{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Initialization, class definition, and declaration.\n", "\n", "import os\n", "import math\n", "from obspy import UTCDateTime\n", "import numpy as np\n", "import copy\n", "\n", "class Event(): # class of earthquake\n", " def __init__(self):\n", " self.name = \"nan\" # evname1 Earthquake name, recommended as \"earthquake\".\n", " self.id = -1\n", " self.lat = 0.0\n", " self.lon = 0.0\n", " self.dep = 0.0\n", " self.mag = 0.0\n", " self.ortime = UTCDateTime(1999,1,1,0,0,0)\n", " self.Nt = 0 # Number of the absolute traveltime of earthquake\n", " self.Ncs_dt = 0 # Number of the commmon source differential traveltime of earthquake\n", " self.Ncr_dt = 0 # Number of the commmon receiver differential traveltime of earthquake\n", " self.t = {} # stname1+phase -> (stname1, phase, time, data_weight)\n", " self.cs_dt = {} # stname1 + stname2 + phase -> (stname1, stname2, phase, dif_time, data_weight)\n", " self.cr_dt = {} # stname1 + evname2 + phase -> (stname1, evname2, phase, dif_time, data_weight)\n", " self.azi_gap = 360.0 # the max azimuthal gap of each earthquake\n", " self.misfit = {} # traveltime residual of the data, the difference between real data and synthetic data, used for evaluation. stname or stname1+stname2 or stname1+evname2 -> residual\n", " self.tag = {} # additional tags for the earthquake, e.g., azi_gap, weight. (azimuthal gap, weight of the earthquake)\n", "\n", "class Station():\n", " def __init__(self):\n", " self.name = \"nan\" # stname1, recommend: network.stname\n", " self.id = -1\n", " self.lat = 0.0\n", " self.lon = 0.0\n", " self.ele = 0.0\n", " self.tag = {} # additional tags for the station, e.g., wright\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: some basic auxiliary functions for processing data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function： cal_dis(lat1, lon1,lat2, lon2) (in kilometers)， cal_azimuth(lat1, lon1, lat2, lon2) (degree) calculate epicentral distance (km) and azimuth (degree)\n", "\n", "def cal_dis(lat1, lon1,lat2, lon2, R = 6371):\n", " latitude1 = (math.pi/180)*lat1\n", " latitude2 = (math.pi/180)*lat2\n", " longitude1 = (math.pi/180)*lon1\n", " longitude2= (math.pi/180)*lon2\n", " # Therefore, the spherical distance between points A and B is:{arccos[sinb*siny+cosb*cosy*cos(a-x)]}*R\n", " # Radius of the earth\n", " if((lat1-lat2)**2+(lon1-lon2)**2<0.000001):\n", " return 0\n", "\n", " d = math.acos(math.sin(latitude1)*math.sin(latitude2)+ math.cos(latitude1)*math.cos(latitude2)*math.cos(longitude2-longitude1))/math.pi*180\n", " return d * 2 * math.pi * R / 360\n", "\n", "def cal_azimuth(lat1, lon1, lat2, lon2):\n", " lat1_rad = lat1 * math.pi / 180\n", " lon1_rad = lon1 * math.pi / 180\n", " lat2_rad = lat2 * math.pi / 180\n", " lon2_rad = lon2 * math.pi / 180\n", "\n", " y = math.sin(lon2_rad - lon1_rad) * math.cos(lat2_rad)\n", " x = math.cos(lat1_rad) * math.sin(lat2_rad) - math.sin(lat1_rad) * math.cos(lat2_rad) * math.cos(lon2_rad - lon1_rad)\n", " brng = math.atan2(y, x) * 180 / math.pi\n", " if((lat1-lat2)**2+(lon1-lon2)**2<0.0001):\n", " return 0\n", " return float((brng + 360.0) % 360.0)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: Coordinate rotation rotate_src_rec(ev_info, st_info, theta0, phi0, psi): rotate to the new coordinate system, satisfying the center point transformation r0, t0, p0 -> r0, 0, 0 and an anticlockwise rotation angle psi.\n", "# Satisfying the center point transformation r0, t0, p0 -> r0, 0, 0 and an anticlockwise rotation angle psi.\n", "\n", "import numpy as np\n", "\n", "RAD2DEG = 180/np.pi\n", "DEG2RAD = np.pi/180\n", "R_earth = 6371.0\n", "\n", "# Spherical coordinates to Cartesian coordinate\n", "def rtp2xyz(r,theta,phi):\n", " x = r * np.cos(theta*DEG2RAD) * np.cos(phi*DEG2RAD)\n", " y = r * np.cos(theta*DEG2RAD) * np.sin(phi*DEG2RAD)\n", " z = r * np.sin(theta*DEG2RAD)\n", " return (x,y,z)\n", "\n", "# Cartesian coordinates to Spherical coordinate\n", "def xyz2rtp(x,y,z):\n", " # theta: -90~90; phi: -180~180\n", " r = np.sqrt(x**2+y**2+z**2)\n", " theta = np.arcsin(z/r)\n", " phi = np.arcsin(y/r/np.cos(theta))\n", "\n", "\n", " idx = np.where((phi > 0) & (x*y < 0))\n", " phi[idx] = np.pi - phi[idx]\n", " idx = np.where((phi < 0) & (x*y > 0))\n", " phi[idx] = -np.pi - phi[idx]\n", "\n", "\n", " # for i in range(phi.size):\n", " # if(phi[i] > 0 and x[i]*y[i] < 0):\n", " # phi[i] = np.pi - phi[i]\n", " # if(phi[i] < 0 and x[i]*y[i] > 0):\n", " # phi[i] = -np.pi - phi[i]\n", "\n", " return (r,theta*RAD2DEG,phi*RAD2DEG)\n", "\n", "# anti-clockwise rotation along x-axis\n", "def rotate_x(x,y,z,theta):\n", " new_x = x\n", " new_y = y * np.cos(theta*DEG2RAD) + z * -np.sin(theta*DEG2RAD)\n", " new_z = y * np.sin(theta*DEG2RAD) + z * np.cos(theta*DEG2RAD)\n", " return (new_x,new_y,new_z)\n", "\n", "# anti-clockwise rotation along y-axis\n", "def rotate_y(x,y,z,theta):\n", " new_x = x * np.cos(theta*DEG2RAD) + z * np.sin(theta*DEG2RAD)\n", " new_y = y\n", " new_z = x * -np.sin(theta*DEG2RAD) + z * np.cos(theta*DEG2RAD)\n", " return (new_x,new_y,new_z)\n", "\n", "# anti-clockwise rotation along z-axis\n", "def rotate_z(x,y,z,theta):\n", " new_x = x * np.cos(theta*DEG2RAD) + y * -np.sin(theta*DEG2RAD)\n", " new_y = x * np.sin(theta*DEG2RAD) + y * np.cos(theta*DEG2RAD)\n", " new_z = z\n", " return (new_x,new_y,new_z)\n", "\n", "# spherical Rotation\n", "\n", "# rotate to the new coordinate, satisfying the center r0,t0,p0 -> r0,0,0 and a anticlockwise angle psi\n", "def rtp_rotation(t,p,theta0,phi0,psi):\n", " # step 1: r,t,p -> x,y,z\n", " (x,y,z) = rtp2xyz(1.0,t,p)\n", "\n", " # step 2: anti-clockwise rotation with -phi0 along z-axis: r0,t0,p0 -> r0,t0,0\n", " (x,y,z) = rotate_z(x,y,z,-phi0)\n", "\n", " # step 3: anti-clockwise rotation with theta0 along y-axis: r0,t0,0 -> r0,0,0\n", " (x,y,z) = rotate_y(x,y,z,theta0)\n", "\n", " # # step 4: anti-clockwise rotation with psi along x-axis\n", " (x,y,z) = rotate_x(x,y,z,psi)\n", "\n", " # step 5: x,y,z -> r,t,p\n", " (new_r,new_t,new_p) = xyz2rtp(x,y,z)\n", "\n", " return (new_t,new_p)\n", "\n", "\n", "def rtp_rotation_reverse(new_t,new_p,theta0,phi0,psi):\n", " # step 1: r,t,p -> x,y,z\n", " (x,y,z) = rtp2xyz(1.0,new_t,new_p)\n", "\n", " # step 2: anti-clockwise rotation with -psi along x-axis\n", " (x,y,z) = rotate_x(x,y,z,-psi)\n", "\n", " # step 3: anti-clockwise rotation with -theta0 along y-axis: r0,0,0 -> r0,t0,0\n", " (x,y,z) = rotate_y(x,y,z,-theta0)\n", "\n", " # step 4: anti-clockwise rotation with phi0 along z-axis: r0,t0,0 -> r0,t0,p0\n", " (x,y,z) = rotate_z(x,y,z,phi0)\n", "\n", " # step 5: x,y,z -> r,t,p\n", " (r,t,p) = xyz2rtp(x,y,z)\n", "\n", " return (t,p)\n", "\n", "def rotate_src_rec(ev_info,st_info,theta0,phi0,psi):\n", " ev_info_rotate = {}\n", " st_info_rotate = {}\n", "\n", " # rotate earthquakes\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", " ev_lat = np.array([ev.lat]); ev_lon = np.array([ev.lon])\n", " (ev_lat,ev_lon,) = rtp_rotation(ev_lat,ev_lon,theta0,phi0,psi)\n", " ev.lat = ev_lat[0]; ev.lon = ev_lon[0]\n", " ev_info_rotate[key_ev] = ev\n", "\n", " # rotate stations\n", " for key_st in st_info:\n", " st = st_info[key_st]\n", " st_lat = np.array([st.lat]); st_lon = np.array([st.lon])\n", " (st_lat,st_lon) = rtp_rotation(st_lat,st_lon,theta0,phi0,psi)\n", " st.lat = st_lat[0]; st.lon = st_lon[0]\n", " st_info_rotate[key_st] = st\n", "\n", " return (ev_info_rotate,st_info_rotate)\n", "\n", "def rotate_src_rec_reverse(ev_info_rotate,st_info_rotate,theta0,phi0,psi):\n", " ev_info = {}\n", " st_info = {}\n", "\n", " # rotate earthquakes\n", " for key_ev in ev_info_rotate:\n", " ev = ev_info_rotate[key_ev]\n", " ev_lat = np.array([ev.lat]); ev_lon = np.array([ev.lon])\n", " (ev_lat,ev_lon,) = rtp_rotation_reverse(ev_lat,ev_lon,theta0,phi0,psi)\n", " ev.lat = ev_lat[0]; ev.lon = ev_lon[0]\n", " ev_info[key_ev] = ev\n", "\n", " # rotate stations\n", " for key_st in st_info_rotate:\n", " st = st_info_rotate[key_st]\n", " st_lat = np.array([st.lat]); st_lon = np.array([st.lon])\n", " (st_lat,st_lon) = rtp_rotation_reverse(st_lat,st_lon,theta0,phi0,psi)\n", " st.lat = st_lat[0]; st.lon = st_lon[0]\n", " st_info[key_st] = st\n", "\n", " return (ev_info,st_info)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# # Function: Coordinate rotation rotate_src_rec(ev_info, st_info, theta0, phi0, psi): rotate to the new coordinate system, satisfying the center point transformation r0, t0, p0 -> r0, 0, 0 and an anticlockwise rotation angle psi.\n", "# # Satisfying the center point transformation r0, t0, p0 -> r0, 0, 0 and an anticlockwise rotation angle psi.\n", "\n", "# import numpy as np\n", "\n", "# RAD2DEG = 180/np.pi\n", "# DEG2RAD = np.pi/180\n", "# R_earth = 6371.0\n", "\n", "# # Spherical coordinates to Cartesian coordinate\n", "# def rtp2xyz(r,theta,phi):\n", "# x = r * np.cos(theta*DEG2RAD) * np.cos(phi*DEG2RAD)\n", "# y = r * np.cos(theta*DEG2RAD) * np.sin(phi*DEG2RAD)\n", "# z = r * np.sin(theta*DEG2RAD)\n", "# return (x,y,z)\n", "\n", "# # Cartesian coordinates to Spherical coordinate\n", "# def xyz2rtp(x,y,z):\n", "# # theta: -90~90; phi: -180~180\n", "# r = np.sqrt(x**2+y**2+z**2)\n", "# theta = np.arcsin(z/r)\n", "# phi = np.arcsin(y/r/np.cos(theta))\n", "\n", "\n", "# if(phi > 0 and x*y < 0):\n", "# phi = np.pi - phi\n", "# if(phi < 0 and x*y > 0):\n", "# phi = -np.pi - phi\n", "\n", "# return (r,theta*RAD2DEG,phi*RAD2DEG)\n", "\n", "# # anti-clockwise rotation along x-axis\n", "# def rotate_x(x,y,z,theta):\n", "# new_x = x\n", "# new_y = y * np.cos(theta*DEG2RAD) + z * -np.sin(theta*DEG2RAD)\n", "# new_z = y * np.sin(theta*DEG2RAD) + z * np.cos(theta*DEG2RAD)\n", "# return (new_x,new_y,new_z)\n", "\n", "# # anti-clockwise rotation along y-axis\n", "# def rotate_y(x,y,z,theta):\n", "# new_x = x * np.cos(theta*DEG2RAD) + z * np.sin(theta*DEG2RAD)\n", "# new_y = y\n", "# new_z = x * -np.sin(theta*DEG2RAD) + z * np.cos(theta*DEG2RAD)\n", "# return (new_x,new_y,new_z)\n", "\n", "# # anti-clockwise rotation along z-axis\n", "# def rotate_z(x,y,z,theta):\n", "# new_x = x * np.cos(theta*DEG2RAD) + y * -np.sin(theta*DEG2RAD)\n", "# new_y = x * np.sin(theta*DEG2RAD) + y * np.cos(theta*DEG2RAD)\n", "# new_z = z\n", "# return (new_x,new_y,new_z)\n", "\n", "# # spherical Rotation\n", "\n", "# # rotate to the new coordinate, satisfying the center r0,t0,p0 -> r0,0,0 and a anticlockwise angle psi\n", "# def rtp_rotation(t,p,theta0,phi0,psi):\n", "# # step 1: r,t,p -> x,y,z\n", "# (x,y,z) = rtp2xyz(1.0,t,p)\n", "\n", "# # step 2: anti-clockwise rotation with -phi0 along z-axis: r0,t0,p0 -> r0,t0,0\n", "# (x,y,z) = rotate_z(x,y,z,-phi0)\n", "\n", "# # step 3: anti-clockwise rotation with theta0 along y-axis: r0,t0,0 -> r0,0,0\n", "# (x,y,z) = rotate_y(x,y,z,theta0)\n", "\n", "# # # step 4: anti-clockwise rotation with psi along x-axis\n", "# (x,y,z) = rotate_x(x,y,z,psi)\n", "\n", "# # step 5: x,y,z -> r,t,p\n", "# (new_r,new_t,new_p) = xyz2rtp(x,y,z)\n", "\n", "# return (new_t,new_p)\n", "\n", "\n", "# def rtp_rotation_reverse(new_t,new_p,theta0,phi0,psi):\n", "# # step 1: r,t,p -> x,y,z\n", "# (x,y,z) = rtp2xyz(1.0,new_t,new_p)\n", "\n", "# # step 2: anti-clockwise rotation