├── .gitignore ├── LICENSE ├── PointCloudObjectRetrieval.py ├── PointCloudSeismicInterpretation.py ├── README.md ├── assets └── images │ ├── geobody-detection.PNG │ └── seismic-segmentation.gif ├── d2geo ├── LICENSE ├── README └── attributes │ ├── CompleTrace.py │ ├── DipAzm.py │ ├── EdgeDetection.py │ ├── Frequency.py │ ├── NoiseReduction.py │ ├── SignalProcess.py │ ├── __init__.py.txt │ ├── io.py │ └── util.py ├── environment.yml ├── notebooks └── F3dataset-segmentation.ipynb ├── requirements.txt └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | data/* 2 | outputs/* 3 | documents/* 4 | 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | 10 | # C extensions 11 | *.so 12 | 13 | # Distribution / packaging 14 | .Python 15 | build/ 16 | develop-eggs/ 17 | dist/ 18 | downloads/ 19 | eggs/ 20 | .eggs/ 21 | lib/ 22 | lib64/ 23 | parts/ 24 | sdist/ 25 | var/ 26 | wheels/ 27 | pip-wheel-metadata/ 28 | share/python-wheels/ 29 | *.egg-info/ 30 | .installed.cfg 31 | *.egg 32 | MANIFEST 33 | 34 | # PyInstaller 35 | # Usually these files are written by a python script from a template 36 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 37 | *.manifest 38 | *.spec 39 | 40 | # Installer logs 41 | pip-log.txt 42 | pip-delete-this-directory.txt 43 | 44 | # Unit test / coverage reports 45 | htmlcov/ 46 | .tox/ 47 | .nox/ 48 | .coverage 49 | .coverage.* 50 | .cache 51 | nosetests.xml 52 | coverage.xml 53 | *.cover 54 | *.py,cover 55 | .hypothesis/ 56 | .pytest_cache/ 57 | 58 | # Translations 59 | *.mo 60 | *.pot 61 | 62 | # Django stuff: 63 | *.log 64 | local_settings.py 65 | db.sqlite3 66 | db.sqlite3-journal 67 | 68 | # Flask stuff: 69 | instance/ 70 | .webassets-cache 71 | 72 | # Scrapy stuff: 73 | .scrapy 74 | 75 | # Sphinx documentation 76 | docs/_build/ 77 | 78 | # PyBuilder 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | .python-version 90 | 91 | # pipenv 92 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 93 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 94 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 95 | # install all needed dependencies. 96 | #Pipfile.lock 97 | 98 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 99 | __pypackages__/ 100 | 101 | # Celery stuff 102 | celerybeat-schedule 103 | celerybeat.pid 104 | 105 | # SageMath parsed files 106 | *.sage.py 107 | 108 | # Environments 109 | .env 110 | .venv 111 | env/ 112 | venv/ 113 | ENV/ 114 | env.bak/ 115 | venv.bak/ 116 | 117 | # Spyder project settings 118 | .spyderproject 119 | .spyproject 120 | 121 | # Rope project settings 122 | .ropeproject 123 | 124 | # mkdocs documentation 125 | /site 126 | 127 | # mypy 128 | .mypy_cache/ 129 | .dmypy.json 130 | dmypy.json 131 | 132 | # Pyre type checker 133 | .pyre/ 134 | -------------------------------------------------------------------------------- /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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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 | -------------------------------------------------------------------------------- /PointCloudObjectRetrieval.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | """ 4 | maintainer: 5 | """ 6 | 7 | import numpy as np 8 | from math import hypot, atan2 9 | import cv2 10 | from sklearn.neighbors import KDTree 11 | from datetime import datetime 12 | import pandas as pd 13 | 14 | class PointCloudObjectRetrieval(): 15 | """ 16 | Description 17 | ----------- 18 | Characterise every object in a seismic point cloud 19 | 20 | Attributes: 21 | ----------- 22 | point_cloud (np.array): 23 | semblance (np.array): 24 | labels_pointcloud (np.array or list): 25 | 26 | Methods: 27 | -------- 28 | get_features: Extracts pandas dataFrame containing the features descriptives of every selected cluster 29 | """ 30 | def __init__( self, point_cloud: np.array, semblance: np.array, labels_point_cloud: (np.array or list) ): 31 | """ 32 | Description 33 | ----------- 34 | Initiates the point cloud objects 35 | 36 | Args: 37 | ----- 38 | point_cloud (np.array): point cloud coordinates (n, 4), first axis being the n points, 39 | second axis being the point coordinates and the amplitude reflectivity value (x, y, z, amp) 40 | semblance (np.array): semblance values of the point cloud 41 | labels_point_cloud (np.array or list): label values of the point cloud 42 | """ 43 | self.point_cloud = point_cloud 44 | self.semblance = semblance 45 | self.labels_point_cloud = labels_point_cloud 46 | 47 | def get_features(self, selected_clusters: (np.array or list)): 48 | """ 49 | Description 50 | ----------- 51 | Extracts pandas dataFrame containing the features descriptives of every selected cluster 52 | Features: segmentID, n points, amplitude mean, semblance mean, Zeboudj distance, 53 | contour ratio, lambda1, lambda2, "lambda3", linearity, 54 | slope, planarity, orientation, rZHigh, rZLow 55 | 56 | Args: 57 | ----- 58 | selected_clusters (np.array or list): list of cluster ids for which to perform the feature extraction 59 | 60 | Returns: 61 | -------- 62 | pd.DataFrame: Each row represents a cluster of the segmented point cloud and each collumn 63 | represents the feature values 64 | """ 65 | t0 = datetime.now() 66 | Features = [] 67 | i=0 68 | for isegment in selected_clusters: 69 | i+=1 70 | print("({}/{})".format(i, len(selected_clusters)), end = "\r") 71 | Features.append([isegment] + extract_features( 72 | self.point_cloud[np.isin(self.labels_point_cloud, isegment)], 73 | self.semblance[np.isin(self.labels_point_cloud, isegment)] 74 | ) 75 | ) 76 | ## store it as a dataframe 77 | featureDF = pd.DataFrame(data=Features, columns=[ 78 | "segmentID", "n points", "amplitude mean", "semblance mean", "Zeboudj distance", 79 | "contour ratio", "lambda1", "lambda2", "lambda3", "linearity", 80 | "slope", "planarity", "orientation", "rZHigh", "rZLow", 81 | ]) 82 | featureDF.set_index('segmentID', inplace=True) 83 | featureDF=(featureDF-featureDF.mean())/featureDF.std() 84 | print('time: {}'.format(datetime.now()-t0)) 85 | return featureDF 86 | 87 | 88 | ######### 89 | ## functions 90 | ######### 91 | 92 | def get_aspect_ratio(pcd: np.array): 93 | """ 94 | Description 95 | ----------- 96 | Gets the 3 eigen values, the linearity, the slope, the planarity and the orientation of of a point cloud object 97 | 98 | Args: 99 | pcd (np.array): a point cloud (n, 3) 100 | 101 | Returns: 102 | -------- 103 | (list): [v1, v2, v3, linearity, slope, planarity, orientation] with vn being the n eigen value of the point cloud 104 | """ 105 | pcd_sampling = np.random.choice(len(pcd), 106 | size=min(int(len(pcd)*0.5), 500), replace=False) #min([int(len(point_cloud)*0.2), 1000]), replace=False) 107 | eigvals, eigvecs = np.linalg.eig(np.cov(pcd[pcd_sampling].T)) 108 | idx = eigvals.argsort()[::-1] 109 | eigvals = eigvals[idx] 110 | eigvecs = eigvecs[:,idx] 111 | orientation = atan2(eigvecs[0,1], eigvecs[0,0]) 112 | return ( 113 | [eigvals[0], eigvals[1], eigvals[2], 114 | (eigvals[0]-eigvals[1])/eigvals[0], eigvals[2]/eigvals[0], (eigvals[1]-eigvals[2])/eigvals[0], orientation] 115 | ) 116 | 117 | def get_outline_ratio(pcd: np.array): 118 | """ 119 | Description 120 | ----------- 121 | Gets the outline ratio of a point cloud object 122 | The outline ratio is obtained by projection the 3D point cloud on a 2D plane, 123 | and computing its contour over its surface 124 | 125 | Retruns: 126 | -------- 127 | float: the contour ratio 128 | """ 129 | #convert 2d projection to image 130 | #shift X and Y coordinate to 0 origin 131 | X_shift = pcd[:,0]-min(pcd[:,0]); Y_shift = pcd[:,1]-min(pcd[:,1]) 132 | X_shift = X_shift.astype(int, copy=False); Y_shift = Y_shift.astype(int, copy=False) 133 | Img = np.zeros((max(X_shift)+1, max(Y_shift)+1)) 134 | Img[X_shift, Y_shift] = 255 135 | Img=Img.astype('uint8') 136 | Imgblur = cv2.blur(Img, (10, 10)) 137 | _, Imgthresh = cv2.threshold(Imgblur, 50, 255, cv2.THRESH_BINARY) 138 | imct, _ = cv2.findContours(Imgthresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 139 | length = 0 140 | for contour in imct: 141 | length += sum(hypot(x1 - x2, y1 - y2) for (x1, y1), (x2, y2) in zip(contour[:,0, :], contour[1:,0, :])) 142 | surface = Imgthresh.sum()/255 143 | return (length/surface) 144 | 145 | def get_zeboudj_distance(pcd: np.array, distance: float = 2.5): 146 | """ 147 | Description 148 | ----------- 149 | Gets the Zeboudj distance of the spatial distribution of amplitude of a point cloud object 150 | 151 | Returns 152 | ------- 153 | """ 154 | #sample large point cloud to save processing time although keeping a good approximate 155 | pcd_sampling = np.random.choice(len(pcd), size=min(int(len(pcd)*0.5), 10000), replace=False) 156 | cl_sample = pcd[pcd_sampling] 157 | #build kdtree for each cluster 158 | tree = KDTree(cl_sample[:,:3]) 159 | #loop over every point within the cluster to compute color distances with its neighbourhood 160 | L_neighbors_idxs = tree.query_radius(cl_sample[:,:3], r=distance) 161 | CI = 0 162 | for ipoint, neighbors_idxs in enumerate (L_neighbors_idxs): 163 | if len(neighbors_idxs) > 0: 164 | Dist_neighbs = np.zeros(len(neighbors_idxs)) 165 | for ineighb, neighb in enumerate (neighbors_idxs): 166 | Dist_neighbs[ineighb] = cl_sample[:,3][ipoint] - cl_sample[:,3][neighb] 167 | CI += Dist_neighbs.max() 168 | CI = CI/len(cl_sample) 169 | return (CI) 170 | 171 | def get_depth_distribution(pcd: np.array): 172 | """ 173 | Description 174 | ----------- 175 | Gets the distribution of points of a point cloud object in the vertical axis 176 | 177 | Returns: 178 | -------- 179 | (list): [p1, p2] p1 proportion under one forth of the depth, p2 proportion above three fourth of the depth 180 | """ 181 | zmin = np.percentile(pcd[:,2], 2) 182 | zmax = np.percentile(pcd[:,2], 98) 183 | r_zlow = len(pcd[pcd[:,2]zmax-(zmax-zmin)/4])/len(pcd) #proportion_over_three_fourth 185 | return([r_zlow, r_zhigh]) 186 | 187 | def extract_features(pcd: np.array, semblance: np.array): 188 | """ 189 | Description 190 | ----------- 191 | Extracts all features of a point cloud object 192 | 193 | Returns: 194 | -------- 195 | (list): [n_points, amp_mean, semblance_mean, zeboudj_distance, outline_ratio, v1, v2, v3, linearity, 196 | slope, planarity, orientation, p1, p2] 197 | """ 198 | n_points = len(pcd) 199 | amp_mean = pcd[:,3].mean() 200 | semb_mean = semblance.mean() 201 | zeboudj_2_5 = get_zeboudj_distance(pcd, distance=2.5) 202 | # zeboudj_3_5 = get_zeboudj_distance(pcd, distance=3.5) 203 | outline_ratio = get_outline_ratio(pcd[:,:2]) 204 | aspect_ratios = get_aspect_ratio(pcd[:,:3]) 205 | r_z = get_depth_distribution(pcd[:,:3]) 206 | return ([n_points]+[amp_mean]+[semb_mean]+[zeboudj_2_5]+[outline_ratio]+aspect_ratios+r_z) 207 | 208 | -------------------------------------------------------------------------------- /PointCloudSeismicInterpretation.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | """ 4 | maintainer: 5 | """ 6 | 7 | import numpy as np 8 | import pandas as pd 9 | from scipy.signal import argrelextrema 10 | from datetime import datetime 11 | from sklearn.cluster import DBSCAN 12 | import dask.array as da 13 | import dask 14 | 15 | from d2geo.attributes.EdgeDetection import EdgeDetection 16 | from d2geo.attributes.util import compute_chunk_size 17 | from utils import * 18 | 19 | 20 | class PointCloudSeismicInterpretation(): 21 | """ 22 | Description 23 | ----------- 24 | Create Point Cloud Seismic dataframe from a 3D seismic cube 25 | The point cloud is extracted by local extrema extraction in the trace direction, 26 | filter on semblance and filter on amplitude 27 | 28 | Attributes: 29 | ----------- 30 | seismic_array (np.array): 3D seismic cube as a 3D numpy array with amplitude reflectivity values 31 | point_cloud (np.array): point cloud attribute as a 2D array with shape (n, 3) with n being the number 32 | of points and the second axis being the coordinates of the points (x, y, z) 33 | amplitude_point_cloud (np.array): amplitude reflectivity values of the points of the point cloud 34 | semblance_array (np.array): 3D semblance cube computed from the 3D seismic 35 | semblance_point_cloud (np.array): semblance values of the points of the point cloud 36 | ampv95p (float): approximate of the 95-th percentile of the seismic amplitude 37 | 38 | Methods: 39 | --------- 40 | load_seismic_array: Loads the 3D seismic cube 41 | extrema_extraction: Extracts the point cloud from the seismic with local extrema in the trace direction 42 | extrema_extraction_dask: Extracts the point cloud from the seismic with local extrema in the trace direction 43 | - multiprocessing with dask 44 | filter_point_cloud_with_semblance: Computes the semblance cube and filters the point cloud based on semblance cut-off 45 | filter_point_cloud_with_amplitude: Filters the point cloud based on amplitude cut-off 46 | DBSCAN_segmentation_sklearn: segments the point cloud based on DBSCAN clustering - sklearn implementation 47 | 48 | """ 49 | def __init__( self, seismic_array, ampv95p=None ): 50 | self.load_seismic_array(seismic_array, ampv95p=ampv95p) 51 | 52 | def load_seismic_array(self, seismic_array, ampv95p=None): 53 | """ 54 | Description 55 | ----------- 56 | Loads the 3D seismic cube, computes 95th amplitude quartile and the intialise the point cloud objects 57 | 58 | Args: 59 | seismic_array (np.array): 3D seismic cube as a 3D numpy array with amplitude reflectivity values 60 | ampv95p (float or None): the 95-th percentile of the seismic amplitude 61 | - if not None prevents from having to compute it 62 | """ 63 | self.seismic_array = seismic_array 64 | if not ampv95p: 65 | self.ampv95p = np.percentile(self.seismic_array[self.seismic_array.shape[0]//2:self.seismic_array.shape[0]//2+100, 66 | self.seismic_array.shape[1]//2:self.seismic_array.shape[1]//2+100, 67 | : ], 95) # compute the 99th percentile of amplitude to scale 68 | else: 69 | self.ampv95p = ampv95p 70 | self.point_cloud = np.array([]) 71 | self.amplitude_point_cloud = np.array([]) 72 | self.semblance_array = np.array([]) 73 | self.semblance_point_cloud = np.array([]) 74 | 75 | def extrema_extraction(self, extrema_type=np.greater): 76 | """ 77 | Description 78 | ----------- 79 | Extract extrema in the trace direction (3rd dimension) of a 3D array 80 | Creates a point cloud attribute (numpy.array) of shape [n_extrema, 3] 81 | Each row of the point cloud being [x,, y, z] of the each extrema 82 | Amplitude values are normalized and stored in a amplitude point cloud attribute 