├── LICENSE ├── README.md ├── ph2dt.py └── phase_selection.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## TomoDD Tools 2 | 原版tomoDD程序包提供的工具包中的一些工具在某些情况下不太好用\ 3 | 故根据实际情况重写\ 4 | ### 包含 5 | #### phase_selection.py 6 | #### ph2dt.py 7 | -------------------------------------------------------------------------------- /ph2dt.py: -------------------------------------------------------------------------------- 1 | ## ph2dt.py v1.0 2 | ## HypoDD或TomoDD中ph2dt程序的替代程序 3 | ## 暂时未考虑事件的最大震相数量和最大邻居数量,因此等效于ph2dt程序中MAXOBS和MAXNGH设置为无穷大 4 | ## 该程序已经对event.sel进行了补零，因此在下一步中无需运行addEve0.awk 5 | import math 6 | #################################################################### 7 | station_file = "station.dat" 8 | ''' 9 | C.KCD01 30.9612 103.9179 0588 10 | C.KCD02 30.9098 103.7578 0629 11 | C.KCD03 30.7189 103.8503 0536 12 | C.KCD04 30.5746 103.5190 0526 13 | ''' 14 | #################################################################### 15 | phase_file = "phase.dat.sel" 16 | ''' 17 | # 2006 10 09 20 39 13.00 30.07 102.5 2 1.4 0 0 0 20061009203913 18 | C.KKD04 9.83420 1 P 19 | C.KKD04 15.8642 1 S 20 | # 2006 10 10 07 49 47.00 29.87 101.88 5 1.3 0 0 0 20061010074947 21 | C.KKD03 7.38000 1 P 22 | C.KKD03 11.4500 1 S 23 | # 2006 10 12 03 06 57.00 27.85 102.03 4 1.4 0 0 0 20061012030657 24 | C.KKD05 39.7600 1 P 25 | # 2006 10 12 17 47 02.00 29.47 103.35 5 2 0 0 0 20061012174702 26 | C.KDB07 35.7000 1 P 27 | C.KKD02 58.6920 1 S 28 | C.KKD03 39.4900 1 S 29 | C.KKD06 31.8693 1 P 30 | C.KKD06 53.1193 1 S 31 | ''' 32 | #################################################################### 33 | MINWGHT = 0 ## 震相数据最小拾取质量 34 | MAXDIST = 800 ## 事件对于台站之间的最大距离 35 | MINSEP = 0.10 ## 事件对之间的最小间距 36 | MAXSEP = 30 ## 事件对之间的最大间距 37 | ## MAXNGH = 30 ## 每个事件的最大邻居数(该参数暂未使用) 38 | MINLNK = 8 ## 一个事件如果能成为另一个事件的邻居，那么该事件对需要的相同震相个数 39 | MINOBS = 8 ## 每一个事件对所需要的震相数的最小值 40 | ## MAXOBS = 120 ## 每一个事件对所需要的震相数的最大值（该参数暂未使用） 41 | ##################################################################### 42 | 43 | ## 计算事件与事件、事件与台站之间的位置 44 | ## position_n = [lat, lon, dep] 45 | def distance(position_1, position_2): 46 | kmperdeg = 111.1949266 47 | lat1, lat2 = position_1[0], position_2[0] 48 | lon1, lon2 = position_1[1], position_2[1] 49 | dep1, dep2 = position_1[2], position_2[2] 50 | dlat = lat1-lat2 51 | dlon = lon1-lon2 52 | ddep = dep1-dep2 53 | dist = math.sqrt((dlat*kmperdeg)**2+(dlon*(math.cos(lat1*3.1415926/180)*kmperdeg))**2+ddep**2) 54 | return dist 55 | 56 | ## 读取台站文件 57 | ## {'XJI': [31.0, 102.4, 0.0]} 58 | stations = {} 59 | n = 0 60 | with open(station_file, "r") as f: 61 | lines = f.readlines() 62 | for line in lines: 63 | n += 1 64 | line = line.split() 65 | key = line[0] 66 | value = [float(line[1]), float(line[2]), -float(line[3])/1000] 67 | stations[key] = value 68 | print(f"stations:{n}") 69 | 70 | ## 读取震相文件 71 | events = [] ## 存放事件编号，用于后面遍历 72 | ## ['620002','620003','620004','620005','620006','620007'] 73 | events_info = {} ## 存放事件信息，事件、经纬度等 74 | ## {'620002': ['2001','1','1','16','2','43.60','29.220','101.070','9.0','0','0','0','0'] 75 | phase = {} ## 存放震相信息，用事件编号索引 76 | ## {'620002': [['MDS', '35.82', '1', 'P'],['MEK', '49.19', '1', 'P']]} 77 | key = "event" 78 | pha = [["phase"],[]] 79 | n1 = 0 80 | n2 = 0 81 | with open(phase_file, "r") as f: 82 | lines = f.readlines() 83 | for line in lines: 84 | line = line.split() 85 | if line[0] == "#": 86 | n1 += 1 87 | phase[key] = pha 88 | pha = [] 89 | key = line[-1] 90 | value = line[1:-1] 91 | events.append(key) 92 | events_info[key] = value 93 | else: 94 | n2 += 1 95 | pha.append(line) 96 | phase[key] = pha 97 | phase.pop('event') 98 | print(f"events:{n1} phase:{n2}") 99 | 100 | ## 删除震中距大于MAXDIST的震相 101 | ## 筛选权重大于MINWGHT的震相 102 | for event in events: 103 | event_position = list(map(float,events_info[event][6:9])) 104 | phas = phase[event] 105 | for pha in phas: 106 | sta_name = pha[0] 107 | sta_position = stations[sta_name] 108 | dist = distance(event_position,sta_position) 109 | if dist >= MAXDIST and pha[2] <= MINWGHT: 110 | phase[event].remove(pha) 111 | 112 | ## 生成event.dat 113 | ## 筛选震相数大于MINOBS的事件 114 | ## 生成event.sel 115 | ## 生成absolute.dat 116 | event_dat = [] 117 | event_sel = [] 118 | absolute = {} 119 | for event in events: 120 | date = events_info[event][0]+events_info[event][1].zfill(2)+events_info[event][2].zfill(2) 121 | time = events_info[event][3].zfill(2)+events_info[event][4].zfill(2)+events_info[event][5].split(".")[0].zfill(2)+events_info[event][5].split(".")[1].zfill(2) 122 | lat = str(format(float(events_info[event][6]),".4f")) 123 | lon = str(format(float(events_info[event][7]),".4f")) 124 | dep = str(format(float(events_info[event][8]),".3f")) 125 | mag = str(format(float(events_info[event][9]),".1f")) 126 | EH = str(format(float(events_info[event][10]),".2f")) 127 | EV = str(format(float(events_info[event][11]),".2f")) 128 | RMS = str(format(float(events_info[event][12]),".2f")) 129 | item = f"{date}\t{time}\t{lat}\t{lon}\t{dep}\t{mag}\t{EH}\t{EV}\t{RMS}\t{event}\n" 130 | event_dat.append(item) 131 | if len(phase[event]) >= MINOBS: 132 | item = f"{date}\t{time}\t{lat}\t{lon}\t{dep}\t{mag}\t{EH}\t{EV}\t{RMS}\t{event}\t0\n" 133 | event_sel.append(item) 134 | absolute[event] = phase[event] 135 | 136 | ## 写入文件 137 | ## 写入event.dat 138 | with open("event.dat", "w") as f: 139 | for i in event_dat: 140 | f.writelines(i) 141 | ## 写入event.sel 142 | with open("event.sel", "w") as f: 143 | for i in event_sel: 144 | f.writelines(i) 145 | ## 写入absolute.dat 146 | with