with -psi along x-axis\n", "# (x,y,z) = rotate_x(x,y,z,-psi)\n", "\n", "# # step 3: anti-clockwise rotation with -theta0 along y-axis: r0,0,0 -> r0,t0,0\n", "# (x,y,z) = rotate_y(x,y,z,-theta0)\n", "\n", "# # step 4: anti-clockwise rotation with phi0 along z-axis: r0,t0,0 -> r0,t0,p0\n", "# (x,y,z) = rotate_z(x,y,z,phi0)\n", "\n", "# # step 5: x,y,z -> r,t,p\n", "# (r,t,p) = xyz2rtp(x,y,z)\n", "\n", "# return (t,p)\n", "\n", "# def rotate_src_rec(ev_info,st_info,theta0,phi0,psi):\n", "# ev_info_rotate = {}\n", "# st_info_rotate = {}\n", "\n", "# # rotate earthquakes\n", "# for key_ev in ev_info:\n", "# ev = ev_info[key_ev]\n", "# (ev.lat,ev.lon,) = rtp_rotation(ev.lat,ev.lon,theta0,phi0,psi)\n", "# ev_info_rotate[key_ev] = ev\n", "\n", "# # rotate stations\n", "# for key_st in st_info:\n", "# st = st_info[key_st]\n", "# (st.lat,st.lon) = rtp_rotation(st.lat,st.lon,theta0,phi0,psi)\n", "# st_info_rotate[key_st] = st\n", "\n", "# return (ev_info_rotate,st_info_rotate)\n", "\n", "# def rotate_src_rec_reverse(ev_info_rotate,st_info_rotate,theta0,phi0,psi):\n", "# ev_info = {}\n", "# st_info = {}\n", "\n", "# # rotate earthquakes\n", "# for key_ev in ev_info_rotate:\n", "# ev = ev_info_rotate[key_ev]\n", "# (ev.lat,ev.lon) = rtp_rotation_reverse(ev.lat,ev.lon,theta0,phi0,psi)\n", "# ev_info[key_ev] = ev\n", "\n", "# # rotate stations\n", "# for key_st in st_info_rotate:\n", "# st = st_info_rotate[key_st]\n", "# (st.lat,st.lon) = rtp_rotation_reverse(st.lat,st.lon,theta0,phi0,psi)\n", "# st_info[key_st] = st\n", "\n", "# return (ev_info,st_info)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# linear_regression(X,Y)\n", "def linear_regression(X,Y):\n", " slope,intercept = np.polyfit(X,Y,deg=1)\n", " fitted_values = slope * X + intercept\n", " residual = Y - fitted_values\n", " SEE = np.std(residual)\n", " return (slope,intercept,SEE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Functions: obtain target information from ev_info and st_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function: output the [lon,lat,dep,weight] of the earthquake\n", "def data_lon_lat_dep_wt_ev(ev_info):\n", " lat = []\n", " lon = []\n", " dep = []\n", " weight = []\n", " for key in ev_info:\n", " lat.append(ev_info[key].lat)\n", " lon.append(ev_info[key].lon)\n", " dep.append(ev_info[key].dep)\n", " try:\n", " weight.append(ev_info[key].tag[\"weight\"])\n", " except:\n", " weight.append(1.0)\n", " return [np.array(lon),np.array(lat),np.array(dep),np.array(weight)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function: output the [lon, lat, dep, ortime] of the earthquake\n", "def data_ev_loc(ev_info):\n", " lat = []\n", " lon = []\n", " dep = []\n", " ortime = []\n", " for key in ev_info:\n", " lat.append(ev_info[key].lat)\n", " lon.append(ev_info[key].lon)\n", " dep.append(ev_info[key].dep)\n", " ortime.append(ev_info[key].ortime.timestamp)\n", " return [np.array(lon),np.array(lat),np.array(dep),np.array(ortime)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function: output the [lon,lat,dep,weight] of the station\n", "def data_lon_lat_ele_wt_st(ev_info,st_info):\n", " names = {}\n", " lat = []\n", " lon = []\n", " ele = []\n", " weight = []\n", " for key_ev in ev_info:\n", " for key_t in ev_info[key_ev].t: # absolute traveltime data\n", " name_st = ev_info[key_ev].t[key_t][0]\n", " names[name_st] = name_st\n", "\n", " for key_t in ev_info[key_ev].cs_dt: # common source differential traveltime data\n", " name_st = ev_info[key_ev].cs_dt[key_t][0]\n", " names[name_st] = name_st\n", " name_st = ev_info[key_ev].cs_dt[key_t][1]\n", " names[name_st] = name_st\n", "\n", " for key_t in ev_info[key_ev].cr_dt: # common receiver differential traveltime data\n", " name_st = ev_info[key_ev].cr_dt[key_t][0]\n", " names[name_st] = name_st\n", "\n", " for name in names: # only output the station which has data\n", " lat.append(st_info[name].lat)\n", " lon.append(st_info[name].lon)\n", " ele.append(st_info[name].ele)\n", " try:\n", " weight.append(st_info[name].tag[\"weight\"])\n", " except:\n", " weight.append(1.0)\n", " return [np.array(lon),np.array(lat),np.array(ele),np.array(weight)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function: output the [dis,time] of all data\n", "def data_dis_time(ev_info,st_info):\n", " all_dis = []\n", " all_time = []\n", " for key_ev in ev_info:\n", " lat_ev = ev_info[key_ev].lat\n", " lon_ev = ev_info[key_ev].lon\n", " dep_ev = ev_info[key_ev].dep\n", " for key_t in ev_info[key_ev].t:\n", " all_time.append(ev_info[key_ev].t[key_t][2])\n", " lat_st = st_info[ev_info[key_ev].t[key_t][0]].lat\n", " lon_st = st_info[ev_info[key_ev].t[key_t][0]].lon\n", " ele_st = st_info[ev_info[key_ev].t[key_t][0]].ele\n", " dis = math.sqrt(cal_dis(lat_ev,lon_ev,lat_st,lon_st)**2 + (dep_ev+ele_st/1000)**2)\n", " all_dis.append(dis)\n", "\n", " return [np.array(all_dis),np.array(all_time)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function: output the [epidis,time] of all data\n", "def data_epidis_time(ev_info,st_info):\n", " all_dis = []\n", " all_time = []\n", " for key_ev in ev_info:\n", " lat_ev = ev_info[key_ev].lat\n", " lon_ev = ev_info[key_ev].lon\n", " for key_t in ev_info[key_ev].t:\n", " all_time.append(ev_info[key_ev].t[key_t][2])\n", " lat_st = st_info[ev_info[key_ev].t[key_t][0]].lat\n", " lon_st = st_info[ev_info[key_ev].t[key_t][0]].lon\n", " dis = cal_dis(lat_ev,lon_ev,lat_st,lon_st)**2\n", " all_dis.append(dis)\n", "\n", " return [np.array(all_dis),np.array(all_time)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# function: output the [cs_dt] of all data\n", "def data_cs_dt(ev_info):\n", " all_time = []\n", " for key_ev in ev_info:\n", " for key_dt in ev_info[key_ev].cs_dt:\n", " all_time.append(ev_info[key_ev].cs_dt[key_dt][3])\n", "\n", " return np.array(all_time)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: data_dis_time_phase(ev_info, st_info, phase_list) Given a list of seismic phases, output the [epicentral distance, arrival time] for each phase.