83 | 84 | Args: 85 | ----- 86 | extrema_type (callable, default np.greater): np.greater or np.less, the type of extrema to extract (maxima or minima) 87 | """ 88 | t0 = datetime.now() 89 | (nx, ny, nz) = self.seismic_array.shape 90 | Lx = [] 91 | Ly = [] 92 | Lz = [] 93 | for i in range (nx): 94 | for j in range (ny): 95 | maxima = argrelextrema(self.seismic_array[i, j], extrema_type)[0] 96 | Lx.extend([i]*len(maxima)) 97 | Ly.extend([j]*len(maxima)) 98 | Lz.extend(maxima) 99 | self.point_cloud = np.zeros((len(Lx), 3), dtype='int32') 100 | self.point_cloud[:, 0] = Lx 101 | self.point_cloud[:, 1] = Ly 102 | self.point_cloud[:, 2] = Lz 103 | self.amplitude_point_cloud = NormalizeData(self.seismic_array[Lx, Ly, Lz], thr=self.ampv95p, type='positive') 104 | self.semblance_point_cloud = np.array([]) 105 | print('Point cloud created - {} points - time to execute: {}'.format(self.point_cloud.shape[0], datetime.now()-t0)) 106 | 107 | def extrema_extraction_dask(self, extrema_type=np.greater): 108 | """ 109 | Description 110 | ----------- 111 | Extract extrema in the trace direction (3rd dimension) of a 3D array 112 | Creates a point cloud attribute (numpy.array) of shape [n_extrema, 3] 113 | Each row of the point cloud being [x,, y, z] of the each extrema 114 | Amplitude values are normalized and stored in a amplitude point cloud attribute 115 | Dask multiprocessing implementation 116 | 117 | Args: 118 | ----- 119 | extrema_type (callable, default np.greater): np.greater or np.less, the type of extrema to extract (maxima or minima) 120 | """ 121 | t0 = datetime.now() 122 | (xChunkSize, yChunkSize, zChunkSize) = compute_chunk_size(self.seismic_array.shape, 123 | self.seismic_array.dtype.itemsize, 124 | kernel=(1, 1, self.seismic_array.shape[2]), 125 | preview=None) 126 | seis_ilBlock_dask_trace = da.from_array(self.seismic_array, chunks=(xChunkSize, yChunkSize, self.seismic_array.shape[2])) 127 | blocks = seis_ilBlock_dask_trace.to_delayed()#.ravel() 128 | results = [] 129 | for ixb, xb in enumerate(blocks): 130 | results.extend([da.from_delayed(get_point_cloud_chunks(yb[0], ixb*xChunkSize, iyb*xChunkSize, extrema_type), shape=(3, np.nan), dtype=np.int64) 131 | for iyb, yb in enumerate(xb)]) 132 | arr = da.concatenate(results, axis=1, allow_unknown_chunksizes=True) 133 | self.point_cloud = arr.compute() 134 | self.point_cloud = self.point_cloud.T 135 | print('point cloud shape {}'.format(self.point_cloud.shape)) 136 | self.amplitude_point_cloud = NormalizeData( 137 | self.point_cloud[:,3], thr=self.ampv95p, type='positive' 138 | ) 139 | self.point_cloud = self.point_cloud[:,:3] 140 | self.semblance_point_cloud = np.array([]) 141 | print('Point cloud created - {} points - time to execute: {}'.format(self.point_cloud.shape[0], datetime.now()-t0)) 142 | 143 | def filter_point_cloud_with_semblance(self, kernel=(3, 3, 9), thr=0.9, in_place=True): 144 | """ 145 | Description 146 | ----------- 147 | Computes the semblance cube and filters the point cloud based on semblance cut-off 148 | 149 | Args: 150 | ----- 151 | kernel (tuple, default (3, 3, 9)): tuple of int (x, y, z) the dimension of the 3D kernel applied to compute semblance 152 | thr (float, default 0.9): semblance threshold, float between 0 and 1 153 | in_place (bool, default True): If True, perform operation in-place. 154 | """ 155 | if self.point_cloud.size == 0: 156 | print('Point cloud not computed - extracting extrema first') 157 | self.extrema_extraction() 158 | return() 159 | 160 | if self.semblance_array.size == 0: 161 | t0 = datetime.now() 162 | daSemblance = EdgeDetection().semblance(darray=self.seismic_array, kernel=kernel) #Dask instance of the semblance 163 | self.semblance_array = daSemblance.compute() 164 | self.semblance_point_cloud = np.array([]) 165 | print('Semblance attribute computed - time to execute: {}'.format(datetime.now()-t0)) 166 | if self.semblance_point_cloud.size == 0: 167 | t0 = datetime.now() 168 | self.semblance_point_cloud = self.semblance_array[self.point_cloud[:, 0], self.point_cloud[:, 1], self.point_cloud[:, 2]] 169 | print('Semblance point cloud extracted - time to execute: {}'.format(datetime.now()-t0)) 170 | t0 = datetime.now() 171 | if in_place: 172 | mask = self.semblance_point_cloud > thr 173 | self.point_cloud = self.point_cloud[mask] 174 | self.amplitude_point_cloud = self.amplitude_point_cloud[mask] 175 | self.semblance_point_cloud = self.semblance_point_cloud[mask] 176 | print('Applied semblance filter in place - {} points - time to execute: {}'.format(self.point_cloud.shape[0], datetime.now()-t0)) 177 | else: 178 | return (self.point_cloud[ self.semblance_point_cloud > thr]) 179 | 180 | def filter_point_cloud_with_amplitude(self, thr=0.25, in_place=True): 181 | """ 182 | Description 183 | ----------- 184 | Filters the point cloud based on amplitude cut-off 185 | 186 | Args: 187 | ----- 188 | thr (float, default 0.25): amplitude threshold, float between 0 and 1 189 | in_place (bool, default True): If True, perform operation in-place. 190 | """ 191 | if self.point_cloud.size == 0: 192 | print('Point cloud not computed - extract extrema first') 193 | t0 = datetime.now() 194 | if in_place: 195 | mask = self.amplitude_point_cloud > thr 196 | self.point_cloud = self.point_cloud[mask] 197 | self.semblance_point_cloud = self.semblance_point_cloud[mask] 198 | self.amplitude_point_cloud = self.amplitude_point_cloud[mask] 199 | print('Applied amplitude filter in place - {} points - time to execute: {}'.format(self.point_cloud.shape[0], datetime.now()-t0)) 200 | else: 201 | return (self.point_cloud[ self.amplitude_point_cloud > thr ]) 202 | 203 | def DBSCAN_segmentation_sklearn(self, eps=2, min_samples=8, z_factor=1): 204 | """ 205 | Description 206 | ----------- 207 | Segments the point cloud based on DBSCAN clustering - sklearn implementation 208 | 209 | Args: 210 | ----- 211 | eps (float, default 2): epsilon distance parameter to DBSCAN 212 | min_samples (int, default 8): minimum number of points parameter to DBSCAN 213 | z_factor (int, default 1): vertical exageration to apply to the seismic point cloud 214 | """ 215 | t0=datetime.now() 216 | point_cloud_to_compute = self.point_cloud.copy() 217 | point_cloud_to_compute[:, 2] = self.point_cloud[:, 2]*z_factor 218 | print('vertical exageration applied') 219 | clustering = DBSCAN(eps=eps, min_samples=min_samples, algorithm='ball_tree', n_jobs=-1).fit(point_cloud_to_compute) 220 | self.labels = clustering.labels_ 221 | print('seismic segmented - {} clusters - time {}'.format( self.labels.max()+1, datetime.now()-t0 )) 222 | 223 | ###### 224 | # Functions 225 | ###### 226 | 227 | def NormalizeData(data: np.array, thr: float, type='positive'): 228 | """ 229 | Description 230 | ----------- 231 | Normalize data between [0, thr] if positive and [-thr, 0] if negative 232 | 233 | Args: 234 | ----- 235 | data (np.array): 236 | thr (float): threshold to ceil the data 237 | 238 | Returns: 239 | -------- 240 | np.array: data normalized 241 | """ 242 | if type == 'positive': 243 | data[data < 0] = 0. 244 | data[data > thr] = thr 245 | return (data / thr) 246 | elif type == 'negative': 247 | data[data > 0] = 0. 248 | data[data < -thr] = -thr 249 | return (data/ thr) 250 | else: 251 | print('Unrecognized type, expected "positive" or "negative", got {}'.format(type)) 252 | return None 253 | 254 | @dask.delayed 255 | def get_point_cloud_chunks(seismic, xchunk, ychunk, extrema_type=np.greater): 256 | """ 257 | Description 258 | ----------- 259 | Extract extrema of a 1D signal - dask delayed implementation 260 | 261 | Args: 262 | ----- 263 | seismic (np.array): 264 | xchunk (int): 265 | ychunk (int): 266 | extrema_type (callable, default np.greater): np.greater or np.less, the type of extrema to extract (maxima or minima) 267 | 268 | Returns: 269 | -------- 270 | np.array: point cloud of extrema from data, shape (3, n): first axis being the coordinates, 271 | second axis being the n extrema points 272 | """ 273 | (nx, ny, nz) = seismic.shape 274 | Lx = [] 275 | Ly = [] 276 | Lz = [] 277 | Lamp = [] 278 | for i in range (nx): 279 | for j in range (ny): 280 | maxima = argrelextrema(seismic[i,j], extrema_type)[0] 281 | Lx.extend([xchunk + i]*len(maxima)) 282 | Ly.extend([ychunk + j]*len(maxima)) 283 | Lz.extend(maxima) 284 | Lamp.extend(seismic[i,j,maxima]) 285 | point_cloud = np.zeros((4, len(Lx),), dtype='int32') 286 | point_cloud[0, :] = Lx 287 | point_cloud[1, :] = Ly 288 | point_cloud[2, :] = Lz 289 | point_cloud[3, :] = Lamp 290 | return(point_cloud) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pyseismic: seismic segmentation and geobody detection in 3D seismic 2 | 3 | Workflow to automatically segment 3D seismic reflection data into objects and detect and extract geobodies. 4 | 5 | ## Usage 6 | 7 | For seismic segmentation 8 | 9 | ```python 10 | import open3d as o3d 11 | from pyntcloud import PyntCloud 12 | 13 | from PointCloudSeismicInterpretation import PointCloudSeismicInterpretation 14 | 15 | #first load your 3D seismic 16 | #seismic_data is your 3D seismic data loaded as a numpy array 17 | 18 | #initiate seismic data object 19 | pointCloudInterpretor = PointCloudSeismicInterpretation(seismic_array=seismic_data) 20 | 21 | #extract a point cloud of trace extrema from the seismic 22 | pointCloudInterpretor.extrema_extraction_dask() 23 | 24 | #compute semblance attribute and filter points based on a semblance cut-off value 25 | pointCloudInterpretor.filter_point_cloud_with_semblance(kernel=(3,3,9), thr=0.85, in_place=True) 26 | 27 | #filter points based on an amplitude cut-off value 28 | pointCloudInterpretor.filter_point_cloud_with_amplitude(thr=0.20, in_place=True) 29 | 30 | #create an open3D point cloud object and segment the point cloud with DBSCAN 31 | pcd_pyntcloud = PyntCloud(pd.DataFrame(data={'x':pointCloudInterpretor.point_cloud.T[0], 32 | 'y':pointCloudInterpretor.point_cloud.T[1], 33 | 'z':pointCloudInterpretor.point_cloud.T[2], 34 | 'amplitude':pointCloudInterpretor.amplitude_point_cloud})[:]) 35 | pcd_o3d = pcd_pyntcloud.to_instance("open3d", mesh=False) 36 | with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm: 37 | labels = np.array( 38 | pcd_o3d.cluster_dbscan(eps=2, min_points=8, print_progress=True)) 39 | ``` 40 | 41 | For geobody detection in a segmented seismic 42 | 43 | ```python 44 | from lshashpy3 import LSHash 45 | from PointCloudObjectRetrieval import PointCloudObjectRetrieval 46 | 47 | #merge seismic point cloud and amplitude point cloud in an numpy array ([n_points, 4]) 48 | pcd = np.zeros((len(pointCloudInterpretor.point_cloud),4)) 49 | pcd[:,:3] = pointCloudInterpretor.point_cloud[:,:] 50 | pcd[:,3]= pointCloudInterpretor.amplitude_point_cloud 51 | #initiate feature extraction object 52 | FeatureExtractor = PointCloudObjectRetrieval( 53 | pcd, 54 | pointCloudInterpretor.semblance_point_cloud, 55 | labels 56 | ) 57 | 58 | #extract features for each point cloud object 59 | standardized_featureDF = FeatureExtractor.get_features(selected_clusters) 60 | 61 | #contruct lsh tables 62 | feature_dict = dict(zip(standardized_featureDF.index, standardized_featureDF[selected_features].values)) 63 | k = 7 # hash size 64 | L = 20 # number of tables 65 | d = len(selected_features) #2048 # Dimension of Feature vector 66 | lsh = LSHash(hash_size=k, input_dim=d, num_hashtables=L) 67 | for ID, vec in notebook.tqdm(feature_dict.items()): 68 | lsh.index(vec.flatten(), extra_data=ID) 69 | 70 | #query similar geobodies to a specific geobody example previously identified 71 | n_items = 10 #number of similar geobodies 72 | feature_example = feature_dict[0].flatten() #features of the geobody example 73 | #replace 0 by the id of the identified example 74 | response = lsh_variable.query(feature_example, num_results=n_items, distance_func='hamming') 75 | ``` 76 | 77 | ## Notebook demo 78 | 79 | For a more complete demo run the the file notebooks/F3dataset-segmentation.ipynb 80 | 81 | It gives a complete example of seismic segmentation of the open-source F3-dataset (https://terranubis.com/datainfo/F3-Demo-2020) and geobody detection. 82 | 83 | #### Point cloud seismic segmentation of F3 seismic data 84 | ![](assets/images/seismic-segmentation.gif) 85 | 86 | #### Geobody detection - elongated geobodies - in the F3 seismic data (150-closest objects) 87 | drawing 88 | -------------------------------------------------------------------------------- /assets/images/geobody-detection.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GeoDataScienceUQ/pyseismic/d8200e1181b6a4ec2719cba6b9ec25a1a5c33469/assets/images/geobody-detection.PNG -------------------------------------------------------------------------------- /assets/images/seismic-segmentation.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GeoDataScienceUQ/pyseismic/d8200e1181b6a4ec2719cba6b9ec25a1a5c33469/assets/images/seismic-segmentation.gif 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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 | -------------------------------------------------------------------------------- /d2geo/README: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GeoDataScienceUQ/pyseismic/d8200e1181b6a4ec2719cba6b9ec25a1a5c33469/d2geo/README -------------------------------------------------------------------------------- /d2geo/attributes/CompleTrace.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Complex Trace Attributes for Seismic Data 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import dask.array as da 12 | import numpy as np 13 | import util 14 | from SignalProcess import SignalProcess as sp 15 | 16 | 17 | class ComplexAttributes(): 18 | """ 19 | Description 20 | ----------- 21 | Class object containing methods for computing complex trace attributes 22 | from 3D seismic data. 23 | 24 | Methods 25 | ------- 26 | create_array 27 | envelope 28 | instantaneous_phase 29 | cosine_instantaneous_phase 30 | relative_amplitude_change 31 | instantaneous_frequency 32 | instantaneous_bandwidth 33 | dominant_frequency 34 | frequency_change 35 | sweetness 36 | quality_factor 37 | response_phase 38 | response_frequency 39 | response_amplitude 40 | apparent_polarity 41 | """ 42 | 43 | def create_array(self, darray, kernel=None, preview=None): 44 | """ 45 | Description 46 | ----------- 47 | Convert input to Dask Array with ideal chunk size as necessary. Perform 48 | necessary ghosting as needed for opertations utilizing windowed functions. 