].transAxes) 439 | # ax[-1].text(0.02, .895, tr_label_3, size=10., ha='left', va='center', transform=ax[-1].transAxes) 440 | # ax[-1].text(0.02, .855, tr_label_4, size=10., ha='left', va='center', transform=ax[-1].transAxes) 441 | # 442 | xmin = tpick_pred - .5 443 | xmax = tpick_pred + .5 444 | ax[-1].set_xlim([xmin, xmax]) 445 | tick_major = np.arange(xmin, xmax + .1, .1) 446 | tick_minor = np.arange(xmin, xmax + .01, .02) 447 | ax[-1].xaxis.set_major_locator(ticker.FixedLocator(tick_major)) 448 | ax[-1].xaxis.set_minor_locator(ticker.FixedLocator(tick_minor)) 449 | ax[-1].set_ylim([-1., 1.]) 450 | ax[-1].set_xlabel("Time [s]") 451 | # 452 | plt.tight_layout() 453 | print(f"plotting predicted phase S: {opath_fig}/{sta}_S_N_mc_{plot_num+1:02}.png") 454 | print(tr_label_1) 455 | print(tr_label_2) 456 | print(tr_label_3) 457 | print(tr_label_4) 458 | ofig = f"{opath_fig}/{sta}_S_N_mcd_{plot_num+1:02}" 459 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 460 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 461 | plt.close() 462 | 463 | 464 | def plot_predicted_phases(config, data, model, plot_comps=['Z','E'], plot_probs=[], shift_probs=True): 465 | """ 466 | Plots predicted P- and S-phase picks on seismic waveforms and additionally predicted discrete class probability time series. 467 | 468 | Parameters 469 | ---------- 470 | config: instance of config.Config 471 | Contains user configuration of seismic waveform data and how this data is processed in DeepPhasePick. 472 | data: instance of data.Data 473 | Contains selected seismic waveform data on which phase detection is applied. 474 | model: instance of model.Model 475 | Contains best models and relevant results obtained from hyperparameter optimization for phase detection and picking. 476 | plot_comps: list of str, optional 477 | Seismic components to be plotted. It can be any of vertical ('Z'), east ('E'), and north ('N'). 478 | By default vertical and east components are plotted. 479 | plot_probs: list of str, optional 480 | Discrete class probability time series to be plotted. It can be any of 'P', 'S' and 'N' (Noise) classes. 481 | By default no probability time series are plotted. 482 | shift_probs: bool, optional. 483 | If True (default), plotted probability time series are shifted in time according to the optimized hyperparameters defining the picking window for each class. 484 | See Figure S1 in Soto and Schurr (2020). 485 | """ 486 | # 487 | # plot format parameters 488 | mpl.rcParams['xtick.major.size'] = 10 489 | mpl.rcParams['xtick.major.width'] = 2 490 | mpl.rcParams['xtick.minor.size'] = 5 491 | mpl.rcParams['xtick.minor.width'] = 2 492 | mpl.rcParams['ytick.major.size'] = 10 493 | mpl.rcParams['ytick.major.width'] = 2 494 | mpl.rcParams['ytick.minor.size'] = 4 495 | mpl.rcParams['ytick.minor.width'] = 2 496 | mpl.rcParams['xtick.labelsize'] = 16 497 | mpl.rcParams['ytick.labelsize'] = 16 498 | mpl.rcParams['axes.titlesize'] = 16 499 | mpl.rcParams['axes.labelsize'] = 16 500 | # 501 | best_params = model.model_detection['best_params'] 502 | add_rows = 0 503 | if len(plot_probs) > 0: 504 | add_rows += 1 505 | # 506 | print("creating plots...") 507 | for i in data.data: 508 | # 509 | for sta in data.data[i]['st']: 510 | # 511 | fig = plt.figure(figsize=(12., 4*(len(plot_comps)+add_rows))) 512 | plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0) 513 | # 514 | for n, ch in enumerate(plot_comps): 515 | # 516 | ax = [] 517 | # 518 | # subplot - waveform trace (input for CNN) 519 | # 520 | tr = data.data[i]['st'][sta].select(channel='*'+ch)[0] 521 | dt = tr.stats.delta 522 | tr_y = tr.data 523 | y_max = np.abs(tr.data).max() 524 | tr_y /= y_max 525 | tr_x = np.arange(tr.data.size) * dt 526 | # 527 | # plot trace 528 | ax.append(fig.add_subplot(len(plot_comps)+add_rows, 1, n+1)) 529 | ax[-1].plot(tr_x, tr_y, c='gray', lw=.2) 530 | # ax[-1].plot(tr_x, tr_y, c='k', lw=.2) 531 | # 532 | # retrieve predicted P, S class probability time series 533 | # 534 | samp_dt = 1 / config.data_params['samp_freq'] 535 | if shift_probs: 536 | tp_shift = (best_params['frac_dsamp_p1']-.5) * best_params['win_size'] * samp_dt 537 | ts_shift = (best_params['frac_dsamp_s1']-.5) * best_params['win_size'] * samp_dt 538 | tn_shift = (best_params['frac_dsamp_n1']-.5) * best_params['win_size'] * samp_dt 539 | else: 540 | tp_shift = 0 541 | ts_shift = 0 542 | tn_shift = 0 543 | # 544 | # plot trace label 545 | # tr_label = f"{tr.stats.network}.