\n", "def data_dis_time_phase(ev_info,st_info,phase_list):\n", " all_dis = {}\n", " all_time = {}\n", " for phase in phase_list:\n", " all_dis[phase] = []\n", " all_time[phase] = []\n", "\n", " for key_ev in ev_info:\n", " lat_ev = ev_info[key_ev].lat\n", " lon_ev = ev_info[key_ev].lon\n", " dep_ev = ev_info[key_ev].dep\n", " for key_t in ev_info[key_ev].t:\n", " phase = key_t.split(\"+\")[1]\n", " if (not phase in phase_list):\n", " continue\n", "\n", " all_time[phase].append(ev_info[key_ev].t[key_t][2])\n", " lat_st = st_info[ev_info[key_ev].t[key_t][0]].lat\n", " lon_st = st_info[ev_info[key_ev].t[key_t][0]].lon\n", " ele_st = st_info[ev_info[key_ev].t[key_t][0]].ele\n", "\n", " dis = math.sqrt(cal_dis(lat_ev,lon_ev,lat_st,lon_st)**2 + (dep_ev+ele_st/1000)**2)\n", " all_dis[phase].append(dis)\n", "\n", " for phase in phase_list:\n", " all_dis[phase] = np.array(all_dis[phase])\n", " all_time[phase] = np.array(all_time[phase])\n", "\n", " return [all_dis,all_time]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: data_lon_lat_dep_wt_ev(ev_info) Outputs the lines connecting station and earthquake for traveltime data as [line_x, line_y].\n", "\n", "def data_line(ev_info,st_info):\n", " line_x = []\n", " line_y = []\n", "\n", " for key_ev in ev_info:\n", " lat_ev = ev_info[key_ev].lat\n", " lon_ev = ev_info[key_ev].lon\n", " for key_t in ev_info[key_ev].t:\n", " lat_st = st_info[ev_info[key_ev].t[key_t][0]].lat\n", " lon_st = st_info[ev_info[key_ev].t[key_t][0]].lon\n", "\n", " line_x.append([lon_ev,lon_st])\n", " line_y.append([lat_ev,lat_st])\n", "\n", " return [line_x,line_y]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: discard some data in ev_info and st_info based on selection criteria" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_ev_region(ev_info, lat1, lat2, lon1, lon2, dep1, dep2) Delete the earthquakes that are out of the specified region.\n", "\n", "def limit_ev_region(ev_info,lat_min,lat_max,lon_min,lon_max,dep_min,dep_max):\n", " count_delete = 0\n", "\n", " del_key_ev = []\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", " lat = ev.lat\n", " lon = ev.lon\n", " dep = ev.dep\n", "\n", " if (lat < min(lat_min,lat_max) or lat > max(lat_min,lat_max) \\\n", " or lon < min(lon_min,lon_max) or lon > max(lon_min,lon_max) \\\n", " or dep < min(dep_min,dep_max) or dep > max(dep_min,dep_max)):\n", " del_key_ev.append(key_ev)\n", " count_delete += 1\n", "\n", " del_key_t = []\n", " for key_t in ev_info[key_ev].cr_dt:\n", " name_ev2 = ev_info[key_ev].cr_dt[key_t][1]\n", " lat2 = ev_info[name_ev2].lat\n", " lon2 = ev_info[name_ev2].lon\n", " dep2 = ev_info[name_ev2].dep\n", " if (lat2 < min(lat_min,lat_max) or lat2 > max(lat_min,lat_max) \\\n", " or lon2 < min(lon_min,lon_max) or lon2 > max(lon_min,lon_max) \\\n", " or dep2 < min(dep_min,dep_max) or dep2 > max(dep_min,dep_max)):\n", "\n", " del_key_t.append(key_t)\n", "\n", " for key_t in del_key_t:\n", " del ev_info[key_ev].cr_dt[key_t]\n", "\n", " ev_info[key_ev].Ncr_dt = len(ev_info[key_ev].cr_dt)\n", "\n", " for key_ev in del_key_ev:\n", " del ev_info[key_ev]\n", "\n", " print(\"delete %d events out of the region, now %d earthquakes are retained within the study region\"%(count_delete,len(ev_info)))\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_st_region(ev_info, st_info, lat1, lat2, lon1, lon2) Delete the stations that are out of the specified region.\n", "\n", "def limit_st_region(ev_info,st_info,lat1,lat2,lon1,lon2):\n", "\n", " for key_ev in ev_info:\n", " # delete the station out of the region in the absolute traveltime data\n", " del_key_t = []\n", " for key_t in ev_info[key_ev].t:\n", " name_st = ev_info[key_ev].t[key_t][0]\n", " lat_st = st_info[name_st].lat\n", " lon_st = st_info[name_st].lon\n", " if(lat_st < min(lat1,lat2) or lat_st > max(lat1,lat2) or lon_st < min(lon1,lon2) or lon_st > max(lon1,lon2)):\n", " del_key_t.append(key_t)\n", "\n", " for key_t in del_key_t:\n", " del ev_info[key_ev].t[key_t]\n", " ev_info[key_ev].Nt = len(ev_info[key_ev].t)\n", "\n", " # delete the station out of the region in the common source differential traveltime data\n", " del_key_t = []\n", " for key_t in ev_info[key_ev].cs_dt:\n", " name_st1 = ev_info[key_ev].cs_dt[key_t][0]\n", " lat_st1 = st_info[name_st1].lat\n", " lon_st1 = st_info[name_st1].lon\n", "\n", " name_st2 = ev_info[key_ev].cs_dt[key_t][1]\n", " lat_st2 = st_info[name_st2].lat\n", " lon_st2 = st_info[name_st2].lon\n", " if(lat_st1 < min(lat1,lat2) or lat_st1 > max(lat1,lat2) or lon_st1 < min(lon1,lon2) or lon_st1 > max(lon1,lon2) \\\n", " or lat_st2 < min(lat1,lat2) or lat_st2 > max(lat1,lat2) or lon_st2 < min(lon1,lon2) or lon_st2 > max(lon1,lon2)):\n", " del_key_t.append(key_t)\n", "\n", " for key_t in del_key_t:\n", " del ev_info[key_ev].cs_dt[key_t]\n", " ev_info[key_ev].Ncs_dt = len(ev_info[key_ev].cs_dt)\n", "\n", " # delete the station out of the region in the common receiver differential traveltime data\n", " del_key_st = []\n", " for key_t in ev_info[key_ev].cr_dt:\n", " name_st = ev_info[key_ev].cr_dt[key_t][0]\n", " lat_st = st_info[name_st].lat\n", " lon_st = st_info[name_st].lon\n", " if(lat_st < min(lat1,lat2) or lat_st > max(lat1,lat2) or lon_st < min(lon1,lon2) or lon_st > max(lon1,lon2)):\n", " del_key_st.append(key_t)\n", "\n", " for key_t in del_key_st:\n", " del ev_info[key_ev].cr_dt[key_t]\n", " ev_info[key_ev].Ncr_dt = len(ev_info[key_ev].cr_dt)\n", "\n", " return ev_info\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_epi_dis(ev_info, st_info, epi_dis1, epi_dis2) Delete the stations with epicentral distance in the range from epi_dis1 to epi_dis2.