49 | 50 | Parameters 51 | ---------- 52 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 53 | 54 | Keywork Arguments 55 | ----------------- 56 | kernel : tuple (len 3), operator size 57 | preview : str, enables or disables preview mode and specifies direction 58 | Acceptable inputs are (None, 'inline', 'xline', 'z') 59 | Optimizes chunk size in different orientations to facilitate rapid 60 | screening of algorithm output 61 | 62 | Returns 63 | ------- 64 | darray : Dask Array 65 | chunk_init : tuple (len 3), chunk size before ghosting. Used in select cases 66 | """ 67 | 68 | # Compute chunk size and convert if not a Dask Array 69 | if not isinstance(darray, da.core.Array): 70 | chunk_size = util.compute_chunk_size(darray.shape, 71 | darray.dtype.itemsize, 72 | kernel=kernel, 73 | preview=preview) 74 | darray = da.from_array(darray, chunks=chunk_size) 75 | chunks_init = darray.chunks 76 | 77 | else: 78 | chunks_init = darray.chunks 79 | 80 | # Ghost Dask Array if operation specifies a kernel 81 | if kernel != None: 82 | hw = tuple(np.array(kernel) // 2) 83 | darray = da.ghost.ghost(darray, depth=hw, boundary='reflect') 84 | 85 | return(darray, chunks_init) 86 | 87 | 88 | def envelope(self, darray, preview=None): 89 | """ 90 | Description 91 | ----------- 92 | Compute the Envelope of the input data 93 | 94 | Parameters 95 | ---------- 96 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 97 | 98 | Keywork Arguments 99 | ----------------- 100 | preview : str, enables or disables preview mode and specifies direction 101 | Acceptable inputs are (None, 'inline', 'xline', 'z') 102 | Optimizes chunk size in different orientations to facilitate rapid 103 | screening of algorithm output 104 | 105 | Returns 106 | ------- 107 | result : Dask Array 108 | """ 109 | 110 | kernel = (1,1,25) 111 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 112 | analytical_trace = darray.map_blocks(util.hilbert, dtype=darray.dtype) 113 | result = da.absolute(analytical_trace) 114 | result = util.trim_dask_array(result, kernel) 115 | 116 | return(result) 117 | 118 | 119 | 120 | def instantaneous_phase(self, darray, preview=None): 121 | """ 122 | Description 123 | ----------- 124 | Compute the Instantaneous Phase of the input data 125 | 126 | Parameters 127 | ---------- 128 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 129 | 130 | Keywork Arguments 131 | ----------------- 132 | preview : str, enables or disables preview mode and specifies direction 133 | Acceptable inputs are (None, 'inline', 'xline', 'z') 134 | Optimizes chunk size in different orientations to facilitate rapid 135 | screening of algorithm output 136 | 137 | Returns 138 | ------- 139 | result : Dask Array 140 | """ 141 | 142 | kernel = (1,1,25) 143 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 144 | analytical_trace = darray.map_blocks(util.hilbert, dtype=darray.dtype) 145 | result = da.rad2deg(da.angle(analytical_trace)) 146 | result = util.trim_dask_array(result, kernel) 147 | 148 | return(result) 149 | 150 | 151 | def cosine_instantaneous_phase(self, darray, preview=None): 152 | """ 153 | Description 154 | ----------- 155 | Compute the Cose of Instantaneous Phase of the input data 156 | 157 | Parameters 158 | ---------- 159 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 160 | 161 | Keywork Arguments 162 | ----------------- 163 | preview : str, enables or disables preview mode and specifies direction 164 | Acceptable inputs are (None, 'inline', 'xline', 'z') 165 | Optimizes chunk size in different orientations to facilitate rapid 166 | screening of algorithm output 167 | 168 | Returns 169 | ------- 170 | result : Dask Array 171 | """ 172 | 173 | darray, chunks_init = self.create_array(darray, preview=preview) 174 | phase = self.instantaneous_phase(darray) 175 | result = da.rad2deg(da.angle(phase)) 176 | 177 | return(result) 178 | 179 | 180 | 181 | def relative_amplitude_change(self, darray, preview=None): 182 | """ 183 | Description 184 | ----------- 185 | Compute the Relative Amplitude Change of the input data 186 | 187 | Parameters 188 | ---------- 189 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 190 | 191 | Keywork Arguments 192 | ----------------- 193 | preview : str, enables or disables preview mode and specifies direction 194 | Acceptable inputs are (None, 'inline', 'xline', 'z') 195 | Optimizes chunk size in different orientations to facilitate rapid 196 | screening of algorithm output 197 | 198 | Returns 199 | ------- 200 | result : Dask Array 201 | """ 202 | 203 | darray, chunks_init = self.create_array(darray, preview=preview) 204 | env = self.envelope(darray) 205 | env_prime = sp().first_derivative(env, axis=-1) 206 | result = env_prime / env 207 | result = da.clip(result, -1, 1) 208 | 209 | return(result) 210 | 211 | 212 | 213 | def amplitude_acceleration(self, darray, preview=None): 214 | """ 215 | Description 216 | ----------- 217 | Compute the Amplitude Acceleration of the input data 218 | 219 | Parameters 220 | ---------- 221 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 222 | 223 | Keywork Arguments 224 | ----------------- 225 | preview : str, enables or disables preview mode and specifies direction 226 | Acceptable inputs are (None, 'inline', 'xline', 'z') 227 | Optimizes chunk size in different orientations to facilitate rapid 228 | screening of algorithm output 229 | 230 | Returns 231 | ------- 232 | result : Dask Array 233 | """ 234 | 235 | darray, chunks_init = self.create_array(darray, preview=preview) 236 | rac = self.relative_amplitude_change(darray) 237 | result = sp().first_derivative(rac, axis=-1) 238 | 239 | return(result) 240 | 241 | 242 | 243 | def instantaneous_frequency(self, darray, sample_rate=4, preview=None): 244 | """ 245 | Description 246 | ----------- 247 | Compute the Instantaneous Frequency of the input data 248 | 249 | Parameters 250 | ---------- 251 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 252 | 253 | Keywork Arguments 254 | ----------------- 255 | sample_rate : Number, sample rate in milliseconds (ms) 256 | preview : str, enables or disables preview mode and specifies direction 257 | Acceptable inputs are (None, 'inline', 'xline', 'z') 258 | Optimizes chunk size in different orientations to facilitate rapid 259 | screening of algorithm output 260 | 261 | Returns 262 | ------- 263 | result : Dask Array 264 | """ 265 | 266 | darray, chunks_init = self.create_array(darray, preview=preview) 267 | 268 | fs = 1000 / sample_rate 269 | phase = self.instantaneous_phase(darray) 270 | phase = da.deg2rad(phase) 271 | phase = phase.map_blocks(np.unwrap, dtype=darray.dtype) 272 | phase_prime = sp().first_derivative(phase, axis=-1) 273 | result = da.absolute((phase_prime / (2.0 * np.pi) * fs)) 274 | 275 | return(result) 276 | 277 | 278 | def instantaneous_bandwidth(self, darray, preview=None): 279 | """ 280 | Description 281 | ----------- 282 | Compute the Instantaneous Bandwidth of the input data 283 | 284 | Parameters 285 | ---------- 286 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 287 | 288 | Keywork Arguments 289 | ----------------- 290 | preview : str, enables or disables preview mode and specifies direction 291 | Acceptable inputs are (None, 'inline', 'xline', 'z') 292 | Optimizes chunk size in different orientations to facilitate rapid 293 | screening of algorithm output 294 | 295 | Returns 296 | ------- 297 | result : Dask Array 298 | """ 299 | 300 | darray, chunks_init = self.create_array(darray, preview=preview) 301 | rac = self.relative_amplitude_change(darray) 302 | result = da.absolute(rac) / (2.0 * np.pi) 303 | 304 | return(result) 305 | 306 | 307 | def dominant_frequency(self, darray, sample_rate=4, preview=None): 308 | """ 309 | Description 310 | ----------- 311 | Compute the Dominant Frequency of the input data 312 | 313 | Parameters 314 | ---------- 315 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 316 | 317 | Keywork Arguments 318 | ----------------- 319 | sample_rate : Number, sample rate in milliseconds (ms) 320 | preview : str, enables or disables preview mode and specifies direction 321 | Acceptable inputs are (None, 'inline', 'xline', 'z') 322 | Optimizes chunk size in different orientations to facilitate rapid 323 | screening of algorithm output 324 | 325 | Returns 326 | ------- 327 | result : Dask Array 328 | """ 329 | 330 | darray, chunks_init = self.create_array(darray, preview=preview) 331 | inst_freq = self.instantaneous_frequency(darray, sample_rate) 332 | inst_band = self.instantaneous_bandwidth(darray) 333 | result = da.hypot(inst_freq, inst_band) 334 | 335 | return(result) 336 | 337 | 338 | def frequency_change(self, darray, sample_rate=4, preview=None): 339 | """ 340 | Description 341 | ----------- 342 | Compute the Frequency Change of the input data 343 | 344 | Parameters 345 | ---------- 346 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 347 | 348 | Keywork Arguments 349 | ----------------- 350 | sample_rate : Number, sample rate in milliseconds (ms) 351 | preview : str, enables or disables preview mode and specifies direction 352 | Acceptable inputs are (None, 'inline', 'xline', 'z') 353 | Optimizes chunk size in different orientations to facilitate rapid 354 | screening of algorithm output 355 | 356 | Returns 357 | ------- 358 | result : Dask Array 359 | """ 360 | 361 | darray, chunks_init = self.create_array(darray, preview=preview) 362 | inst_freq = self.instantaneous_frequency(darray, sample_rate) 363 | result = sp().first_derivative(inst_freq, axis=-1) 364 | 365 | return(result) 366 | 367 | 368 | def sweetness(self, darray, sample_rate=4, preview=None): 369 | """ 370 | Description 371 | ----------- 372 | Compute the Sweetness of the input data 373 | 374 | Parameters 375 | ---------- 376 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 377 | 378 | Keywork Arguments 379 | ----------------- 380 | sample_rate : Number, sample rate in milliseconds (ms) 381 | preview : str, enables or disables preview mode and specifies direction 382 | Acceptable inputs are (None, 'inline', 'xline', 'z') 383 | Optimizes chunk size in different orientations to facilitate rapid 384 | screening of algorithm output 385 | 386 | Returns 387 | ------- 388 | result : Dask Array 389 | """ 390 | 391 | def func(chunk): 392 | chunk[chunk < 5] = 5 393 | return(chunk) 394 | 395 | darray, chunks_init = self.create_array(darray, preview=preview) 396 | inst_freq = self.instantaneous_frequency(darray, sample_rate) 397 | inst_freq = inst_freq.map_blocks(func, dtype=darray.dtype) 398 | env = self.envelope(darray) 399 | 400 | result = env / inst_freq 401 | 402 | return(result) 403 | 404 | 405 | def quality_factor(self, darray, sample_rate=4, preview=None): 406 | """ 407 | Description 408 | ----------- 409 | Compute the Quality Factor of the input data 410 | 411 | Parameters 412 | ---------- 413 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 414 | 415 | Keywork Arguments 416 | ----------------- 417 | sample_rate : Number, sample rate in milliseconds (ms) 418 | preview : str, enables or disables preview mode and specifies direction 419 | Acceptable inputs are (None, 'inline', 'xline', 'z') 420 | Optimizes chunk size in different orientations to facilitate rapid 421 | screening of algorithm output 422 | 423 | Returns 424 | ------- 425 | result : Dask Array 426 | """ 427 | 428 | darray, chunks_init = self.create_array(darray, preview=preview) 429 | 430 | inst_freq = self.instantaneous_frequency(darray, sample_rate) 431 | rac = self.relative_amplitude_change(darray) 432 | 433 | result = (np.pi * inst_freq) / rac 434 | 435 | return(result) 436 | 437 | 438 | def response_phase(self, darray, preview=None): 439 | """ 440 | Description 441 | ----------- 442 | Compute the Response Phase of the input data 443 | 444 | Parameters 445 | ---------- 446 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 447 | 448 | Keywork Arguments 449 | ----------------- 450 | preview : str, enables or disables preview mode and specifies direction 451 | Acceptable inputs are (None, 'inline', 'xline', 'z') 452 | Optimizes chunk size in different orientations to facilitate rapid 453 | screening of algorithm output 454 | 455 | Returns 456 | ------- 457 | result : Dask Array 458 | """ 459 | 460 | def operation(chunk1, chunk2, chunk3): 461 | 462 | out = np.zeros(chunk1.shape) 463 | for i,j in np.ndindex(out.shape[:-1]): 464 | 465 | ints = np.unique(chunk3[i, j, :]) 466 | 467 | for ii in ints: 468 | 469 | idx = np.where(chunk3[i, j, :] == ii)[0] 470 | peak = idx[chunk1[i, j, idx].argmax()] 471 | out[i, j, idx] = chunk2[i, j, peak] 472 | 473 | return(out) 474 | 475 | darray, chunks_init = self.create_array(darray, preview=preview) 476 | env = self.envelope(darray) 477 | phase = self.instantaneous_phase(darray) 478 | troughs = env.map_blocks(util.local_events, comparator=np.less, 479 | dtype=darray.dtype) 480 | troughs = troughs.cumsum(axis=-1) 481 | result = da.map_blocks(operation, env, phase, troughs, dtype=darray.dtype) 482 | result[da.isnan(result)] = 0 483 | 484 | 485 | return(result) 486 | 487 | 488 | def response_frequency(self, darray, sample_rate=4, preview=None): 489 | """ 490 | Description 491 | ----------- 492 | Compute the Response Frequency of the input data 493 | 494 | Parameters 495 | ---------- 496 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 497 | 498 | Keywork Arguments 499 | ----------------- 500 | sample_rate : Number, sample rate in milliseconds (ms) 501 | preview : str, enables or disables preview mode and specifies direction 502 | Acceptable inputs are (None, 'inline', 'xline', 'z') 503 | Optimizes chunk size in different orientations to facilitate rapid 504 | screening of algorithm output 505 | 506 | Returns 507 | ------- 508 | result : Dask Array 509 | """ 510 | 511 | def operation(chunk1, chunk2, chunk3): 512 | 513 | out = np.zeros(chunk1.shape) 514 | for i,j in np.ndindex(out.shape[:-1]): 515 | 516 | ints = np.unique(chunk3[i, j, :]) 517 | 518 | for ii in ints: 519 | 520 | idx = np.where(chunk3[i, j, :] == ii)[0] 521 | peak = idx[chunk1[i, j, idx].argmax()] 522 | out[i, j, idx] = chunk2[i, j, peak] 523 | 524 | return(out) 525 | 526 | darray, chunks_init = self.create_array(darray, preview=preview) 527 | env = self.envelope(darray) 528 | inst_freq = self.instantaneous_frequency(darray, sample_rate) 529 | troughs = env.map_blocks(util.local_events, comparator=np.less, 530 | dtype=darray.dtype) 531 | troughs = troughs.cumsum(axis=-1) 532 | result = da.map_blocks(operation, env, inst_freq, troughs, dtype=darray.dtype) 