open("absolute.dat", "w") as f: 147 | for i in absolute.keys(): 148 | f.writelines(f"#\t\t\t{i}\n") 149 | for j in absolute[i]: 150 | sta = j[0] 151 | ttime = str(format(float(j[1]),".2f")) 152 | weight = str(format(float(j[2]),".2f")) 153 | type = j[3] 154 | f.writelines(f"{sta}\t\t{ttime}\t\t{weight}\t\t{type}\n") 155 | 156 | ## 更新事件目录 157 | events = [] 158 | for i in event_sel: 159 | events.append(i.split()[-2]) 160 | 161 | ## 计算震相对，生成dt.ct文件 162 | dtct = {} ## key = event1,value = event_pair 163 | ## event_pair = {} ## key = event2,value = [common phase] 164 | for index in range(len(events)-1): 165 | event_pair = {} ## key = event2,value = [common phase] 166 | event1 = events[index] 167 | print(event1) 168 | event1_position = list(map(float,events_info[event1][6:9])) 169 | event1_phase = phase[event1] 170 | for event2 in events[index+1:]: 171 | event2_position = list(map(float,events_info[event2][6:9])) 172 | event2_phase = phase[event2] 173 | dist = distance(event1_position,event2_position) 174 | if MINSEP <= dist <= MAXSEP: 175 | common = [] 176 | for pha1 in event1_phase: 177 | for pha2 in event2_phase: 178 | if pha1[0] == pha2[0] and pha1[-1] == pha2[-1]: 179 | t1 = str(format(float(pha1[1]),".4f")) 180 | t2 = str(format(float(pha2[1]),".4f")) 181 | weight = str(format((float(pha1[2])+float(pha2[2]))/2,".3f")) 182 | item = f"{pha1[0]}\t\t{t1}\t\t{t2}\t\t{weight}\t\t{pha1[-1]}\n" 183 | common.append(item) 184 | if len(common) >= MINOBS : 185 | event_pair[str(format(dist,".4f"))+" "+event2] = common 186 | if len(event_pair) != 0: 187 | dtct[event1] = event_pair 188 | 189 | ## 写入文件 190 | with open("dt.ct", "w") as f: 191 | for i in dtct.keys(): 192 | for j in dtct[i].keys(): 193 | f.writelines(f"#\t\t\t{i}\t\t\t{j.split()[1]}\n") 194 | for k in dtct[i][j]: 195 | f.writelines(k) -------------------------------------------------------------------------------- /phase_selection.py: -------------------------------------------------------------------------------- 1 | ## phase_selection.py v1.0 2 | ## HypoDD或TomoDD中phaseSelection的替代 3 | ## 原程序只能使用一次函数进行拟合，对于震中距较远的事件拟合较差，无法筛选 4 | ## 该程序可以自行设置拟合函数，尝试得到最好的拟合结果 5 | 6 | import scipy 7 | import matplotlib.pyplot as plt 8 | import numpy as np 9 | import math 10 | ###########################文件位置#################################### 11 | station_file = "../data/station.dat" 12 | phase_file = "../data/phase.dat" 13 | sel_file = "./phase.dat.sel" 14 | ###########################拟合函数#################################### 15 | def p_func(x,a,b,c): 16 | return c*x**a+b 17 | def s_func(x,a,b): 18 | return x**a+b 19 | ###########################调整截距#################################### 20 | pd1 = 5 21 | pd2 = 6 22 | sd1 = 7 23 | sd2 = 7 24 | ###################################################################### 25 | ## 计算台站和事件的距离 26 | def distance(lat1,lat2,lon1,lon2,dep1,dep2): 27 | dlat = lat1-lat2 28 | dlon = lon1-lon2 29 | ddep = dep1-dep2 30 | dist = math.sqrt((dlat*111)**2+(dlon*(math.cos(lat1*3.1415926/180)*111))**2+ddep**2) 31 | return dist 32 | ## 读取台站位置信息 33 | stations = {} 34 | with open(station_file, "r") as f: 35 | lines = f.readlines() 36 | for line in lines: 37 | line = line.split() 38 | stations[line[0]] = [float(line[1]),float(line[2]),-float(line[3])/1000] 39 | 40 | p_dist = [] 41 | p_ttime = [] 42 | s_dist = [] 43 | s_ttime = [] 44 | e_lat,e_lon,e_dep = 0,0,0 45 | with open(phase_file, "r") as f: 46 | lines = f.readlines() 47 | for line in lines: 48 | line = line.split() 49 | if line[0] == "#": 50 | e_lat = float(line[7]) 51 | e_lon = float(line[8]) 52 | e_dep = float(line[9]) 53 | else: 54 | sta = line[0] 55 | t_time = float(line[1]) 56 | p_type = line[-1] 57 | s_lat = stations[sta][0] 58 | s_lon = stations[sta][1] 59 | s_dep = stations[sta][2] 60 | dist = distance(e_lat,s_lat,e_lon,s_lon,e_dep,s_dep) 61 | if p_type == "P": 62 | p_dist.append(dist) 63 | p_ttime.append(t_time) 64 | else: 65 | s_dist.append(dist) 66 | s_ttime.append(t_time) 67 | 68 | p_fit = scipy.optimize.curve_fit(p_func,p_dist,p_ttime)[0] 69 | p_a = p_fit[0] 70 | p_b = p_fit[1] 71 | p_c = p_fit[2] 72 | p_x = [] 73 | p_y = [] 74 | for i in range(0,700,10): 75 | p_x.append(i) 76 | p_y.append(p_func(i, p_a, p_b, p_c)) 77 | p_y1 = [] 78 | p_y2 = [] 79 | for i in p_y: 80 | p_y1.append(i+pd1) 81 | p_y2.append(i-pd2) 82 | 83 | s_fit = scipy.optimize.curve_fit(s_func,s_dist,s_ttime)[0] 84 | s_a = s_fit[0] 85 | s_b = s_fit[1] 86 | s_x = [] 87 | s_y = [] 88 | for i in range(0,700,10): 89 | s_x.append(i) 90 | s_y.append(s_func(i, s_a, s_b)) 91 | s_y1 = [] 92 | s_y2 = [] 93 | for i in s_y: 94 | s_y1.append(i+sd1) 95 | s_y2.append(i-sd2) 96 | 97 | plt.figure(1) 98 | plt.xlabel("distance") 99 | plt.ylabel("travel time") 100 | plt.scatter(p_dist,p_ttime,s=1,c="b",alpha=1) 101 | plt.plot(p_x,p_y,c = "orange") 102 | plt.plot(p_x,p_y1,c = "orange") 103 | plt.plot(p_x,p_y2,c = "orange") 104 | plt.text(0, max(p_ttime),str(len(p_dist))) 105 | 106 | plt.figure(2) 107 | plt.xlabel("distance") 108 | plt.ylabel("travel time") 109 | plt.scatter(s_dist,s_ttime,s=1,c="r",alpha=1) 110 | plt.plot(s_x,s_y,c = "orange") 111 | plt.plot(s_x,s_y1,c = "orange") 112 | plt.plot(s_x,s_y2,c = "orange") 113 | plt.text(0, max(s_ttime),str(len(s_dist))) 114 | ## 绘制筛选前的走时曲线 115 | plt.show() 116 | 117 | catalog = [] 118 | e_lat,e_lon,e_dep = 0,0,0 119 | with open(phase_file, "r") as f: 120 | lines = f.readlines() 121 | for line in lines: 122 | if line[0] == "#": 123 | lin = line.split() 124 | e_lat = float(lin[7]) 125 | e_lon = float(lin[8]) 126 | e_dep = float(lin[9]) 127 | catalog.append(line) 128 | else: 129 | lin = line.split() 130 | sta = lin[0] 131 | t_time = float(lin[1]) 132 | p_type = lin[-1] 133 | s_lat = stations[sta][0] 134 | s_lon = stations[sta][1] 135 | s_dep = stations[sta][2] 136 | dist = distance(e_lat,s_lat,e_lon,s_lon,e_dep,s_dep) 137 | if p_type == "P": 138 | pred_tt = p_func(dist,p_a,p_b,p_c) 139 | else: 140 | pred_tt = s_func(dist,s_a,s_b) 141 | if dist>50 and pred_tt-pd2 <= t_time <= pred_tt+pd1: 142 | catalog.append(line) 143 | elif dist<=50: 144 | catalog.append(line) 145 | 146 | p_dist = [] 147 | p_ttime = [] 148 | s_dist = [] 149 | s_ttime = [] 150 | e_lat,e_lon,e_dep = 0,0,0 151 | for phase in catalog: 152 | line = phase.split() 153 | if line[0] == "#": 154 | e_lat = float(line[7]) 155 | e_lon = float(line[8]) 156 | e_dep = float(line[9]) 157 | else: 158 | sta = line[0] 159 | t_time = float(line[1]) 160 | p_type = line[-1] 161 | s_lat = stations[sta][0] 162 | s_lon = stations[sta][1] 163 | s_dep = stations[sta][2] 164 | dist = distance(e_lat,s_lat,e_lon,s_lon,e_dep,s_dep) 165 | if p_type == "P": 166 | p_dist.append(dist) 167 | p_ttime.append(t_time) 168 | else: 169 | s_dist.append(dist) 170 | s_ttime.append(t_time) 171 | 172 | p_fit = scipy.optimize.curve_fit(p_func,p_dist,p_ttime)[0] 173 | p_a = p_fit[0] 174 | p_b = p_fit[1] 175 | p_x = [] 176 | p_y = [] 177 | for i in range(0,700,10): 178 | p_x.append(i) 179 | p_y.append(p_func(i, p_a, p_b, p_c)) 180 | p_y1 = [] 181 | p_y2 = [] 182 | for i in p_y: 183 | p_y1.append(i+pd1) 184 | p_y2.append(i-pd2) 185 | 186 | s_fit = scipy.optimize.curve_fit(s_func,s_dist,s_ttime)[0] 187 | s_a = s_fit[0] 188 | s_b = s_fit[1] 189 | s_x = [] 190 | s_y = [] 191 | for i in range(0,700,10): 192 | s_x.append(i) 193 | s_y.append(s_func(i, s_a, s_b)) 194 | s_y1 = [] 195 | s_y2 = [] 196 | for i in s_y: 197 | s_y1.append(i+sd1) 198 | s_y2.append(i-sd2) 199 | 200 | plt.figure(3) 201 | plt.xlabel("distance") 202 | plt.ylabel("travel time") 203 | plt.scatter(p_dist,p_ttime,s=1,c="b",alpha=1) 204 | plt.plot(p_x,p_y,c = "orange") 205 | plt.plot(p_x,p_y1,c = "orange") 206 | plt.plot(p_x,p_y2,c = "orange") 207 | plt.text(0, max(p_ttime),str(len(p_dist))) 208 | 209 | plt.figure(4) 210 | plt.xlabel("distance") 211 | plt.ylabel("travel time") 212 | plt.scatter(s_dist,s_ttime,s=1,c="r",alpha=1) 213 | plt.plot(s_x,s_y,c = "orange") 214 | plt.plot(s_x,s_y1,c = "orange") 215 | plt.plot(s_x,s_y2,c = "orange") 216 | plt.text(0, max(s_ttime),str(len(s_dist))) 217 | ## 绘制筛选后的走时曲线 218 | plt.show() 219 | ## 将筛选后的震相写入文件 220 | with open(sel_file, "w") as f: 221 | for i in catalog: 222 | f.writelines(i) --------------------------------------------------------------------------------