\n", "\n", "def limit_epi_dis(ev_info,st_info,epi_dis1,epi_dis2):\n", "\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", "\n", " lat_ev = ev.lat\n", " lon_ev = ev.lon\n", "\n", " # delete the absolute traveltime data\n", " del_key_t = []\n", " for key_t in ev.t:\n", " stname = ev.t[key_t][0]\n", " lat_st = st_info[stname].lat\n", " lon_st = st_info[stname].lon\n", " dis = cal_dis(lat_ev, lon_ev, lat_st, lon_st)\n", " if (dis > epi_dis1 and dis < epi_dis2):\n", " del_key_t.append(key_t)\n", " for key_t in del_key_t:\n", " del ev.t[key_t]\n", " ev.Nt = len(ev.t)\n", "\n", " # delete the common source differential traveltime data\n", " del_key_t = []\n", " for key_t in ev.cs_dt:\n", " for i in range(2):\n", " stname = ev.t[key_t][i]\n", " lat_st = st_info[stname].lat\n", " lon_st = st_info[stname].lon\n", " dis = cal_dis(lat_ev, lon_ev, lat_st, lon_st)\n", "\n", " if (dis > epi_dis1 and dis < epi_dis2):\n", " del_key_t.append(key_t)\n", " break\n", " for key_t in del_key_t:\n", " del ev.cs_dt[key_t]\n", " ev.Ncs_dt = len(ev.cs_dt)\n", "\n", " # delete the common receiver differential traveltime data\n", " del_key_t = []\n", " for key_t in ev.cr_dt:\n", " stname = ev.cr_dt[key_t][0]\n", " lat_st = st_info[stname].lat\n", " lon_st = st_info[stname].lon\n", " dis = cal_dis(lat_ev, lon_ev, lat_st, lon_st)\n", " if (dis > epi_dis1 and dis < epi_dis2):\n", " del_key_t.append(key_t)\n", "\n", " lat_ev2 = ev_info[ev.cr_dt[key_t][1]].lat\n", " lon_ev2 = ev_info[ev.cr_dt[key_t][1]].lon\n", " dis = cal_dis(lat_ev2, lon_ev2, lat_st, lon_st)\n", " if (dis > epi_dis1 and dis < epi_dis2):\n", " del_key_t.append(key_t)\n", "\n", " for key_t in del_key_t:\n", " del ev.cr_dt[key_t]\n", " ev.Ncr_dt = len(ev.cr_dt)\n", "\n", "\n", " ev_info[key_ev] = ev\n", "\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_data_residual(ev_info, st_info, slope, intercept, up, down) Limit the data within the range defined by the line time = dis * slope + intercept and the bounds up and down.\n", "\n", "# remove outliers, only retain data satisfying: slope * dis + intercept + down < time < slope * dis + intercept + up\n", "def limit_data_residual(ev_info,st_info,slope,intercept,up,down):\n", " for key_ev in ev_info:\n", " lat_ev = ev_info[key_ev].lat\n", " lon_ev = ev_info[key_ev].lon\n", " dep_ev = ev_info[key_ev].dep\n", " del_key_t = []\n", " for key_t in ev_info[key_ev].t:\n", " name_st = ev_info[key_ev].t[key_t][0]\n", " lat_st = st_info[name_st].lat\n", " lon_st = st_info[name_st].lon\n", " ele_st = st_info[name_st].ele\n", " dis = math.sqrt(cal_dis(lat_ev,lon_ev,lat_st,lon_st)**2 + (dep_ev+ele_st/1000)**2)\n", " residual = ev_info[key_ev].t[key_t][2] - (slope*dis+intercept)\n", "\n", " if (residual < down or residual > up):\n", " del_key_t.append(key_t)\n", "\n", " for key_t in del_key_t:\n", " del ev_info[key_ev].t[key_t]\n", "\n", " for key_ev in ev_info:\n", " ev_info[key_ev].Nt = len(ev_info[key_ev].t)\n", "\n", " return ev_info\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_data_phase(ev_info, phase_list) Retain only the specified seismic phases.\n", "\n", "def limit_data_phase(ev_info,phase_list):\n", " for key_ev in ev_info:\n", " # process the absolute traveltime data\n", " new_t = {}\n", " for key_t in ev_info[key_ev].t:\n", " phase = ev_info[key_ev].t[key_t][1]\n", " if phase in phase_list:\n", " new_t[key_t] = ev_info[key_ev].t[key_t]\n", "\n", " ev_info[key_ev].t = new_t\n", " ev_info[key_ev].Nt = len(ev_info[key_ev].t)\n", "\n", " # process the common source differential traveltime data\n", " new_t = {}\n", " for key_t in ev_info[key_ev].cs_dt:\n", " phase = ev_info[key_ev].cs_dt[key_t][2]\n", " phase = phase.split(\",\")[0]\n", " if phase in phase_list:\n", " new_t[key_t] = ev_info[key_ev].cs_dt[key_t]\n", "\n", " ev_info[key_ev].cs_dt = new_t\n", " ev_info[key_ev].Ncs_dt = len(ev_info[key_ev].cs_dt)\n", "\n", " # process the common receiver differential traveltime data\n", " new_t = {}\n", " for key_t in ev_info[key_ev].cr_dt:\n", " phase = ev_info[key_ev].cr_dt[key_t][2]\n", " phase = phase.split(\",\")[0]\n", " if phase in phase_list:\n", " new_t[key_t] = ev_info[key_ev].cr_dt[key_t]\n", "\n", " ev_info[key_ev].cr_dt = new_t\n", " ev_info[key_ev].Ncr_dt = len(ev_info[key_ev].cr_dt)\n", "\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_min_Nt(min_Nt_thd, ev_info) Delete the earthquakes with the number of data less than min_Nt_thd.\n", "\n", "def limit_min_Nt(min_Nt_thd, ev_info):\n", " Nev = len(ev_info)\n", "\n", " del_key_ev = []\n", " for key_ev in ev_info:\n", " if(ev_info[key_ev].Nt < min_Nt_thd):\n", " del_key_ev.append(key_ev)\n", "\n", " for key_ev in del_key_ev:\n", " del ev_info[key_ev]\n", "\n", " print(\"Original data set has %d earthquakes, %d earthquakes are deleted, %d earthquakes are retained\"%(Nev,len(del_key_ev),len(ev_info)))\n", "\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_azi_gap(gap_thd) Calculate the azimuthal gap for all events and delete events with a gap greater than gap_thd.