533 | result[da.isnan(result)] = 0 534 | 535 | return(result) 536 | 537 | 538 | def response_amplitude(self, darray, preview=None): 539 | """ 540 | Description 541 | ----------- 542 | Compute the Response Amplitude of the input data 543 | 544 | Parameters 545 | ---------- 546 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 547 | 548 | Keywork Arguments 549 | ----------------- 550 | preview : str, enables or disables preview mode and specifies direction 551 | Acceptable inputs are (None, 'inline', 'xline', 'z') 552 | Optimizes chunk size in different orientations to facilitate rapid 553 | screening of algorithm output 554 | 555 | Returns 556 | ------- 557 | result : Dask Array 558 | """ 559 | 560 | def operation(chunk1, chunk2, chunk3): 561 | 562 | out = np.zeros(chunk1.shape) 563 | for i,j in np.ndindex(out.shape[:-1]): 564 | 565 | ints = np.unique(chunk3[i, j, :]) 566 | 567 | for ii in ints: 568 | 569 | idx = np.where(chunk3[i, j, :] == ii)[0] 570 | peak = idx[chunk1[i, j, idx].argmax()] 571 | out[i, j, idx] = chunk2[i, j, peak] 572 | 573 | return(out) 574 | 575 | darray, chunks_init = self.create_array(darray, preview=preview) 576 | env = self.envelope(darray) 577 | troughs = env.map_blocks(util.local_events, comparator=np.less, 578 | dtype=darray.dtype) 579 | troughs = troughs.cumsum(axis=-1) 580 | result = da.map_blocks(operation, env, darray, troughs, dtype=darray.dtype) 581 | result[da.isnan(result)] = 0 582 | 583 | return(result) 584 | 585 | 586 | def apparent_polarity(self, darray, preview=None): 587 | """ 588 | Description 589 | ----------- 590 | Compute the Apparent Polarity of the input data 591 | 592 | Parameters 593 | ---------- 594 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 595 | 596 | Keywork Arguments 597 | ----------------- 598 | preview : str, enables or disables preview mode and specifies direction 599 | Acceptable inputs are (None, 'inline', 'xline', 'z') 600 | Optimizes chunk size in different orientations to facilitate rapid 601 | screening of algorithm output 602 | 603 | Returns 604 | ------- 605 | result : Dask Array 606 | """ 607 | def operation(chunk1, chunk2, chunk3): 608 | 609 | out = np.zeros(chunk1.shape) 610 | for i,j in np.ndindex(out.shape[:-1]): 611 | 612 | ints = np.unique(chunk3[i, j, :]) 613 | 614 | for ii in ints: 615 | 616 | idx = np.where(chunk3[i, j, :] == ii)[0] 617 | peak = idx[chunk1[i, j, idx].argmax()] 618 | out[i, j, idx] = chunk1[i, j, peak] * np.sign(chunk2[i, j, peak]) 619 | 620 | return(out) 621 | 622 | darray, chunks_init = self.create_array(darray, preview=preview) 623 | env = self.envelope(darray) 624 | troughs = env.map_blocks(util.local_events, comparator=np.less, 625 | dtype=darray.dtype) 626 | troughs = troughs.cumsum(axis=-1) 627 | result = da.map_blocks(operation, env, darray, troughs, dtype=darray.dtype) 628 | result[da.isnan(result)] = 0 629 | 630 | return(result) -------------------------------------------------------------------------------- /d2geo/attributes/DipAzm.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Dip & Azimuth Calculations for Seismic Data 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import numpy as np 12 | import dask.array as da 13 | from scipy import ndimage as ndi 14 | import util 15 | from SignalProcess import SignalProcess as sp 16 | 17 | 18 | class DipAzm(): 19 | """ 20 | Description 21 | ----------- 22 | Class object containing methods for computing dip & azimuth attributes 23 | from 3D seismic data. 24 | 25 | Methods 26 | ------- 27 | create_array 28 | gradient_dips 29 | gradient_structure_tensor 30 | gst_2D_dips 31 | gst_3D_dip 32 | gst_3D_azm 33 | """ 34 | 35 | def create_array(self, darray, kernel=None, preview=None): 36 | """ 37 | Description 38 | ----------- 39 | Convert input to Dask Array with ideal chunk size as necessary. Perform 40 | necessary ghosting as needed for opertations utilizing windowed functions. 41 | 42 | Parameters 43 | ---------- 44 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 45 | 46 | Keywork Arguments 47 | ----------------- 48 | kernel : tuple (len 3), operator size 49 | preview : str, enables or disables preview mode and specifies direction 50 | Acceptable inputs are (None, 'inline', 'xline', 'z') 51 | Optimizes chunk size in different orientations to facilitate rapid 52 | screening of algorithm output 53 | 54 | Returns 55 | ------- 56 | darray : Dask Array 57 | chunk_init : tuple (len 3), chunk size before ghosting. Used in select cases 58 | """ 59 | 60 | if not isinstance(darray, da.core.Array): 61 | chunk_size = util.compute_chunk_size(darray.shape, 62 | darray.dtype.itemsize, 63 | kernel=kernel, 64 | preview=preview) 65 | darray = da.from_array(darray, chunks=chunk_size) 66 | chunks_init = darray.chunks 67 | 68 | else: 69 | chunks_init = darray.chunks 70 | 71 | if kernel != None: 72 | hw = tuple(np.array(kernel) // 2) 73 | darray = da.ghost.ghost(darray, depth=hw, boundary='reflect') 74 | 75 | return(darray, chunks_init) 76 | 77 | 78 | def gradient_dips(self, darray, dip_factor=10, kernel=(3,3,3), preview=None): 79 | """ 80 | Description 81 | ----------- 82 | Compute Inline and Crossline Dip from the Inline, Crossline, & Z Gradients 83 | 84 | Parameters 85 | ---------- 86 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 87 | 88 | Keywork Arguments 89 | ----------------- 90 | dip_factor : Number, scalar for dip values 91 | kernel : tuple (len 3), operator size 92 | preview : str, enables or disables preview mode and specifies direction 93 | Acceptable inputs are (None, 'inline', 'xline', 'z') 94 | Optimizes chunk size in different orientations to facilitate rapid 95 | screening of algorithm output 96 | 97 | Returns 98 | ------- 99 | il_dip : Dask Array, Inline Dips 100 | xl_dip : Dask Array, Crossline Dips 101 | """ 102 | 103 | # Generate Dask Array as necessary 104 | darray, chunks_init = self.create_array(darray, kernel=None, preview=preview) 105 | 106 | # Compute I, J, K gradients 107 | gi = sp().first_derivative(darray, axis=0) 108 | gj = sp().first_derivative(darray, axis=1) 109 | gk = sp().first_derivative(darray, axis=2) 110 | 111 | # Compute dips 112 | il_dip = -(gi / gk) * dip_factor 113 | xl_dip = -(gj / gk) * dip_factor 114 | 115 | il_dip[da.isnan(il_dip)] = 0 116 | xl_dip[da.isnan(xl_dip)] = 0 117 | 118 | # Perform smoothing as specified 119 | if kernel != None: 120 | hw = tuple(np.array(kernel) // 2) 121 | il_dip = il_dip.map_overlap(ndi.median_filter, depth=hw, boundary='reflect', 122 | dtype=darray.dtype, size=kernel) 123 | xl_dip = xl_dip.map_overlap(ndi.median_filter, depth=hw, boundary='reflect', 124 | dtype=darray.dtype, size=kernel) 125 | 126 | return(il_dip, xl_dip) 127 | 128 | 129 | def gradient_structure_tensor(self, darray, kernel, preview=None): 130 | """ 131 | Description 132 | ----------- 133 | Convience function to compute the Inner Product of Gradients 134 | 135 | Parameters 136 | ---------- 137 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 138 | kernel : tuple (len 3), operator size 139 | 140 | Keywork Arguments 141 | ----------------- 142 | preview : str, enables or disables preview mode and specifies direction 143 | Acceptable inputs are (None, 'inline', 'xline', 'z') 144 | Optimizes chunk size in different orientations to facilitate rapid 145 | screening of algorithm output 146 | 147 | Returns 148 | ------- 149 | gi2, gj2, gk2, gigj, gigk, gjgk : Dask Array 150 | """ 151 | 152 | # Generate Dask Array as necessary 153 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 154 | 155 | # Compute I, J, K gradients 156 | gi = sp().first_derivative(darray, axis=0) 157 | gj = sp().first_derivative(darray, axis=1) 158 | gk = sp().first_derivative(darray, axis=2) 159 | gi = util.trim_dask_array(gi, kernel) 160 | gj = util.trim_dask_array(gj, kernel) 161 | gk = util.trim_dask_array(gk, kernel) 162 | 163 | # Compute Inner Product of Gradients 164 | hw = tuple(np.array(kernel) // 2) 165 | gi2 = (gi * gi).map_overlap(ndi.uniform_filter, depth=hw, boundary='reflect', 166 | dtype=darray.dtype, size=kernel) 167 | gj2 = (gj * gj).map_overlap(ndi.uniform_filter, depth=hw, boundary='reflect', 168 | dtype=darray.dtype, size=kernel) 169 | gk2 = (gk * gk).map_overlap(ndi.uniform_filter, depth=hw, boundary='reflect', 170 | dtype=darray.dtype, size=kernel) 171 | gigj = (gi * gj).map_overlap(ndi.uniform_filter, depth=hw, boundary='reflect', 172 | dtype=darray.dtype, size=kernel) 173 | gigk = (gi * gk).map_overlap(ndi.uniform_filter, depth=hw, boundary='reflect', 174 | dtype=darray.dtype, size=kernel) 175 | gjgk = (gj * gk).map_overlap(ndi.uniform_filter, depth=hw, boundary='reflect', 176 | dtype=darray.dtype, size=kernel) 177 | 178 | return(gi2, gj2, gk2, gigj, gigk, gjgk) 179 | 180 | 181 | def gst_2D_dips(self, darray, dip_factor=10, kernel=(3,3,3), preview=None): 182 | """ 183 | Description 184 | ----------- 185 | Compute Inline and Crossline Dip from the Gradient Structure Tensor 186 | 187 | Parameters 188 | ---------- 189 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 190 | 191 | Keywork Arguments 192 | ----------------- 193 | dip_factor : Number, scalar for dip values 194 | kernel : tuple (len 3), operator size 195 | preview : str, enables or disables preview mode and specifies direction 196 | Acceptable inputs are (None, 'inline', 'xline', 'z') 197 | Optimizes chunk size in different orientations to facilitate rapid 198 | screening of algorithm output 199 | 200 | Returns 201 | ------- 202 | il_dip : Dask Array, Inline Dips 203 | xl_dip : Dask Array, Crossline Dips 204 | """ 205 | 206 | # Compute dips from Eigenvectors of GST 207 | def operation(gi2, gj2, gk2, gigj, gigk, gjgk, axis): 208 | np.seterr(all='ignore') 209 | 210 | shape = gi2.shape 211 | 212 | gst = np.array([[gi2, gigj, gigk], 213 | [gigj, gj2, gjgk], 214 | [gigk, gjgk, gk2]]) 215 | 216 | gst = np.moveaxis(gst, [0,1], [-2,-1]) 217 | gst = gst.reshape((-1, 3, 3)) 218 | 219 | evals, evecs = np.linalg.eigh(gst) 220 | ndx = evals.argsort() 221 | evecs = evecs[np.arange(0,gst.shape[0],1),:,ndx[:,2]] 222 | 223 | out = -evecs[:, axis] / evecs[:, 2] 224 | out = out.reshape(shape) 225 | 226 | return(out) 227 | 228 | # Compute Inner Product of Gradients and Dips 229 | gi2, gj2, gk2, gigj, gigk, gjgk = self.gradient_structure_tensor(darray, kernel, 230 | preview=preview) 231 | il_dip = da.map_blocks(operation, gi2, gj2, gk2, gigj, gigk, gjgk, axis=0, 232 | dtype=darray.dtype) 233 | xl_dip = da.map_blocks(operation, gi2, gj2, gk2, gigj, gigk, gjgk, axis=1, 234 | dtype=darray.dtype) 235 | 236 | il_dip *= dip_factor 237 | xl_dip *= dip_factor 238 | il_dip[da.isnan(il_dip)] = 0 239 | xl_dip[da.isnan(xl_dip)] = 0 240 | 241 | return(il_dip, xl_dip) 242 | 243 | 244 | def gst_3D_dip(self, darray, dip_factor=10, kernel=(3,3,3), preview=None): 245 | """ 246 | Description 247 | ----------- 248 | Compute 3D Dip from the Gradient Structure Tensor 249 | 250 | Parameters 251 | ---------- 252 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 253 | 254 | Keywork Arguments 255 | ----------------- 256 | dip_factor : Number, scalar for dip values 257 | kernel : tuple (len 3), operator size 258 | preview : str, enables or disables preview mode and specifies direction 259 | Acceptable inputs are (None, 'inline', 'xline', 'z') 260 | Optimizes chunk size in different orientations to facilitate rapid 261 | screening of algorithm output 262 | 263 | Returns 264 | ------- 265 | result : Dask Array, dip in degrees 266 | """ 267 | 268 | # Function to compute 3D dip from GST 269 | def operation(gi2, gj2, gk2, gigj, gigk, gjgk, axis): 270 | np.seterr(all='ignore') 271 | 272 | shape = gi2.shape 273 | 274 | gst = np.array([[gi2, gigj, gigk], 275 | [gigj, gj2, gjgk], 276 | [gigk, gjgk, gk2]]) 277 | 278 | gst = np.moveaxis(gst, [0,1], [-2,-1]) 279 | gst = gst.reshape((-1, 3, 3)) 280 | 281 | evals, evecs = np.linalg.eigh(gst) 282 | ndx = evals.argsort() 283 | evecs = evecs[np.arange(0,gst.shape[0],1),:,ndx[:,2]] 284 | 285 | norm_factor = np.linalg.norm(evecs, axis = -1) 286 | evecs[:, 0] /= norm_factor 287 | evecs[:, 1] /= norm_factor 288 | evecs[:, 2] /= norm_factor 289 | 290 | evecs[evecs[:, 2] < 0] *= np.sign(evecs[evecs[:, 2] < 0]) 291 | 292 | dip = np.dot(evecs, np.array([0,0,1])) 293 | dip = np.arccos(dip) 294 | dip = dip.reshape(shape) 295 | 296 | dip = np.rad2deg(dip)# - 90 297 | 298 | return(dip) 299 | 300 | # Compute Inner Product of Gradients and Dips 301 | gi2, gj2, gk2, gigj, gigk, gjgk = self.gradient_structure_tensor(darray, kernel, 302 | preview=preview) 303 | result = da.map_blocks(operation, gi2, gj2, gk2, gigj, gigk, gjgk, axis=0, 304 | dtype=darray.dtype) 305 | result[da.isnan(result)] = 0 306 | 307 | return(result) 308 | 309 | 310 | def gst_3D_azm(self, darray, dip_factor=10, kernel=(3,3,3), preview=None): 311 | """ 312 | Description 313 | ----------- 314 | Compute 3D Azimuth from the Gradient Structure Tensor 315 | 316 | Parameters 317 | ---------- 318 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 319 | 320 | Keywork Arguments 321 | ----------------- 322 | dip_factor : Number, scalar for dip values 323 | kernel : tuple (len 3), operator size 324 | preview : str, enables or disables preview mode and specifies direction 325 | Acceptable inputs are (None, 'inline', 'xline', 'z') 326 | Optimizes chunk size in different orientations to facilitate rapid 327 | screening of algorithm output 328 | 329 | Returns 330 | ------- 331 | result : Dask Array, azimuth in degrees 332 | """ 333 | 334 | # Function to compute 3D azimuth from GST 335 | def operation(gi2, gj2, gk2, gigj, gigk, gjgk, axis): 336 | np.seterr(all='ignore') 337 | 338 | shape = gi2.shape 339 | 340 | gst = np.array([[gi2, gigj, gigk], 341 | [gigj, gj2, gjgk], 342 | [gigk, gjgk, gk2]]) 343 | 344 | gst = np.moveaxis(gst, [0,1], [-2,-1]) 345 | gst = gst.reshape((-1, 3, 3)) 346 | 347 | evals, evecs = np.linalg.eigh(gst) 348 | ndx = evals.argsort() 349 | evecs = evecs[np.arange(0,gst.shape[0],1),:,ndx[:,2]] 350 | 351 | norm_factor = np.linalg.norm(evecs, axis = -1) 352 | evecs[:, 0] /= norm_factor 353 | evecs[:, 1] /= norm_factor 354 | evecs[:, 2] /= norm_factor 355 | 356 | evecs[evecs[:, 2] < 0] *= np.sign(evecs[evecs[:, 2] < 0]) 357 | 358 | azm = np.arctan2(evecs[:, 0], evecs[:, 1]) 359 | azm = azm.reshape(shape) 360 | azm = np.rad2deg(azm) 361 | azm[azm < 0] += 360 362 | 363 | return(azm) 364 | 365 | # Compute Inner Product of Gradients and Azimuth 366 | gi2, gj2, gk2, gigj, gigk, gjgk = self.gradient_structure_tensor(darray, kernel, 367 | preview=preview) 368 | result = da.map_blocks(operation, gi2, gj2, gk2, gigj, gigk, gjgk, axis=0, 369 | dtype=darray.dtype) 370 | result[da.isnan(result)] = 0 371 | 372 | return(result) 373 | -------------------------------------------------------------------------------- /d2geo/attributes/EdgeDetection.