{tr.stats.station}.{tr.stats.channel}" 546 | tr_label = f"{tr.stats.channel}" 547 | box_label = dict(boxstyle='square', facecolor='white', alpha=.9) 548 | ax[-1].text(0.02, .95, tr_label, size=14., ha='left', va='center', transform=ax[-1].transAxes, bbox=box_label) 549 | # 550 | tstart_plot = tr.stats.starttime 551 | tend_plot = tr.stats.endtime 552 | print(i, sta, ch, tstart_plot, tend_plot) 553 | # 554 | # lines at predicted picks 555 | # 556 | if sta in model.picks[i]: 557 | # 558 | for ii, k in enumerate(model.picks[i][sta]['P']['true_arg']): 559 | # 560 | # P pick corrected after phase picking 561 | # 562 | tstart_win = model.picks[i][sta]['P']['twd'][k]['tstart_win'] 563 | tend_win = model.picks[i][sta]['P']['twd'][k]['tend_win'] 564 | if config.picking['run_mcd']: 565 | tpick_pred = model.picks[i][sta]['P']['twd'][k]['pick_ml']['tpick'] 566 | # tpick_th1 = model.picks[i][sta]['P']['twd'][k]['pick_ml']['tpick_th1'] 567 | # tpick_th2 = model.picks[i][sta]['P']['twd'][k]['pick_ml']['tpick_th2'] 568 | # pick_class = model.picks[i][sta]['P']['twd'][k]['pick_ml']['pick_class'] 569 | else: 570 | # tpick_pred = model.picks[i][sta]['P']['twd'][k]['pick_ml']['tpick_det'] 571 | tpick_pred = model.picks[i][sta]['P']['twd'][k]['pick_ml_det'] 572 | tp_plot = tstart_win - tstart_plot + tpick_pred 573 | if ii == 0: 574 | ax[-1].axvline(tp_plot, c='r', lw=1.5, ls='-', label='P pick') 575 | else: 576 | ax[-1].axvline(tp_plot, c='r', lw=1.5, ls='-') 577 | # 578 | for jj, l in enumerate(model.picks[i][sta]['S']['true_arg']): 579 | # 580 | # S pick corrected after phase picking 581 | # 582 | tstart_win = model.picks[i][sta]['S']['twd'][l]['tstart_win'] 583 | if config.picking['run_mcd']: 584 | tpick_pred = model.picks[i][sta]['S']['twd'][l]['pick_ml']['tpick'] 585 | # tpick_th1 = model.picks[i][sta]['S']['twd'][l]['pick_ml']['tpick_th1'] 586 | # tpick_th2 = model.picks[i][sta]['S']['twd'][l]['pick_ml']['tpick_th2'] 587 | # pick_class = model.picks[i][sta]['S']['twd'][l]['pick_ml']['pick_class'] 588 | else: 589 | # tpick_pred = model.picks[i][sta]['S']['twd'][l]['pick_ml']['tpick_det'] 590 | tpick_pred = model.picks[i][sta]['S']['twd'][l]['pick_ml_det'] 591 | ts_plot = tstart_win - tstart_plot + tpick_pred 592 | if jj == 0: 593 | ax[-1].axvline(ts_plot, c='b', lw=1.5, ls='-', label='S pick') 594 | else: 595 | ax[-1].axvline(ts_plot, c='b', lw=1.5, ls='-') 596 | # 597 | ylim = [-1., 1.] 598 | ax[-1].set_ylim(ylim) 599 | ax[-1].set_xlim([0, tend_plot - tstart_plot]) 600 | if n == len(plot_comps)-1: 601 | plt.legend(loc='lower left', fontsize=14.) 602 | if add_rows == 0: 603 | ax[-1].set_xlabel("Time [s]") 604 | # 605 | # plot predicted P, S, Noise class probability functions 606 | # 607 | if len(plot_probs) > 0: 608 | ax.append(fig.add_subplot(len(plot_comps)+add_rows, 1, len(plot_comps)+1)) 609 | ax[-1].set_xlim([0, tend_plot - tstart_plot]) 610 | ax[-1].set_xlabel("Time [s]") 611 | ax[-1].set_ylim([-.05, 1.05]) 612 | ax[-1].set_ylabel("Probability") 613 | if 'P' in plot_probs: 614 | x_prob_p = model.detections[i][sta]['tt']+tp_shift 615 | y_prob_p = model.detections[i][sta]['ts'][:,0] 616 | ax[-1].plot(x_prob_p, y_prob_p, c='red', lw=0.75, label='P') 617 | if 'S' in plot_probs: 618 | x_prob_s = model.detections[i][sta]['tt']+ts_shift 619 | y_prob_s = model.detections[i][sta]['ts'][:,1] 620 | ax[-1].plot(x_prob_s, y_prob_s, c='blue', lw=0.75, label='S') 621 | if 'N' in plot_probs: 622 | x_prob_n = model.detections[i][sta]['tt']+tn_shift 623 | y_prob_n = model.detections[i][sta]['ts'][:,2] 624 | ax[-1].plot(x_prob_n, y_prob_n, c='k', lw=0.75, label='N') 625 | if len(plot_probs) > 0: 626 | plt.legend(loc='lower left', fontsize=14.) 627 | # 628 | plt.tight_layout() 629 | # 630 | opath = model.detections[i][sta]['opath'] 631 | tstr_start = tr.stats.starttime.strftime("%Y%m%dT%H%M%S") 632 | tstr_end = tr.stats.endtime.strftime("%Y%m%dT%H%M%S") 633 | opath = f"{opath}/wf_plots" 634 | os.makedirs(opath, exist_ok=True) 635 | # 636 | ofig = f"{opath}/{config.data['net']}_{sta}_{tstr_start}_{tstr_end}" 637 | plt.savefig(f"{ofig}.png", bbox_inches='tight', dpi=90) 638 | # plt.savefig(f"{ofig}.eps", format='eps', bbox_inches='tight', dpi=150) 639 | plt.close() 640 | --------------------------------------------------------------------------------