\n", "def limit_azi_gap(gap_thd,ev_info,st_info):\n", " Nev = len(ev_info)\n", "\n", " del_key_ev = []\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", " gap = cal_azi_gap(ev,st_info)\n", " if (gap > gap_thd):\n", " del_key_ev.append(key_ev)\n", " else:\n", " ev_info[key_ev].tag[\"azi_gap\"] = gap\n", " for key_ev in del_key_ev:\n", " del ev_info[key_ev]\n", "\n", " print(\"Original data set has %d earthquakes, %d earthquakes are deleted, %d earthquakes are retained\"%(Nev,len(del_key_ev),len(ev_info)))\n", "\n", " return ev_info\n", "\n", "# Function: cal_azi_gap(ev, st_info) Calculate the azimuthal gap of a single earthquake.\n", "def cal_azi_gap(ev,st_info):\n", " azi_all = []\n", " lat_ev = ev.lat\n", " lon_ev = ev.lon\n", " stlist = {}\n", " for key in ev.t:\n", " stname = ev.t[key][0]\n", " if (not stname in stlist):\n", " lat_st = st_info[stname].lat\n", " lon_st = st_info[stname].lon\n", " azi = cal_azimuth(lat_ev, lon_ev, lat_st, lon_st)\n", " azi_all.append(azi)\n", " stlist[stname] = 1\n", "\n", " azi_all.sort()\n", " if(len(azi_all) < 2):\n", " return 360.0\n", " else:\n", " gap = 0.0\n", " for i in range(len(azi_all)-1):\n", " gap = max(gap,azi_all[i+1] - azi_all[i])\n", " gap = max(gap,azi_all[0] + 360 - azi_all[-1])\n", " return gap\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: limit_earthquake_decluster_Nt(ev_info, dlat, dlon, ddep, Top_N) Divide the region into several subdomains, sort by the number of arrival times, and retain only the top Top_N earthquakes with the most arrival times in each box.\n", "# option 3, declustering. Divide the region into several subdomains, retain the Top N earthquakes in terms of the number of arrival times in each subdomain.\n", "def limit_earthquake_decluster_Nt(ev_info,dlat,dlon,ddep,Top_N):\n", " # subdivide earthquakes into different subdomains\n", " [ev_info,tag2name] = tag_event_cluster(dlat,dlon,ddep,ev_info)\n", "\n", " # sort earthquakes in the same subdomain\n", " # Sort the quality of earthquakes within each tag based on the number of arrivals.\n", " tag2name = sort_cluster_Nt(ev_info, tag2name)\n", "\n", " # only retain Top_N earthquakes in each subdomain\n", " # Within each tag, prioritize selecting the top Top_N earthquakes.\n", " [ev_info,tag2name] = limit_decluster(ev_info, tag2name,Top_N)\n", "\n", " return ev_info\n", "\n", "\n", "\n", "# Function: tag_event_cluster(size_lat, size_lon, size_dep, ev_info) Subdivide the study area, assign each earthquake to a subregion, and place it in a tag.\n", "def tag_event_cluster(size_lat,size_lon,size_dep,ev_info):\n", " tag2name = {}\n", " for key_ev in ev_info:\n", " name = ev_info[key_ev].name\n", " lat = ev_info[key_ev].lat\n", " lon = ev_info[key_ev].lon\n", " dep = ev_info[key_ev].dep\n", " tag = \"%d_%d_%d\"%(math.floor(lon/size_lon),math.floor(lat/size_lat),math.floor(dep/size_dep))\n", " ev_info[key_ev].tag[\"cluster\"] = tag\n", "\n", " if (tag in tag2name):\n", " tag2name[tag].append(name)\n", " else:\n", " tag2name[tag] = []\n", " tag2name[tag].append(name)\n", "\n", " return [ev_info,tag2name]\n", "\n", "# Function: sort_cluster_Nt(ev_info, tag2name) Sort the quality of earthquakes within each tag based on the number of arrivals.\n", "def sort_cluster_Nt(ev_info, tag2name):\n", " for key_tag in tag2name:\n", " names_ev = tag2name[key_tag]\n", " Nt = []\n", " for key_ev in names_ev:\n", " Nt.append(len(ev_info[key_ev].t))\n", "\n", " # Sort the earthquakes within each tag based on the number of arrivals.\n", " sorted_Nt = sorted(enumerate(Nt), key=lambda x: x[1], reverse=True)\n", " tag2name[key_tag] = []\n", " for index, Nt in sorted_Nt:\n", " tag2name[key_tag].append(names_ev[index])\n", "\n", " return tag2name\n", "\n", "# Function: limit_cluster(ev_info, tag2name, Max) Prioritize selecting the top Max earthquakes within each tag.\n", "def limit_decluster(ev_info, tag2name, Max):\n", " del_key_ev = []\n", " for key_tag in tag2name:\n", " names_ev = tag2name[key_tag]\n", "\n", " if(len(names_ev) > Max):\n", " tag2name[key_tag] = names_ev[0:Max]\n", " for i in range(Max,len(names_ev)): # Delete earthquakes that exceed the threshold in the sorted list.\n", " del_key_ev.append(names_ev[i])\n", "\n", " for key_ev in del_key_ev:\n", " del ev_info[key_ev]\n", "\n", " return [ev_info,tag2name]\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: assign weights to earthquakes, stations, and data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: box_weighting_ev(ev_info, dlat, dlon, ddep) Assign box-weight to the earthquakes.\n", "def box_weighting_ev(ev_info,dlon,dlat,ddep):\n", "\n", " # categorization\n", " distribute = {}\n", " all_tag_wt = {}\n", "\n", " for key_ev in ev_info:\n", " lat_id = math.floor((ev_info[key_ev].lat) / dlat)\n", " lon_id = math.floor((ev_info[key_ev].lon) / dlon)\n", " dep_id = math.floor((ev_info[key_ev].dep) / ddep)\n", "\n", " tag = '%d_%d_%d'%(lat_id,lon_id,dep_id)\n", " if (tag in distribute):\n", " distribute[tag] += 1\n", " else:\n", " distribute[tag] = 1\n", "\n", " max_weight = 0\n", " for tag in distribute:\n", " all_tag_wt[tag] = 1.0/math.sqrt(distribute[tag])\n", " max_weight = max(max_weight,all_tag_wt[tag])\n", "\n", " for key_ev in ev_info:\n", " lat_id = math.floor((ev_info[key_ev].lat) / dlat)\n", " lon_id = math.floor((ev_info[key_ev].lon) / dlon)\n", " dep_id = math.floor((ev_info[key_ev].dep) / ddep)\n", "\n", " tag = '%d_%d_%d'%(lat_id,lon_id,dep_id)\n", "\n", " ev_info[key_ev].tag[\"weight\"] = all_tag_wt[tag]/max_weight\n", "\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: geographical_weighting_ev_rough(ev_info, dlat, dlon, ddep) Assign geographical weighting to the earthquakes roughly.