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Edge Detection Attributes for Seismic Data 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | 12 | import dask.array as da 13 | import numpy as np 14 | from scipy import ndimage as ndi 15 | import util 16 | from SignalProcess import SignalProcess as sp 17 | 18 | 19 | 20 | class EdgeDetection(): 21 | """ 22 | Description 23 | ----------- 24 | Class object containing methods for computing edge attributes 25 | from 3D seismic data. 26 | 27 | Methods 28 | ------- 29 | create_array 30 | semblance 31 | eig_complex 32 | gradient_structure_tensor 33 | chaos 34 | volume_curvature 35 | """ 36 | 37 | def create_array(self, darray, kernel, preview): 38 | """ 39 | Description 40 | ----------- 41 | Convert input to Dask Array with ideal chunk size as necessary. Perform 42 | necessary ghosting as needed for opertations utilizing windowed functions. 43 | 44 | Parameters 45 | ---------- 46 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 47 | 48 | Keywork Arguments 49 | ----------------- 50 | kernel : tuple (len 3), operator size 51 | preview : str, enables or disables preview mode and specifies direction 52 | Acceptable inputs are (None, 'inline', 'xline', 'z') 53 | Optimizes chunk size in different orientations to facilitate rapid 54 | screening of algorithm output 55 | 56 | Returns 57 | ------- 58 | darray : Dask Array 59 | chunk_init : tuple (len 3), chunk size before ghosting. Used in select cases 60 | """ 61 | 62 | # Compute chunk size and convert if not a Dask Array 63 | if not isinstance(darray, da.core.Array): 64 | chunk_size = util.compute_chunk_size(darray.shape, 65 | darray.dtype.itemsize, 66 | kernel=kernel, 67 | preview=preview) 68 | darray = da.from_array(darray, chunks=chunk_size) 69 | chunks_init = darray.chunks 70 | 71 | else: 72 | chunks_init = darray.chunks 73 | 74 | # Ghost Dask Array if operation specifies a kernel 75 | if kernel != None: 76 | hw = tuple(np.array(kernel) // 2) 77 | darray = da.overlap.overlap(darray, depth=hw, boundary='reflect') 78 | 79 | return(darray, chunks_init) 80 | 81 | 82 | def semblance(self, darray, kernel=(3,3,9), preview=None): 83 | """ 84 | Description 85 | ----------- 86 | Compute multi-trace semblance from 3D seismic 87 | 88 | Parameters 89 | ---------- 90 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 91 | 92 | Keywork Arguments 93 | ----------------- 94 | kernel : tuple (len 3), operator size 95 | preview : str, enables or disables preview mode and specifies direction 96 | Acceptable inputs are (None, 'inline', 'xline', 'z') 97 | Optimizes chunk size in different orientations to facilitate rapid 98 | screening of algorithm output 99 | 100 | Returns 101 | ------- 102 | result : Dask Array 103 | """ 104 | 105 | # Function to extract patches and perform algorithm 106 | def operation(chunk, kernel): 107 | np.seterr(all='ignore') 108 | x = util.extract_patches(chunk, kernel) 109 | s1 = np.sum(x, axis=(-3,-2)) ** 2 110 | s2 = np.sum(x ** 2, axis=(-3,-2)) 111 | sembl = s1.sum(axis = -1) / s2.sum(axis = -1) 112 | sembl /= kernel[0] * kernel[1] 113 | return(sembl) 114 | 115 | # Generate Dask Array as necessary and perform algorithm 116 | darray, chunks_init = self.create_array(darray, kernel, preview) 117 | result = darray.map_blocks(operation, kernel=kernel, dtype=darray.dtype, chunks=chunks_init) 118 | result[da.isnan(result)] = 0 119 | 120 | return(result) 121 | 122 | 123 | 124 | def gradient_structure_tensor(self, darray, kernel=(3,3,9), preview=None): 125 | """ 126 | Description 127 | ----------- 128 | Compute discontinuity from eigenvalues of gradient structure tensor 129 | 130 | Parameters 131 | ---------- 132 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 133 | 134 | Keywork Arguments 135 | ----------------- 136 | kernel : tuple (len 3), operator size 137 | preview : str, enables or disables preview mode and specifies direction 138 | Acceptable inputs are (None, 'inline', 'xline', 'z') 139 | Optimizes chunk size in different orientations to facilitate rapid 140 | screening of algorithm output 141 | 142 | Returns 143 | ------- 144 | result : Dask Array 145 | """ 146 | 147 | # Function to extract patches and perform algorithm 148 | def operation(gi2, gj2, gk2, gigj, gigk, gjgk): 149 | np.seterr(all='ignore') 150 | 151 | chunk_shape = gi2.shape 152 | 153 | gst = np.array([[gi2, gigj, gigk], 154 | [gigj, gj2, gjgk], 155 | [gigk, gjgk, gk2]]) 156 | 157 | gst = np.moveaxis(gst, [0,1], [-2,-1]) 158 | gst = gst.reshape((-1, 3, 3)) 159 | 160 | eigs = np.sorgst(np.linalg.eigvalsh(gst)) 161 | e1 = eigs[:, 2].reshape(chunk_shape) 162 | e2 = eigs[:, 1].reshape(chunk_shape) 163 | e3 = eigs[:, 0].reshape(chunk_shape) 164 | 165 | # Compute cvals from Eigenvalues 166 | cline = (e2 - e3) / (e2 + e3) 167 | cplane = (e1 - e2) / (e1 + e2) 168 | cfault = cline * (1 - cplane) 169 | 170 | return(cfault) 171 | 172 | # Generate Dask Array as necessary 173 | darray, chunks_init = self.create_array(darray, kernel, preview) 174 | 175 | # Compute I, J, K gradients 176 | gi = sp().first_derivative(darray, axis=0) 177 | gj = sp().first_derivative(darray, axis=1) 178 | gk = sp().first_derivative(darray, axis=2) 179 | 180 | # Compute the Inner Product of the Gradients 181 | gi2 = (gi * gi).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 182 | gj2 = (gj * gj).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 183 | gk2 = (gk * gk).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 184 | gigj = (gi * gj).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 185 | gigk = (gi * gk).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 186 | gjgk = (gj * gk).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 187 | 188 | result = da.map_blocks(operation, gi2, gj2, gk2, gigj, gigk, gjgk, 189 | dtype=darray.dtype) 190 | result = util.trim_dask_array(result, kernel) 191 | result[da.isnan(result)] = 0 192 | 193 | return(result) 194 | 195 | 196 | def eig_complex(self, darray, kernel=(3,3,9), preview=None): 197 | """ 198 | Description 199 | ----------- 200 | Compute multi-trace semblance from 3D seismic incorporating the 201 | analytic trace 202 | 203 | Parameters 204 | ---------- 205 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 206 | 207 | Keywork Arguments 208 | ----------------- 209 | kernel : tuple (len 3), operator size 210 | preview : str, enables or disables preview mode and specifies direction 211 | Acceptable inputs are (None, 'inline', 'xline', 'z') 212 | Optimizes chunk size in different orientations to facilitate rapid 213 | screening of algorithm output 214 | 215 | Returns 216 | ------- 217 | result : Dask Array 218 | """ 219 | 220 | # Function to compute the COV 221 | def cov(x, ki, kj, kk): 222 | x = x.reshape((ki * kj, kk)) 223 | x = np.hstack([x.real, x.imag]) 224 | return(x.dot(x.T)) 225 | 226 | # Function to extract patches and perform algorithm 227 | def operation(chunk, kernel): 228 | np.seterr(all='ignore') 229 | ki, kj, kk = kernel 230 | patches = util.extract_patches(chunk, kernel) 231 | 232 | out_data = [] 233 | for i in range(0, patches.shape[0]): 234 | traces = patches[i] 235 | traces = traces.reshape(-1, ki * kj * kk) 236 | cov = np.apply_along_axis(cov, 1, traces, ki, kj, kk) 237 | vals = np.linalg.eigvals(cov) 238 | vals = np.abs(vals.max(axis=1) / vals.sum(axis=1)) 239 | 240 | out_data.append(vals) 241 | 242 | out_data = np.asarray(out_data).reshape(patches.shape[:3]) 243 | 244 | return(out_data) 245 | 246 | # Generate Dask Array as necessary and perform algorithm 247 | darray, chunks_init = self.create_array(darray, kernel, preview) 248 | hilbert = darray.map_blocks(util.hilbert, dtype=darray.dtype) 249 | result = hilbert.map_blocks(operation, kernel=kernel, dtype=darray.dtype) 250 | result = util.trim_dask_array(result, kernel) 251 | result[da.isnan(result)] = 0 252 | 253 | return(result) 254 | 255 | 256 | def chaos(self, darray, kernel=(3,3,9), preview=None): 257 | """ 258 | Description 259 | ----------- 260 | Compute multi-trace chaos from 3D seismic 261 | 262 | Parameters 263 | ---------- 264 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 265 | 266 | Keywork Arguments 267 | ----------------- 268 | kernel : tuple (len 3), operator size 269 | preview : str, enables or disables preview mode and specifies direction 270 | Acceptable inputs are (None, 'inline', 'xline', 'z') 271 | Optimizes chunk size in different orientations to facilitate rapid 272 | screening of algorithm output 273 | 274 | Returns 275 | ------- 276 | result : Dask Array 277 | """ 278 | 279 | # Function to extract patches and perform algorithm 280 | def operation(gi2, gj2, gk2, gigj, gigk, gjgk): 281 | np.seterr(all='ignore') 282 | 283 | chunk_shape = gi2.shape 284 | 285 | gst = np.array([[gi2, gigj, gigk], 286 | [gigj, gj2, gjgk], 287 | [gigk, gjgk, gk2]]) 288 | 289 | gst = np.moveaxis(gst, [0,1], [-2,-1]) 290 | gst = gst.reshape((-1, 3, 3)) 291 | 292 | eigs = np.sort(np.linalg.eigvalsh(gst)) 293 | e1 = eigs[:, 2].reshape(chunk_shape) 294 | e2 = eigs[:, 1].reshape(chunk_shape) 295 | e3 = eigs[:, 0].reshape(chunk_shape) 296 | 297 | out = (2 * e2) / (e1 + e3) 298 | 299 | return(out) 300 | 301 | # Generate Dask Array as necessary 302 | darray, chunks_init = self.create_array(darray, kernel, preview) 303 | 304 | # Compute I, J, K gradients 305 | gi = sp().first_derivative(darray, axis=0) 306 | gj = sp().first_derivative(darray, axis=1) 307 | gk = sp().first_derivative(darray, axis=2) 308 | 309 | # Compute the Inner Product of the Gradients 310 | gi2 = (gi * gi).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 311 | gj2 = (gj * gj).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 312 | gk2 = (gk * gk).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 313 | gigj = (gi * gj).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 314 | gigk = (gi * gk).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 315 | gjgk = (gj * gk).map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 316 | 317 | result = da.map_blocks(operation, gi2, gj2, gk2, gigj, gigk, gjgk, 318 | dtype=darray.dtype) 319 | result = util.trim_dask_array(result, kernel) 320 | result[da.isnan(result)] = 0 321 | 322 | return(result) 323 | 324 | 325 | def volume_curvature(self, darray_il, darray_xl, dip_factor=10, kernel=(3,3,3), 326 | preview=None): 327 | """ 328 | Description 329 | ----------- 330 | Compute volume curvature attributes from 3D seismic dips 331 | 332 | Parameters 333 | ---------- 334 | darray_il : Array-like, Inline dip - acceptable inputs include 335 | Numpy, HDF5, or Dask Arrays 336 | darray_xl : Array-like, Crossline dip - acceptable inputs include 337 | Numpy, HDF5, or Dask Arrays 338 | 339 | Keywork Arguments 340 | ----------------- 341 | dip_factor : Number, scalar for dip values 342 | kernel : tuple (len 3), operator size 343 | preview : str, enables or disables preview mode and specifies direction 344 | Acceptable inputs are (None, 'inline', 'xline', 'z') 345 | Optimizes chunk size in different orientations to facilitate rapid 346 | screening of algorithm output 347 | 348 | Returns 349 | ------- 350 | H, K, Kmax, Kmin, KMPos, KMNeg : Dask Array, {H : 'Mean Curvature', 351 | K : 'Gaussian Curvature', 352 | Kmax : 'Max Curvature', 353 | Kmin : 'Min Curvature', 354 | KMPos : Most Positive Curvature, 355 | KMNeg : Most Negative Curvature} 356 | """ 357 | 358 | np.seterr(all='ignore') 359 | 360 | # Generate Dask Array as necessary 361 | darray_il, chunks_init = self.create_array(darray_il, kernel, preview=preview) 362 | darray_xl, chunks_init = self.create_array(darray_xl, kernel, preview=preview) 363 | 364 | u = -darray_il / dip_factor 365 | v = -darray_xl / dip_factor 366 | w = da.ones_like(u, chunks=u.chunks) 367 | 368 | # Compute Gradients 369 | ux = sp().first_derivative(u, axis=0) 370 | uy = sp().first_derivative(u, axis=1) 371 | uz = sp().first_derivative(u, axis=2) 372 | vx = sp().first_derivative(v, axis=0) 373 | vy = sp().first_derivative(v, axis=1) 374 | vz = sp().first_derivative(v, axis=2) 375 | 376 | # Smooth Gradients 377 | ux = ux.map_blocks(ndi.uniform_filter, size=kernel, dtype=ux.dtype) 378 | uy = uy.map_blocks(ndi.uniform_filter, size=kernel, dtype=ux.dtype) 379 | uz = uz.map_blocks(ndi.uniform_filter, size=kernel, dtype=ux.dtype) 380 | vx = vx.map_blocks(ndi.uniform_filter, size=kernel, dtype=ux.dtype) 381 | vy = vy.map_blocks(ndi.uniform_filter, size=kernel, dtype=ux.dtype) 382 | vz = vz.map_blocks(ndi.uniform_filter, size=kernel, dtype=ux.dtype) 383 | 384 | u = util.trim_dask_array(u, kernel) 385 | v = util.trim_dask_array(v, kernel) 386 | w = util.trim_dask_array(w, kernel) 387 | ux = util.trim_dask_array(ux, kernel) 388 | uy = util.trim_dask_array(uy, kernel) 389 | uz = util.trim_dask_array(uz, kernel) 390 | vx = util.trim_dask_array(vx, kernel) 391 | vy = util.trim_dask_array(vy, kernel) 392 | vz = util.trim_dask_array(vz, kernel) 393 | 394 | wx = da.zeros_like(ux, chunks=ux.chunks, dtype=ux.dtype) 395 | wy = da.zeros_like(ux, chunks=ux.chunks, dtype=ux.dtype) 396 | wz = da.zeros_like(ux, chunks=ux.chunks, dtype=ux.dtype) 397 | 398 | uv = u * v 399 | vw = v * w 400 | u2 = u * u 401 | v2 = v * v 402 | w2 = w * w 403 | u2pv2 = u2 + v2 404 | v2pw2 = v2 + w2 405 | s = da.sqrt(u2pv2 + w2) 406 | 407 | # Measures of surfaces 408 | E = da.ones_like(u, chunks=u.chunks, dtype=u.dtype) 409 | F = -(u * w) / (da.sqrt(u2pv2) * da.sqrt(v2pw2)) 410 | G = da.ones_like(u, chunks=u.chunks, dtype=u.dtype) 411 | D = -(-uv * vx+u2 * vy + v2 * ux - uv * uy) / (u2pv2 * s) 412 | Di = -(vw * (uy + vx) - 2 * u * w * vy - v2 * (uz + wx) + uv * (vz + wy)) / (2 * da.sqrt(u2pv2) * da.sqrt(v2pw2) * s) 413 | Dii = -(-vw * wy + v2 * wz + w2 * vy - vw * vz) / (v2pw2 *s) 414 | H = (E * Dii - 2 * F *Di + G * D) / (2 * (E * G - F * F)) 415 | K = (D * Dii - Di * Di) / (E * G - F * F) 416 | Kmin = H - da.sqrt(H * H - K) 417 | Kmax = H + da.sqrt(H * H - K) 418 | 419 | H[da.isnan(H)] = 0 420 | K[da.isnan(K)] = 0 421 | Kmax[da.isnan(Kmax)] = 0 422 | Kmin[da.isnan(Kmin)] = 0 423 | 424 | KMPos = da.maximum(Kmax, Kmin) 425 | KMNeg = da.minimum(Kmax, Kmin) 426 | 427 | return(H, K, Kmax, Kmin, KMPos, KMNeg) 428 | -------------------------------------------------------------------------------- /d2geo/attributes/Frequency.