\n", "def geographical_weighting_ev_rough(ev_info,dlat,dlon,ddep,coefficient = 0.5):\n", "\n", " # categorization\n", " distribute = {}\n", " all_tag_wt = {}\n", "\n", " for key_ev in ev_info:\n", " lat_id = int(ev_info[key_ev].lat/dlat)\n", " lon_id = int(ev_info[key_ev].lon/dlat)\n", " dep_id = int(ev_info[key_ev].dep/ddep)\n", "\n", "\n", " tag = '%d_%d_%d'%(lat_id,lon_id,dep_id)\n", " if (tag in distribute):\n", " distribute[tag] += 1\n", " else:\n", " distribute[tag] = 1\n", "\n", " # Calculate the weight of each category.\n", " delta0 = 0\n", " for tag1 in distribute:\n", " tmp1 = tag1.split('_')\n", " # evlat1 = float(tmp1[0])*dlat; evlon1 = float(tmp1[1])*dlon; evdep1 = float(tmp1[2])*ddep\n", "\n", " for tag2 in distribute:\n", " tmp2 = tag2.split('_')\n", " # evlat2 = float(tmp2[0])*dlat; evlon2 = float(tmp2[1])*dlon; evdep2 = float(tmp2[2])*ddep\n", "\n", " # distance of id\n", " delta_tp = math.sqrt((int(tmp1[0]) - int(tmp2[0]))**2 + (int(tmp1[1]) - int(tmp2[1]))**2 + (int(tmp1[2]) - int(tmp2[2]))**2)\n", " delta0 = delta0 + distribute[tag1] * distribute[tag2] * delta_tp\n", "\n", " delta0 = delta0/(len(ev_info)**2) * coefficient\n", "\n", " max_weight = 0.0\n", " for tag1 in distribute:\n", " tmp1 = tag1.split('_')\n", " # evlat1 = float(tmp1[0])*dlat; evlon1 = float(tmp1[1])*dlon; evdep1 = float(tmp1[2])*ddep\n", "\n", " weight = 0\n", " for tag2 in distribute:\n", " tmp2 = tag2.split('_')\n", " # evlat2 = float(tmp2[0])*dlat; evlon2 = float(tmp2[1])*dlon; evdep2 = float(tmp2[2])*ddep\n", "\n", " delta_tp = math.sqrt((int(tmp1[0]) - int(tmp2[0]))**2 + (int(tmp1[1]) - int(tmp2[1]))**2 + (int(tmp1[2]) - int(tmp2[2]))**2)\n", " weight = weight + math.exp(-(delta_tp/delta0)**2) * distribute[tag2]\n", "\n", " all_tag_wt[tag1] = (1.0/weight)\n", " max_weight = max(max_weight,1.0/weight)\n", "\n", " # Assign weights to each earthquake based on its tag.\n", " for key_ev in ev_info:\n", " lat_id = int(ev_info[key_ev].lat/dlat)\n", " lon_id = int(ev_info[key_ev].lon/dlon)\n", " dep_id = int(ev_info[key_ev].dep/ddep)\n", "\n", " tag = '%d_%d_%d'%(lat_id,lon_id,dep_id)\n", "\n", " ev_info[key_ev].tag[\"weight\"] = all_tag_wt[tag]/max_weight\n", " return ev_info\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: box_weighting_st(ev_info, st_info, dlat, dlon) Assign geographical weighting to the stations roughly.\n", "def box_weighting_st(ev_info,st_info,dlon,dlat):\n", "\n", " [lon_ev,lat_ev,dep_ev,wt_ev] = data_lon_lat_dep_wt_ev(ev_info)\n", "\n", " # Integrate all involved stations.\n", " wt_st = {}\n", " name_st = {}\n", " for key_ev in ev_info:\n", " for key_t in ev_info[key_ev].t:\n", " name_rec = ev_info[key_ev].t[key_t][0]\n", " wt_st[name_rec] = -1.0\n", " name_st[name_rec] = 1\n", "\n", " # categorization\n", " distribute = {}\n", " all_tag_wt = {}\n", "\n", " # Count the number of stations in each subdomain.\n", " for key_st in name_st:\n", " lat_id = math.floor((st_info[key_st].lat) / dlat)\n", " lon_id = math.floor((st_info[key_st].lon) / dlon)\n", "\n", " tag = '%d_%d'%(lat_id,lon_id)\n", " if (tag in distribute):\n", " distribute[tag] += 1\n", " else:\n", " distribute[tag] = 1\n", "\n", " max_weight = 0\n", " for tag in distribute:\n", " all_tag_wt[tag] = 1.0/math.sqrt(distribute[tag])\n", " max_weight = max(max_weight,all_tag_wt[tag])\n", "\n", " # Assign weights to each station based on its tag.\n", " for key_st in name_st:\n", " lat_id = math.floor((st_info[key_st].lat) / dlat)\n", " lon_id = math.floor((st_info[key_st].lon) / dlon)\n", " tag = '%d_%d'%(lat_id,lon_id)\n", " wt_st[key_st] = all_tag_wt[tag]/max_weight\n", "\n", " # modify weight tag in st_info\n", " for key_t in wt_st:\n", " st_info[key_t].tag[\"weight\"] = wt_st[key_t]\n", "\n", " # modify weight of abs data ev_info\n", " for key_ev in ev_info:\n", " for key_t in ev_info[key_ev].t:\n", " name_rec = ev_info[key_ev].t[key_t][0]\n", " ev_info[key_ev].t[key_t][3] = wt_st[name_rec]\n", "\n", " return [ev_info,st_info]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: geographical_weighting_st(ev_info,st_info) Assign geographical weighting to the stations roughly.\n", "def geographical_weighting_st(ev_info,st_info,coefficient = 0.5):\n", "\n", " # Integrate all involved stations.\n", " wt_st = {}\n", " name_st = {}\n", " for key_ev in ev_info:\n", " for key_t in ev_info[key_ev].t:\n", " name_rec = ev_info[key_ev].t[key_t][0]\n", " wt_st[name_rec] = -1.0\n", " name_st[name_rec] = 1\n", "\n", " # Calculate the weight of each station.\n", " delta0 = 0\n", " for key_st1 in name_st:\n", " stlat1 = st_info[key_st1].lat\n", " stlon1 = st_info[key_st1].lon\n", "\n", " for key_st2 in name_st:\n", " stlat2 = st_info[key_st2].lat\n", " stlon2 = st_info[key_st2].lon\n", "\n", " delta_tp = cal_dis(stlat1,stlon1,stlat2,stlon2)\n", " delta0 = delta0 + delta_tp\n", "\n", " delta0 = delta0/(len(wt_st)**2)*coefficient\n", "\n", " max_weight = 0.0\n", " for key_st1 in name_st:\n", " stlat1 = st_info[key_st1].lat\n", " stlon1 = st_info[key_st1].lon\n", "\n", " weight = 0\n", " for key_st2 in name_st:\n", " stlat2 = st_info[key_st2].lat\n", " stlon2 = st_info[key_st2].lon\n", "\n", " delta_tp = cal_dis(stlat1,stlon1,stlat2,stlon2)\n", " weight = weight + math.exp(-(delta_tp/delta0)**2)\n", "\n", " wt_st[key_st1] = (1.0/weight)\n", " max_weight = max(max_weight,1.0/weight)\n", "\n", " for key_st1 in wt_st:\n", " wt_st[key_st1] = wt_st[key_st1]/max_weight\n", "\n", " # Add weight to each data point in the earthquakes.