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Frequency attributes for Seismic data 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import dask.array as da 12 | import numpy as np 13 | from scipy import signal 14 | import util 15 | 16 | 17 | 18 | class Frequency(): 19 | """ 20 | Description 21 | ----------- 22 | Class object containing methods for performing frequency filtering 23 | 24 | Methods 25 | ------- 26 | create_array 27 | lowpass_filter 28 | highpass_filter 29 | bandpass_filter 30 | cwt_ricker 31 | cwt_ormsby 32 | """ 33 | 34 | def create_array(self, darray, kernel, preview): 35 | """ 36 | Description 37 | ----------- 38 | Convert input to Dask Array with ideal chunk size as necessary. Perform 39 | necessary ghosting as needed for opertations utilizing windowed functions. 40 | 41 | Parameters 42 | ---------- 43 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 44 | 45 | Keywork Arguments 46 | ----------------- 47 | kernel : tuple (len 3), operator size 48 | preview : str, enables or disables preview mode and specifies direction 49 | Acceptable inputs are (None, 'inline', 'xline', 'z') 50 | Optimizes chunk size in different orientations to facilitate rapid 51 | screening of algorithm output 52 | 53 | Returns 54 | ------- 55 | darray : Dask Array 56 | chunk_init : tuple (len 3), chunk size before ghosting. Used in select cases 57 | """ 58 | 59 | # Compute chunk size and convert if not a Dask Array 60 | if not isinstance(darray, da.core.Array): 61 | chunk_size = util.compute_chunk_size(darray.shape, 62 | darray.dtype.itemsize, 63 | kernel=kernel, 64 | preview=preview) 65 | darray = da.from_array(darray, chunks=chunk_size) 66 | chunks_init = darray.chunks 67 | 68 | else: 69 | chunks_init = darray.chunks 70 | 71 | # Ghost Dask Array if operation specifies a kernel 72 | if kernel != None: 73 | hw = tuple(np.array(kernel) // 2) 74 | darray = da.ghost.ghost(darray, depth=hw, boundary='reflect') 75 | 76 | return(darray, chunks_init) 77 | 78 | 79 | def lowpass_filter(self, darray, freq, sample_rate=4, preview=None): 80 | """ 81 | Description 82 | ----------- 83 | Perform low pass filtering of 3D seismic data 84 | 85 | Parameters 86 | ---------- 87 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 88 | freq : Number (Hz), frequency cutoff used in filter 89 | 90 | Keywork Arguments 91 | ----------------- 92 | sample_rate : Number, sample rate in milliseconds (ms) 93 | preview : str, enables or disables preview mode and specifies direction 94 | Acceptable inputs are (None, 'inline', 'xline', 'z') 95 | Optimizes chunk size in different orientations to facilitate rapid 96 | screening of algorithm output 97 | 98 | Returns 99 | ------- 100 | result : Dask Array 101 | """ 102 | 103 | # Filtering Function 104 | def filt(chunk, B, A): 105 | 106 | out = signal.filtfilt(B, A, x=chunk) 107 | 108 | return(out) 109 | 110 | # Generate Dask Array as necessary and perform algorithm 111 | darray, chunks_init = self.create_array(darray, kernel=None, 112 | preview=preview) 113 | fs = 1000 / sample_rate 114 | nyq = fs * 0.5 115 | B, A = signal.butter(6, freq/nyq, btype='lowpass', analog=False) 116 | result = darray.map_blocks(filt, B, A, dtype=darray.dtype) 117 | 118 | return(result) 119 | 120 | def highpass_filter(self, darray, freq, sample_rate=4, preview=None): 121 | """ 122 | Description 123 | ----------- 124 | Perform high pass filtering of 3D seismic data 125 | 126 | Parameters 127 | ---------- 128 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 129 | freq : Number (Hz), frequency cutoff used in filter 130 | 131 | Keywork Arguments 132 | ----------------- 133 | sample_rate : Number, sample rate in milliseconds (ms) 134 | preview : str, enables or disables preview mode and specifies direction 135 | Acceptable inputs are (None, 'inline', 'xline', 'z') 136 | Optimizes chunk size in different orientations to facilitate rapid 137 | screening of algorithm output 138 | 139 | Returns 140 | ------- 141 | result : Dask Array 142 | """ 143 | 144 | # Filtering Function 145 | def filt(chunk, B, A): 146 | 147 | out = signal.filtfilt(B, A, x=chunk) 148 | 149 | return(out) 150 | 151 | # Generate Dask Array as necessary and perform algorithm 152 | darray, chunks_init = self.create_array(darray, kernel=None, 153 | preview=preview) 154 | fs = 1000 / sample_rate 155 | nyq = fs * 0.5 156 | B, A = signal.butter(6, freq/nyq, btype='highpass', analog=False) 157 | result = darray.map_blocks(filt, B, A, dtype=darray.dtype) 158 | 159 | return(result) 160 | 161 | 162 | def bandpass_filter(self, darray, freq_lp, freq_hp, sample_rate=4, preview=None): 163 | """ 164 | Description 165 | ----------- 166 | Perform bandpass filtering of 3D seismic data 167 | 168 | Parameters 169 | ---------- 170 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 171 | freq_lp : Number (Hz), frequency cutoff used in low pass filter 172 | freq_hp : Number (Hz), frequency cutoff used in high pass filter 173 | 174 | Keywork Arguments 175 | ----------------- 176 | sample_rate : Number, sample rate in milliseconds (ms) 177 | preview : str, enables or disables preview mode and specifies direction 178 | Acceptable inputs are (None, 'inline', 'xline', 'z') 179 | Optimizes chunk size in different orientations to facilitate rapid 180 | screening of algorithm output 181 | 182 | Returns 183 | ------- 184 | result : Dask Array 185 | """ 186 | 187 | # Filtering Function 188 | def filt(chunk, B, A): 189 | 190 | out = signal.filtfilt(B, A, x=chunk) 191 | 192 | return(out) 193 | 194 | # Generate Dask Array as necessary and perform algorithm 195 | darray, chunks_init = self.create_array(darray, kernel=None, 196 | preview=preview) 197 | fs = 1000 / sample_rate 198 | nyq = fs * 0.5 199 | B, A = signal.butter(6, (freq_lp/nyq, freq_hp/nyq), btype='bandpass', analog=False) 200 | result = darray.map_blocks(filt, B, A, dtype=darray.dtype) 201 | 202 | return(result) 203 | 204 | 205 | def cwt_ricker(self, darray, freq, sample_rate=4, preview=None): 206 | """ 207 | Description 208 | ----------- 209 | Perform Continuous Wavelet Transform using Ricker Wavelet 210 | 211 | Parameters 212 | ---------- 213 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 214 | freq : Number (Hz), frequency defining Ricker Wavelet 215 | 216 | Keywork Arguments 217 | ----------------- 218 | sample_rate : Number, sample rate in milliseconds (ms) 219 | preview : str, enables or disables preview mode and specifies direction 220 | Acceptable inputs are (None, 'inline', 'xline', 'z') 221 | Optimizes chunk size in different orientations to facilitate rapid 222 | screening of algorithm output 223 | 224 | Returns 225 | ------- 226 | result : Dask Array 227 | """ 228 | 229 | # Generate wavelet of specified frequency 230 | def wavelet(freq, sample_rate): 231 | 232 | sr = sample_rate / 1000 233 | t = np.arange(-0.512 / 2, 0.512 / 2, sr) 234 | out = (1 - (2 * (np.pi * freq * t) ** 2)) * np.exp(-(np.pi * freq * t) ** 2) 235 | 236 | return(out) 237 | 238 | # Convolve wavelet with trace 239 | def convolve(chunk, w): 240 | 241 | out = np.zeros(chunk.shape) 242 | 243 | for i,j in np.ndindex(chunk.shape[:-1]): 244 | out[i, j] = signal.fftconvolve(chunk[i, j], w, mode='same') 245 | 246 | return(out) 247 | 248 | # Generate Dask Array as necessary and perform algorithm 249 | darray, chunks_init = self.create_array(darray, kernel=None, 250 | preview=preview) 251 | w = wavelet(freq, sample_rate) 252 | result = darray.map_blocks(convolve, w=w, dtype=darray.dtype) 253 | 254 | return(result) 255 | 256 | 257 | def cwt_ormsby(self, darray, freqs, sample_rate=4, preview=None): 258 | """ 259 | Description 260 | ----------- 261 | Perform Continuous Wavelet Transform using Ormsby Wavelet 262 | 263 | Parameters 264 | ---------- 265 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 266 | freq : tuple (len 4), frequency cutoff used in filter 267 | 268 | Keywork Arguments 269 | ----------------- 270 | sample_rate : Number, sample rate in milliseconds (ms) 271 | preview : str, enables or disables preview mode and specifies direction 272 | Acceptable inputs are (None, 'inline', 'xline', 'z') 273 | Optimizes chunk size in different orientations to facilitate rapid 274 | screening of algorithm output 275 | 276 | Returns 277 | ------- 278 | result : Dask Array 279 | """ 280 | # Generate wavelet of specified frequencyies 281 | def wavelet(freqs, sample_rate): 282 | 283 | f1, f2, f3, f4 = freqs 284 | sr = sample_rate / 1000 285 | t = np.arange(-0.512 / 2, 0.512 / 2, sr) 286 | 287 | term1 = (((np.pi * f4) ** 2) / ((np.pi * f4) - (np.pi * f3))) * np.sinc(np.pi * f4 * t) ** 2 288 | term2 = (((np.pi * f3) ** 2) / ((np.pi * f4) - (np.pi * f3))) * np.sinc(np.pi * f3 * t) ** 2 289 | term3 = (((np.pi * f2) ** 2) / ((np.pi * f2) - (np.pi * f1))) * np.sinc(np.pi * f2 * t) ** 2 290 | term4 = (((np.pi * f1) ** 2) / ((np.pi * f2) - (np.pi * f1))) * np.sinc(np.pi * f1 * t) ** 2 291 | 292 | out = (term1 - term2) - (term3 - term4) 293 | 294 | return(out) 295 | 296 | # Convolve wavelet with trace 297 | def convolve(chunk, w): 298 | 299 | out = np.zeros(chunk.shape) 300 | 301 | for i,j in np.ndindex(chunk.shape[:-1]): 302 | out[i, j] = signal.fftconvolve(chunk[i, j], w, mode='same') 303 | 304 | return(out) 305 | 306 | # Generate Dask Array as necessary and perform algorithm 307 | darray, chunks_init = self.create_array(darray, kernel=None, 308 | preview=preview) 309 | w = wavelet(freqs, sample_rate) 310 | result = darray.map_blocks(convolve, w=w, dtype=darray.dtype) 311 | 312 | return(result) -------------------------------------------------------------------------------- /d2geo/attributes/NoiseReduction.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Noise Reduction attributes for Seismic Data 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import dask.array as da 12 | import numpy as np 13 | from scipy import ndimage as ndi 14 | import util 15 | 16 | 17 | 18 | class NoiseReduction(): 19 | """ 20 | Description 21 | ----------- 22 | Class object containing methods for reducing noise in 3D seismic 23 | 24 | Methods 25 | ------- 26 | create_array 27 | gaussian 28 | median 29 | convolution 30 | """ 31 | 32 | def create_array(self, darray, kernel, preview): 33 | """ 34 | Description 35 | ----------- 36 | Convert input to Dask Array with ideal chunk size as necessary. Perform 37 | necessary ghosting as needed for opertations utilizing windowed functions. 38 | 39 | Parameters 40 | ---------- 41 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 42 | 43 | Keywork Arguments 44 | ----------------- 45 | kernel : tuple (len 3), operator size 46 | preview : str, enables or disables preview mode and specifies direction 47 | Acceptable inputs are (None, 'inline', 'xline', 'z') 48 | Optimizes chunk size in different orientations to facilitate rapid 49 | screening of algorithm output 50 | 51 | Returns 52 | ------- 53 | darray : Dask Array 54 | chunk_init : tuple (len 3), chunk size before ghosting. Used in select cases 55 | """ 56 | 57 | # Compute chunk size and convert if not a Dask Array 58 | if not isinstance(darray, da.core.Array): 59 | chunk_size = util.compute_chunk_size(darray.shape, 60 | darray.dtype.itemsize, 61 | kernel=kernel, 62 | preview=preview) 63 | darray = da.from_array(darray, chunks=chunk_size) 64 | chunks_init = darray.chunks 65 | 66 | else: 67 | chunks_init = darray.chunks 68 | 69 | # Ghost Dask Array if operation specifies a kernel 70 | if kernel != None: 71 | hw = tuple(np.array(kernel) // 2) 72 | darray = da.ghost.ghost(darray, depth=hw, boundary='reflect') 73 | 74 | return(darray, chunks_init) 75 | 76 | 77 | def gaussian(self, darray, sigmas=(1, 1, 1), preview=None): 78 | """ 79 | Description 80 | ----------- 81 | Perform gaussian smoothing of input seismic 82 | 83 | Parameters 84 | ---------- 85 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 86 | 87 | Keywork Arguments 88 | ----------------- 89 | sigmas : tuple (len 3), smoothing parameters in I, J, K 90 | preview : str, enables or disables preview mode and specifies direction 91 | Acceptable inputs are (None, 'inline', 'xline', 'z') 92 | Optimizes chunk size in different orientations to facilitate rapid 93 | screening of algorithm output 94 | 95 | Returns 96 | ------- 97 | result : Dask Array 98 | """ 99 | 100 | # Generate Dask Array as necessary and perform algorithm 101 | kernel = tuple((np.array(sigmas) * 2.5).astype(int)) 102 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 103 | result = darray.map_blocks(ndi.gaussian_filter, sigma=sigmas, dtype=darray.dtype) 104 | result = util.trim_dask_array(result, kernel) 105 | result[da.isnan(result)] = 0 106 | 107 | return(result) 108 | 109 | 110 | def median(self, darray, kernel=(3, 3, 3), preview=None): 111 | """ 112 | Description 113 | ----------- 114 | Perform median smoothing of input seismic data 115 | 116 | Parameters 117 | ---------- 118 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 119 | 120 | Keywork Arguments 121 | ----------------- 122 | kernel : tuple (len 3), operator size in I, J, K 123 | preview : str, enables or disables preview mode and specifies direction 124 | Acceptable inputs are (None, 'inline', 'xline', 'z') 125 | Optimizes chunk size in different orientations to facilitate rapid 126 | screening of algorithm output 127 | 128 | Returns 129 | ------- 130 | result : Dask Array 131 | """ 132 | 133 | # Generate Dask Array as necessary and perform algorithm 134 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 135 | result = darray.map_blocks(ndi.median_filter, size=kernel, dtype=darray.dtype) 136 | result = util.trim_dask_array(result, kernel) 137 | result[da.isnan(result)] = 0 138 | 139 | return(result) 140 | 141 | def convolution(self, darray, kernel=(3, 3, 3), preview=None): 142 | """ 143 | Description 144 | ----------- 145 | Perform convolution smoothing of input seismic data 146 | 147 | Parameters 148 | ---------- 149 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 150 | 151 | Keywork Arguments 152 | ----------------- 153 | kernel : tuple (len 3), operator size in I, J, K 154 | preview : str, enables or disables preview mode and specifies direction 155 | Acceptable inputs are (None, 'inline', 'xline', 'z') 156 | Optimizes chunk size in different orientations to facilitate rapid 157 | screening of algorithm output 158 | 159 | Returns 160 | ------- 161 | result : Dask Array 162 | """ 163 | 164 | # Generate Dask Array as necessary and perform algorithm 165 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 166 | result = darray.map_blocks(ndi.uniform_filter, size=kernel, dtype=darray.dtype) 167 | result = util.trim_dask_array(result, kernel) 168 | result[da.isnan(result)] = 0 169 | 170 | return(result) 171 | 172 | -------------------------------------------------------------------------------- /d2geo/attributes/SignalProcess.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Various signal processing attributes for Seismic Data 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import dask.array as da 12 | import numpy as np 13 | from scipy import ndimage as ndi 14 | from scipy import signal 15 | import util 16 | 17 | 18 | 19 | class SignalProcess(): 20 | """ 21 | Description 22 | ----------- 23 | Class object containing methods for performing various Signal Processing 24 | algorithms 25 | 26 | Methods 27 | ------- 28 | create_array 29 | first_derivative 30 | second_derivative 31 | histogram_equalization 32 | time_gain 33 | rescale_amplitude_range 34 | rms 35 | trace_agc 36 | gradient_magnitude 37 | reflection_intensity 38 | phase_rotation 39 | """ 40 | 41 | def create_array(self, darray, kernel, preview): 42 | """ 43 | Description 44 | ----------- 45 | Convert input to Dask Array with ideal chunk size as necessary. Perform 46 | necessary ghosting as needed for opertations utilizing windowed functions. 47 | 48 | Parameters 49 | ---------- 50 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 51 | 52 | Keywork Arguments 53 | ----------------- 54 | kernel : tuple (len 3), operator size 55 | preview : str, enables or disables preview mode and specifies direction 56 | Acceptable inputs are (None, 'inline', 'xline', 'z') 57 | Optimizes chunk size in different orientations to facilitate rapid 58 | screening of algorithm output 59 | 60 | Returns 61 | ------- 62 | darray : Dask Array 63 | chunk_init : tuple (len 3), chunk size before ghosting. Used in select cases 64 | """ 65 | 66 | # Compute chunk size and convert if not a Dask Array 67 | if not isinstance(darray, da.core.Array): 68 | chunk_size = util.compute_chunk_size(darray.shape, 69 | darray.dtype.itemsize, 70 | kernel=kernel, 71 | preview=preview) 72 | darray = da.from_array(darray, chunks=chunk_size) 73 | chunks_init = darray.chunks 74 | 75 | else: 76 | chunks_init = darray.chunks 77 | 78 | # Ghost Dask Array if operation specifies a kernel 79 | if kernel != None: 80 | hw = tuple(np.array(kernel) // 2) 81 | darray = da.ghost.ghost(darray, depth=hw, boundary='reflect') 82 | 83 | return(darray, chunks_init) 84 | 85 | 86 | def first_derivative(self, darray, axis=-1, preview=None): 87 | """ 88 | Description 89 | ----------- 90 | Compute first derivative of seismic data in specified direction 91 | 92 | Parameters 93 | ---------- 94 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 95 | 96 | Keywork Arguments 97 | ----------------- 98 | axis : Number, axis dimension 99 | preview : str, enables or disables preview mode and specifies direction 100 | Acceptable inputs are (None, 'inline', 'xline', 'z') 101 | Optimizes chunk size in different orientations to facilitate rapid 102 | screening of algorithm output 103 | 104 | Returns 105 | ------- 106 | result : Dask Array 107 | """ 108 | 109 | kernel = (3,3,3) 110 | axes = [ax for ax in range(darray.ndim) if ax != axis] 111 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 112 | result0 = darray.map_blocks(ndi.correlate1d, weights=[-0.5, 0, 0.5], 113 | axis=axis, dtype=darray.dtype) 114 | result1 = result0.map_blocks(ndi.correlate1d, weights=[0.178947,0.642105,0.178947], 115 | axis=axes[0], dtype=darray.dtype) 116 | result2 = result1.map_blocks(ndi.correlate1d, weights=[0.178947,0.642105,0.178947], 117 | axis=axes[1], dtype=darray.dtype) 118 | result = util.trim_dask_array(result2, kernel) 119 | 120 | return(result) 121 | 122 | 123 | def second_derivative(self, darray, axis=-1, preview=None): 124 | """ 125 | Description 126 | ----------- 127 | Compute second derivative of seismic data in specified direction 128 | 129 | Parameters 130 | ---------- 131 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 132 | 133 | Keywork Arguments 134 | ----------------- 135 | axis : Number, axis dimension 136 | preview : str, enables or disables preview mode and specifies direction 137 | Acceptable inputs are (None, 'inline', 'xline', 'z') 138 | Optimizes chunk size in different orientations to facilitate rapid 139 | screening of algorithm output 140 | 141 | Returns 142 | ------- 143 | result : Dask Array 144 | """ 145 | 146 | kernel = (5,5,5) 147 | axes = [ax for ax in range(darray.ndim) if ax != axis] 148 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 149 | result0 = darray.map_blocks(ndi.correlate1d, weights=[0.232905, 0.002668, -0.471147, 0.002668, 0.232905], 150 | axis=axis, dtype=darray.dtype) 151 | result1 = result0.map_blocks(ndi.correlate1d, weights=[0.030320, 0.249724, 0.439911, 0.249724, 0.030320], 152 | axis=axes[0], dtype=darray.dtype) 153 | result2 = result1.map_blocks(ndi.correlate1d, weights=[0.030320, 0.249724, 0.439911, 0.249724, 0.030320], 154 | axis=axes[1], dtype=darray.dtype) 155 | result = util.trim_dask_array(result2, kernel) 156 | 157 | return(result) 158 | 159 | 160 | def histogram_equalization(self, darray, preview=None): 161 | """ 162 | Description 163 | ----------- 164 | Perform histogram equalization of seismic data 165 | 166 | Parameters 167 | ---------- 168 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 169 | 170 | Keywork Arguments 171 | ----------------- 172 | preview : str, enables or disables preview mode and specifies direction 173 | Acceptable inputs are (None, 'inline', 'xline', 'z') 174 | Optimizes chunk size in different orientations to facilitate rapid 175 | screening of algorithm output 176 | 177 | Returns 178 | ------- 179 | result : Dask Array 180 | """ 181 | 182 | # Function to interpolate seismic to new scaling 183 | def interp(chunk, cdf, bins): 184 | 185 | out = np.interp(chunk.ravel(), bins, cdf) 186 | 187 | return(out.reshape(chunk.shape)) 188 | 189 | darray, chunks_init = self.create_array(darray, preview=preview) 190 | hist, bins = da.histogram(darray, bins=np.linspace(darray.min(), darray.max(), 191 | 256, dtype=darray.dtype)) 192 | cdf = hist.cumsum(axis=-1) 193 | cdf = cdf / cdf[-1] 194 | bins = (bins[:-1] + bins[1:]) / 2 195 | 196 | result = darray.map_blocks(interp, cdf=cdf, bins=bins, dtype=darray.dtype) 197 | 198 | return(result) 199 | 200 | 201 | def time_gain(self, darray, gain_val=1.5, preview=None): 202 | """ 203 | Description 204 | ----------- 205 | Gain the amplitudes in the Z/K dimension 206 | 207 | Parameters 208 | ---------- 209 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 210 | 211 | Keywork Arguments 212 | ----------------- 213 | gain_val : Float, exponential value 214 | preview : str, enables or disables preview mode and specifies direction 215 | Acceptable inputs are (None, 'inline', 'xline', 'z') 216 | Optimizes chunk size in different orientations to facilitate rapid 217 | screening of algorithm output 218 | 219 | Returns 220 | ------- 221 | result : Dask Array 222 | """ 223 | 224 | darray, chunks_init = self.create_array(darray, preview=preview) 225 | z_ind = da.ones(darray.shape, chunks=darray.chunks).cumsum(axis=-1) 226 | gain = (1 + z_ind) ** gain_val 227 | 228 | result = darray * gain 229 | 230 | return(result) 231 | 232 | 233 | 234 | def rescale_amplitude_range(self, darray, min_val, max_val, preview=None): 235 | """ 236 | Description 237 | ----------- 238 | Clip the seismic data to specified values 239 | 240 | Parameters 241 | ---------- 242 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 243 | min_val : Number, min clip value 244 | max_val : Numer, max clip value 245 | 246 | Keywork Arguments 247 | ----------------- 248 | preview : str, enables or disables preview mode and specifies direction 249 | Acceptable inputs are (None, 'inline', 'xline', 'z') 250 | Optimizes chunk size in different orientations to facilitate rapid 251 | screening of algorithm output 252 | 253 | Returns 254 | ------- 255 | result : Dask Array 256 | """ 257 | 258 | darray, chunks_init = self.create_array(darray, preview=preview) 259 | result = da.clip(darray, min_val, max_val) 260 | 261 | return(result) 262 | 263 | 264 | def rms(self, darray, kernel=(1,1,9), preview=None): 265 | """ 266 | Description 267 | ----------- 268 | Compute the Root Mean Squared (RMS) value within a specified window 269 | 270 | Parameters 271 | ---------- 272 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 273 | 274 | Keywork Arguments 275 | ----------------- 276 | kernel : tuple (len 3), operator size 277 | preview : str, enables or disables preview mode and specifies direction 278 | Acceptable inputs are (None, 'inline', 'xline', 'z') 279 | Optimizes chunk size in different orientations to facilitate rapid 280 | screening of algorithm output 281 | 282 | Returns 283 | ------- 284 | result : Dask Array 285 | """ 286 | 287 | # Function to extract patches and perform algorithm 288 | def operation(chunk, kernel): 289 | x = util.extract_patches(chunk, kernel) 290 | out = np.sqrt(np.mean(x ** 2, axis=(-3, -2, -1))) 291 | 292 | return(out) 293 | 294 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 295 | result = darray.map_blocks(operation, kernel=kernel, dtype=darray.dtype, chunks=darray.chunks) 296 | result = util.trim_dask_array(result, kernel) 297 | result[da.isnan(result)] = 0 298 | 299 | return(result) 300 | 301 | 302 | def trace_agc(self, darray, kernel=(1,1,9), preview=None): 303 | """ 304 | Description 305 | ----------- 306 | Apply an adaptive trace gain to input seismic 307 | 308 | Parameters 309 | ---------- 310 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 311 | 312 | Keywork Arguments 313 | ----------------- 314 | kernel : tuple (len 3), operator size 315 | preview : str, enables or disables preview mode and specifies direction 316 | Acceptable inputs are (None, 'inline', 'xline', 'z') 317 | Optimizes chunk size in different orientations to facilitate rapid 318 | screening of algorithm output 319 | 320 | Returns 321 | ------- 322 | result : Dask Array 323 | """ 324 | 325 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 326 | rms = self.rms(darray, kernel) 327 | rms_max = rms.max() 328 | result = darray * (1.5 - (rms / rms_max)) 329 | result[da.isnan(result)] = 0 330 | 331 | return(result) 332 | 333 | 334 | def gradient_magnitude(self, darray, sigmas=(1,1,1), preview=None): 335 | """ 336 | Description 337 | ----------- 338 | Compute the 3D Gradient Magnitude using a Gaussian Operator 339 | 340 | Parameters 341 | ---------- 342 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 343 | 344 | Keywork Arguments 345 | ----------------- 346 | sigmas : tuple (len 3), gaussian operator in I, J, K 347 | preview : str, enables or disables preview mode and specifies direction 348 | Acceptable inputs are (None, 'inline', 'xline', 'z') 349 | Optimizes chunk size in different orientations to facilitate rapid 350 | screening of algorithm output 351 | 352 | Returns 353 | ------- 354 | result : Dask Array 355 | """ 356 | 357 | kernel = tuple(2 * (4 * np.array(sigmas) + 0.5).astype(int) + 1) 358 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 359 | result = darray.map_blocks(ndi.gaussian_gradient_magnitude, sigma=sigmas, dtype=darray.dtype) 360 | result = util.trim_dask_array(result, kernel) 361 | result[da.isnan(result)] = 0 362 | 363 | return(result) 364 | 365 | 366 | def reflection_intensity(self, darray, kernel=(1,1,9), preview=None): 367 | """ 368 | Description 369 | ----------- 370 | Compute reflection intensity by integrating the trace over a specified window 371 | 372 | Parameters 373 | ---------- 374 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 375 | 376 | Keywork Arguments 377 | ----------------- 378 | kernel : tuple (len 3), operator size 379 | preview : str, enables or disables preview mode and specifies direction 380 | Acceptable inputs are (None, 'inline', 'xline', 'z') 381 | Optimizes chunk size in different orientations to facilitate rapid 382 | screening of algorithm output 383 | 384 | Returns 385 | ------- 386 | result : Dask Array 387 | """ 388 | 389 | # Function to extract patches and perform algorithm 390 | def operation(chunk, kernel): 391 | x = util.extract_patches(chunk, (1, 1, kernel[-1])) 392 | out = np.trapz(x).reshape(x.shape[:3]) 393 | 394 | return(out) 395 | 396 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 397 | result = darray.map_blocks(operation, kernel=kernel, dtype=darray.dtype, chunks=chunks_init) 398 | result[da.isnan(result)] = 0 399 | 400 | return(result) 401 | 402 | 403 | def phase_rotation(self, darray, rotation, preview=None): 404 | """ 405 | Description 406 | ----------- 407 | Rotate the phase of the seismic data by a specified angle 408 | 409 | Parameters 410 | ---------- 411 | darray : Array-like, acceptable inputs include Numpy, HDF5, or Dask Arrays 412 | rotation : Number (degrees), angle of rotation 413 | 414 | Keywork Arguments 415 | ----------------- 416 | preview : str, enables or disables preview mode and specifies direction 417 | Acceptable inputs are (None, 'inline', 'xline', 'z') 418 | Optimizes chunk size in different orientations to facilitate rapid 419 | screening of algorithm output 420 | 421 | Returns 422 | ------- 423 | result : Dask Array 424 | """ 425 | 426 | phi = np.deg2rad(rotation) 427 | kernel = (1,1,25) 428 | darray, chunks_init = self.create_array(darray, kernel, preview=preview) 429 | analytical_trace = darray.map_blocks(signal.hilbert, dtype=darray.dtype) 430 | result = analytical_trace.real * da.cos(phi) - analytical_trace.imag * da.sin(phi) 431 | result = util.trim_dask_array(result, kernel) 432 | result[da.isnan(result)] = 0 433 | 434 | return(result) -------------------------------------------------------------------------------- /d2geo/attributes/__init__.py.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GeoDataScienceUQ/pyseismic/d8200e1181b6a4ec2719cba6b9ec25a1a5c33469/d2geo/attributes/__init__.py.txt -------------------------------------------------------------------------------- /d2geo/attributes/io.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Utilities for reading seismic data from SEGY and converting to usable format. 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import dask.array as da 12 | import numpy as np 13 | import segyio 14 | import h5py 15 | from shutil import copyfile as cf 16 | 17 | 18 | def segy_read(segy_path, out_path, out_name): 19 | 20 | def write(chunk, segy_file, dset): 21 | for i in chunk: 22 | dset[i[0], i[1], :] = segy_file.trace.raw[i[2]] 23 | 24 | return(chunk) 25 | 26 | segy_file = segyio.open(segy_path) 27 | trace_inlines = segy_file.attributes(segyio.TraceField.INLINE_3D)[:] 28 | trace_xlines = segy_file.attributes(segyio.TraceField.CROSSLINE_3D)[:] 29 | 30 | trace_inlines_unique = np.unique(trace_inlines) 31 | trace_xlines_unique = np.unique(trace_xlines) 32 | 33 | num_inline = trace_inlines_unique.size 34 | num_xline = trace_xlines_unique.size 35 | num_zsamples = len(segy_file.samples) 36 | 37 | min_inline = trace_inlines_unique.min() 38 | min_xline = trace_xlines_unique.min() 39 | min_zsample = segy_file.samples.min() 40 | 41 | max_inline = trace_inlines_unique.max() 42 | max_xline = trace_xlines_unique.max() 43 | max_zsample = segy_file.samples.max() 44 | 45 | inc_inline = int((max_inline - min_inline) / num_inline) 46 | inc_xline = int((max_xline - min_xline) / num_xline) 47 | inc_zsample = segy_file.bin[segyio.BinField.Interval] / 1000 48 | 49 | shape = (trace_inlines_unique.size, trace_xlines_unique.size, num_zsamples) 50 | ti_idx = trace_inlines - trace_inlines.min() 51 | tx_idx = trace_xlines - trace_xlines.min() 52 | idx = np.arange(ti_idx.size) 53 | coords = np.dstack((ti_idx, tx_idx, idx))[0] 54 | coords = da.from_array(coords, chunks=(25, 3)) 55 | 56 | with h5py.File(out_path, 'w') as f: 57 | 58 | dset = f.create_dataset(out_name, shape=shape) 59 | 60 | dset.attrs['dims'] = shape 61 | 62 | dset.attrs['inc_inline'] = inc_inline 63 | dset.attrs['inc_xline'] = inc_xline 64 | dset.attrs['inc_zsample'] = inc_zsample 65 | 66 | dset.attrs['min_inline'] = min_inline 67 | dset.attrs['min_xline'] = min_xline 68 | dset.attrs['min_zsample'] = min_zsample 69 | 70 | dset.attrs['max_inline'] = max_inline 71 | dset.attrs['max_xline'] = max_xline 72 | dset.attrs['max_zsample'] = max_zsample 73 | 74 | coords.map_blocks(write, segy_file, dset, dtype=np.float32).compute() 75 | 76 | 77 | 78 | def segy_write(in_data, template_segy, out_file): 79 | 80 | cf(template_segy, out_file) 81 | 82 | with segyio.open(out_file, 'r+') as f: 83 | 84 | for i in range(in_data.shape[0]): 85 | try: 86 | il = f.ilines[i] 87 | f.iline[il] = in_data[i] 88 | 89 | except Exception: 90 | continue -------------------------------------------------------------------------------- /d2geo/attributes/util.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Utilities for working with seismic and computing volume attributes. 4 | 5 | @author: Braden Fitz-Gerald 6 | @email: braden.fitzgerald@gmail.com 7 | 8 | """ 9 | 10 | # Import Libraries 11 | import dask.array as da 12 | import numpy as np 13 | import h5py 14 | import psutil 15 | 16 | 17 | def compute_chunk_size(shape, byte_size, kernel=None, preview=None): 18 | """ 19 | Description 20 | ----------- 21 | Compute ideal block size for Dask Array given specific information about 22 | the computer being used, the input data, kernel size, and whether or not 23 | this operation is is 'preview' mode. 24 | 25 | Parameters 26 | ---------- 27 | shape : tuple (len 3), shape of seismic data 28 | byte_size : int, byte size of seismic data dtype 29 | 30 | Keywork Arguments 31 | ----------------- 32 | kernel : tuple (len 3), operator size 33 | preview : str, enables or disables preview mode and specifies direction 34 | Acceptable inputs are (None, 'inline', 'xline', 'z') 35 | Optimizes chunk size in different orientations to facilitate rapid 36 | screening of algorithm output 37 | 38 | Returns 39 | ------- 40 | chunk_size : tuple (len 3), optimal chunk size 41 | """ 42 | 43 | # Evaluate kernel 44 | if kernel == None: 45 | kernel = (1,1,1) 46 | ki, kj, kk = kernel 47 | else: 48 | ki, kj, kk = kernel 49 | 50 | # Identify acceptable chunk sizes 51 | i_s = np.arange(ki, shape[0]) 52 | j_s = np.arange(kj, shape[1]) 53 | k_s = np.arange(kk, shape[2]) 54 | 55 | modi = shape[0] % i_s 56 | modj = shape[1] % j_s 57 | modk = shape[2] % k_s 58 | 59 | kki = i_s[(modi >= ki) | (modi == 0)] 60 | kkj = j_s[(modj >= kj) | (modj == 0)] 61 | kkk = k_s[(modk >= kk) | (modk == 0)] 62 | 63 | # Compute Machine Specific information 64 | mem = psutil.virtual_memory().available 65 | cpus = psutil.cpu_count() 66 | byte_size = byte_size 67 | M = ((mem / (cpus * byte_size)) / (ki * kj * kk)) * 0.75 68 | 69 | # Compute chunk size if preview mode is disabled 70 | if preview == None: 71 | # M *= 0.3 72 | Mij = kki * kkj.reshape(-1,1) * shape[2] 73 | Mij[Mij > M] = -1 74 | Mij = Mij.diagonal() 75 | 76 | chunks = [kki[Mij.argmax()], kkj[Mij.argmax()], shape[2]] 77 | 78 | # Compute chunk size if preview mode is enabled 79 | else: 80 | kki = kki.min() 81 | kkj = kkj.min() 82 | kkk = kkk.min() 83 | 84 | if preview == 'inline': 85 | if (kki * shape[1] * shape[2]) < M: 86 | chunks = [kki, shape[1], shape[2]] 87 | 88 | else: 89 | j_s = np.arange(kkj, shape[1]) 90 | modj = shape[1] % j_s 91 | kkj = j_s[(modj >= kj) | (modj == 0)] 92 | Mj = j_s * kki * shape[2] 93 | Mj = Mj[Mj < M] 94 | chunks = [kki, Mj.argmax(), shape[2]] 95 | 96 | elif preview == 'xline': 97 | if (kkj * shape[0] * shape[2]) < M: 98 | chunks = [shape[0], kkj, shape[2]] 99 | 100 | else: 101 | i_s = np.arange(kki, shape[0]) 102 | modi = shape[0] % i_s 103 | kki = i_s[(modi >= ki) | (modi == 0)] 104 | Mi = i_s * kkj * shape[2] 105 | Mi = Mi[Mi < M] 106 | chunks = [Mi.argmax(), kkj, shape[2]] 107 | 108 | else: 109 | if (kkk * shape[0] * shape[1]) < M: 110 | chunks = [shape[0], shape[2], kk] 111 | 112 | else: 113 | j_s = np.arange(kkj, shape[1]) 114 | modj = shape[1] % j_s 115 | kkj = j_s[(modj >= kj) | (modj == 0)] 116 | Mj = j_s * kkk * shape[0] 117 | Mj = Mj[Mj < M] 118 | chunks = [shape[0], Mj.argmax(), kkk] 119 | 120 | return(tuple(chunks)) 121 | 122 | 123 | 124 | def trim_dask_array(in_data, kernel): 125 | """ 126 | Description 127 | ----------- 128 | Trim resuling Dask Array given a specified kernel size 129 | 130 | Parameters 131 | ---------- 132 | in_data : Dask Array 133 | kernel : tuple (len 3), operator size 134 | 135 | Returns 136 | ------- 137 | out : Dask Array 138 | """ 139 | 140 | # Compute half windows and assign to dict 141 | hw = tuple(np.array(kernel) // 2) 142 | axes = {0 : hw[0], 1 : hw[1], 2: hw[2]} 143 | 144 | return(da.ghost.trim_internal(in_data, axes=axes)) 145 | 146 | 147 | 148 | def available_volumes(file_path): 149 | """ 150 | Description 151 | ----------- 152 | Convience function to evaluate what volumes exist and what their names are 153 | 154 | Parameters 155 | ---------- 156 | file_path : str, path to file 157 | 158 | Returns 159 | ------- 160 | vols : list, array of volume names in file 161 | """ 162 | 163 | # Iterate through HDF5 file and output dataset names 164 | with h5py.File(file_path) as f: 165 | vols = [i for i in f] 166 | 167 | return(vols) 168 | 169 | 170 | 171 | def read(file_path): 172 | """ 173 | Description 174 | ----------- 175 | Convience function to read file and create a pointer to data on disk 176 | 177 | Parameters 178 | ---------- 179 | file_path : str, path to file 180 | 181 | Returns 182 | ------- 183 | data : HDF5 dataset, pointer to data on disk 184 | """ 185 | 186 | data = h5py.File(file_path)['data'] 187 | 188 | return(data) 189 | 190 | 191 | 192 | def save(out_data, out_file): 193 | """ 194 | Description 195 | ----------- 196 | Convience function to read file and create a pointer to data on disk 197 | 198 | Parameters 199 | ---------- 200 | out_data : Dask Array, data to be saved to disk 201 | out_file : str, path to file to save to 202 | """ 203 | 204 | # Save to disk if object is Dask Array 205 | try: 206 | out_data.to_hdf5(out_file, 'data') 207 | except Exception: 208 | raise Exception('Object is not a Dask Array') 209 | 210 | 211 | 212 | def convert_dtype(in_data, min_val, max_val, to_dtype): 213 | """ 214 | Description 215 | ----------- 216 | Convience function to read file and create a pointer to data on disk 217 | 218 | Parameters 219 | ---------- 220 | in_data : Dask Array, data to convert 221 | min_val : number, lower clip 222 | max_val : number, upper clip 223 | to_dtype : NumPy dtype 224 | Acceptable formats include (np.int8, np.float16, np.float32) 225 | 226 | Returns 227 | ------- 228 | out : Dask Array, converted data 229 | """ 230 | 231 | # Check if data is already in correct format 232 | if in_data.dtype == to_dtype: 233 | return(in_data) 234 | 235 | 236 | else: 237 | 238 | in_data = da.clip(in_data, min_val, max_val) 239 | if to_dtype == np.int8: 240 | in_data = ((in_data - min_val) / (max_val - min_val)) 241 | dtype = np.iinfo(np.int8) 242 | in_data *= dtype.max - dtype.min 243 | in_data -= dtype.min 244 | out = in_data.astype(np.int8) 245 | 246 | elif to_dtype == np.float16: 247 | out = in_data.astype(np.float16) 248 | 249 | elif to_dtype == np.int32: 250 | out = in_data.astype(np.float32) 251 | 252 | else: 253 | raise Exception('Not a valid dtype') 254 | 255 | return(out) 256 | 257 | 258 | 259 | def extract_patches(in_data, kernel): 260 | """ 261 | Description 262 | ----------- 263 | Reshape in_data into a collection of patches defined by kernel 264 | 265 | Parameters 266 | ---------- 267 | in_data : Dask Array, data to convert 268 | kernel : tuple (len 3), operator size 269 | 270 | Returns 271 | ------- 272 | out : Numpy Array, has shape (in_data.shape[0], in_data.shape[1], 273 | in_data.shape[2], kernel[0], kernel[1], kernel[2]) 274 | """ 275 | 276 | strides = in_data.strides + in_data.strides 277 | shape = (np.array(in_data.shape) - np.array(kernel)) + 1 278 | shape = tuple(list(shape) + list(kernel)) 279 | 280 | patches = np.lib.stride_tricks.as_strided(in_data, 281 | shape=shape, 282 | strides=strides) 283 | return(patches) 284 | 285 | 286 | 287 | def local_events(in_data, comparator): 288 | """ 289 | Description 290 | ----------- 291 | Find local peaks or troughs depending on comparator used 292 | 293 | Parameters 294 | ---------- 295 | in_data : Dask Array, data to convert 296 | comparator : function, defines truth between neighboring elements 297 | 298 | Returns 299 | ------- 300 | out : Numpy Array 301 | """ 302 | 303 | idx = np.arange(0, in_data.shape[-1]) 304 | trace = in_data.take(idx, axis=-1, mode='clip') 305 | plus = in_data.take(idx + 1, axis=-1, mode='clip') 306 | minus = in_data.take(idx - 1, axis=-1, mode='clip') 307 | 308 | result = np.ones(in_data.shape, dtype=np.bool) 309 | 310 | result &= comparator(trace, plus) 311 | result &= comparator(trace, minus) 312 | 313 | return(result) 314 | 315 | 316 | 317 | def hilbert(in_data): 318 | """ 319 | Description 320 | ----------- 321 | Perform Hilbert Transform on input data 322 | 323 | Parameters 324 | ---------- 325 | in_data : Dask Array, data to convert 326 | 327 | Returns 328 | ------- 329 | out : Numpy Array 330 | """ 331 | 332 | N = in_data.shape[-1] 333 | 334 | Xf = np.fft.fftpack.fft(in_data, n=N, axis=-1) 335 | 336 | h = np.zeros(N) 337 | if N % 2 == 0: 338 | h[0] = h[N // 2] = 1 339 | h[1:N // 2] = 2 340 | else: 341 | h[0] = 1 342 | h[1:(N + 1) // 2] = 2 343 | 344 | if in_data.ndim > 1: 345 | ind = [np.newaxis] * in_data.ndim 346 | ind[-1] = slice(None) 347 | h = h[ind] 348 | x = np.fft.fftpack.ifft(Xf * h, axis=-1) 349 | return x -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: seismicpy 2 | channels: 3 | - defaults 4 | dependencies: 5 | - ca-certificates=2024.7.2=haa95532_0 6 | - libffi=3.4.4=hd77b12b_1 7 | - openssl=3.0.14=h827c3e9_0 8 | - pip=24.0=py38haa95532_0 9 | - python=3.8.19=h1aa4202_0 10 | - setuptools=69.5.1=py38haa95532_0 11 | - sqlite=3.45.3=h2bbff1b_0 12 | - vc=14.2=h2eaa2aa_4 13 | - vs2015_runtime=14.29.30133=h43f2093_4 14 | - wheel=0.43.0=py38haa95532_0 15 | - pip: 16 | - dask==2021.11.1 17 | - numpy==1.24.4 18 | - open3d==0.13.0 19 | - opencv-python-headless==4.5.4.60 20 | - pandas==2.0.3 21 | - pyntcloud==0.1.5 22 | - scikit-image==0.18.1 23 | - scikit-learn==1.1.3 24 | - scipy==1.10.1 25 | - segysak==0.3.3 26 | - lshashpy3==0.0.9 27 | - matplotlib==3.7.5 28 | - h5py==3.8.0 29 | - xarray==2023.1.0 -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | dask 2 | opencv-python-headless 3 | pandas 4 | pyntcloud 5 | scikit-image 6 | scikit-learn 7 | scipy 8 | segysak 9 | lshashpy3 10 | matplotlib 11 | h5py==3.8.0 12 | xarray==2023.1.0 13 | numpy==1.24.4 14 | open3d==0.13.0 -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | """ 4 | maintainer: 5 | """ 6 | 7 | import pickle 8 | import sys 9 | 10 | def to_pickle(obj, file): 11 | with open(file, 'wb') as handle: 12 | pickle.dump(obj, handle, protocol=pickle.HIGHEST_PROTOCOL) 13 | 14 | # Adapted from https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console/14879561 15 | # Print iterations progress 16 | def printProgressBar (iteration, total, prefix = 'Progress: ', suffix = 'Complete', decimals = 1, bar_length=100): 17 | """ 18 | Call in a loop to create terminal progress bar 19 | @params: 20 | iteration - Required : current iteration (Int) 21 | total - Required : total iterations (Int) 22 | prefix - Optional : prefix string (Str) 23 | suffix - Optional : suffix string (Str) 24 | decimals - Optional : positive number of decimals in percent complete (Int) 25 | bar_length - Optional : character length of bar (Int) 26 | """ 27 | str_format = "{0:." + str(decimals) + "f}" 28 | percents = str_format.format(100 * (iteration / float(total))) 29 | filled_length = int(round(bar_length * iteration / float(total))) 30 | bar = '█' * filled_length + '-' * (bar_length - filled_length) 31 | 32 | # print('\r%s |%s| %s%% %s' % (prefix, bar, percents, suffix), end = '\r'), 33 | # if iteration == total: 34 | # print() 35 | 36 | sys.stdout.write('\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)), 37 | if iteration == total: 38 | sys.stdout.write('\n') 39 | sys.stdout.flush() 40 | 41 | def write_pcd_to_ASCII(pcd, cdp_x, cdp_y, twt, file_path): 42 | with open(file_path, 'w') as the_file: 43 | #convert point from cube coordinates to point referenced coordinates 44 | the_file.write("CDP_X CDP_Y TWT\n") 45 | for (x, y, z) in pcd: 46 | # the_file.write("INLINE : {} XLINE : {} {} {} {}\n".format( 47 | # int(iline[x]), int(xline[y]), float(cdp_x[x, y]), float(cdp_y[x, y]), int(twt[z]))) 48 | the_file.write("{} {} {}\n".format( 49 | float(cdp_x[x, y]), float(cdp_y[x, y]), int(twt[z]))) --------------------------------------------------------------------------------