\n", " for key_ev in ev_info:\n", " for key_t in ev_info[key_ev].t:\n", " name_rec = ev_info[key_ev].t[key_t][0]\n", " if (not name_rec in wt_st):\n", " ValueError(\"The station of the data is not in the calculation list\")\n", "\n", " if (len(ev_info[key_ev].t[key_t])==3):\n", " ev_info[key_ev].t[key_t].append(wt_st[name_rec])\n", " elif (len(ev_info[key_ev].t[key_t])==4):\n", " ev_info[key_ev].t[key_t][3] = wt_st[name_rec]\n", " else:\n", " ValueError(\"Error in the weight information of the absolute traveltime data\")\n", "\n", " # modify weight tag in st_info\n", " for key_t in wt_st:\n", " st_info[key_t].tag[\"weight\"] = wt_st[key_t]\n", "\n", " # modify weight of abs data ev_info\n", " for key_ev in ev_info:\n", " for key_t in ev_info[key_ev].t:\n", " name_rec = ev_info[key_ev].t[key_t][0]\n", " ev_info[key_ev].t[key_t][3] = wt_st[name_rec]\n", "\n", " return [ev_info,st_info]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function: add noise into data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function：assign_gaussian_noise():\n", "def assign_gaussian_noise(ev_info,sigma):\n", "\n", " # Record which seismic phases correspond to each station.\n", " st2phase = {} # Station name -> [Keys of absolute arrival time data related to this station]\n", "\n", "\n", " for key_ev in ev_info:\n", " # Absolute arrival time noise\n", " for key_t in ev_info[key_ev].t:\n", " stname = ev_info[key_ev].t[key_t][0]\n", " ev_info[key_ev].t[key_t][2] = ev_info[key_ev].t[key_t][2] + np.random.normal(0,sigma)\n", " if(stname in st2phase):\n", " st2phase[stname].append(key_t)\n", " else:\n", " st2phase[stname] = [key_t]\n", "\n", " for key_ev in ev_info:\n", " # Double-difference arrival time noise\n", " for key_dt in ev_info[key_ev].cs_dt:\n", " stname1 = ev_info[key_ev].cs_dt[key_dt][0]\n", " stname2 = ev_info[key_ev].cs_dt[key_dt][1]\n", " t1 = -999\n", " t2 = -999\n", " # Search for the arrival time of the data.\n", " if (stname1 in st2phase):\n", " for key_t in st2phase[stname1]:\n", " if (key_t in ev_info[key_ev].t):\n", " t1 = ev_info[key_ev].t[key_t][2]\n", " break\n", " if (stname2 in st2phase):\n", " for key_t in st2phase[stname2]:\n", " if (key_t in ev_info[key_ev].t):\n", " t2 = ev_info[key_ev].t[key_t][2]\n", " break\n", "\n", " if (t1 == -999 or t2 == -999):\n", " # If there is no absolute arrival time data, the double-difference data residuals increase by a factor of sqrt(2) in noise.\n", " ev_info[key_ev].cs_dt[key_dt][3] = ev_info[key_ev].cs_dt[key_dt][3] + np.random.normal(0,sigma*np.sqrt(2))\n", " print('no data: ', key_ev, key_dt)\n", " else:\n", " # If there is absolute arrival time data, the double-difference data is obtained by subtraction.\n", " ev_info[key_ev].cs_dt[key_dt][3] = t1 - t2\n", "\n", " # Common station double-difference arrival time\n", " for key_dt in ev_info[key_ev].cr_dt:\n", " stname = ev_info[key_ev].cr_dt[key_dt][0]\n", " key_ev2 = ev_info[key_ev].cr_dt[key_dt][1]\n", "\n", " t1 = -999\n", " t2 = -999\n", " # Search for the arrival time of the data.\n", " if (stname in st2phase):\n", " for key_t in st2phase[stname]:\n", " if (key_t in ev_info[key_ev].t):\n", " t1 = ev_info[key_ev].t[key_t][2]\n", " break\n", " else:\n", " print('not found 1: ', key_ev, key_t)\n", "\n", " for key_t in st2phase[stname]:\n", " if (key_t in ev_info[key_ev2].t):\n", " t2 = ev_info[key_ev2].t[key_t][2]\n", " break\n", " else:\n", " print('not found 2: ', key_ev, key_t)\n", "\n", " if (t1 == -999 or t2 == -999):\n", " # If there is no absolute arrival time data, the double-difference data residuals increase by a factor of sqrt(2) in noise.\n", " ev_info[key_ev].cr_dt[key_dt][3] = ev_info[key_ev].cr_dt[key_dt][3] + np.random.normal(0,sigma*np.sqrt(2))\n", " print('no data: ', key_ev, key_dt)\n", " else:\n", " # If there is absolute arrival time data, the double-difference data is obtained by subtraction.\n", " ev_info[key_ev].cr_dt[key_dt][3] = t1 - t2\n", "\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function：assign_uniform_noise_to_ev():\n", "def assign_uniform_noise_to_ev(ev_info, range_lat, range_lon, range_dep, range_time):\n", "\n", " # Loop through all earthquakes and assign noise to them.\n", " ev_noise = {} # Name of the earthquake -> noise of [lat,lon,dep,ortime]\n", " # loop list of earthquakes\n", " for key_ev in ev_info:\n", " evname = key_ev\n", " if (evname in ev_noise):\n", " print(\"error: repeated earthquake name\")\n", " exit()\n", " else:\n", " # generate noise\n", " ev_noise[evname] = np.random.uniform(-1,1,4) * np.array([range_lat,range_lon,range_dep,range_time])\n", "\n", " # Add noise to each data point.\n", " for key_ev in ev_info:\n", "\n", " # Absolute arrival time noise\n", " for key_t in ev_info[key_ev].t:\n", "\n", " ev_info[key_ev].t[key_t][2] = ev_info[key_ev].t[key_t][2] - ev_noise[key_ev][3]\n", "\n", "\n", " # Double-difference arrival time noise (double-difference arrival time remains unchanged)\n", "\n", " # Common station double-difference arrival time\n", " for key_dt in ev_info[key_ev].cr_dt:\n", " key_ev2 = ev_info[key_ev].cr_dt[key_dt][1]\n", "\n", " if (key_ev2 in ev_noise):\n", " ev_info[key_ev].cr_dt[key_dt][3] = ev_info[key_ev].cr_dt[key_dt][3] - ev_noise[key_ev][3] + ev_noise[key_ev2][3]\n", " else:\n", " print(\"earthquake %s is not included in ev_list\"%(key_ev2))\n", " ev_noise[key_ev2] = np.random.uniform(-1,1,4) * np.array([range_lat,range_lon,range_dep,range_time])\n", " ev_info[key_ev].cr_dt[key_dt][3] = ev_info[key_ev].cr_dt[key_dt][3] - ev_noise[key_ev][3] + ev_noise[key_ev2][3]\n", "\n", "\n", " # Add noise to each earthquake.\n", " for key_ev in ev_noise:\n", " ev_info[key_ev].lat = ev_info[key_ev].lat + ev_noise[key_ev][0]\n", " ev_info[key_ev].lon = ev_info[key_ev].lon + ev_noise[key_ev][1]\n", " ev_info[key_ev].dep = abs(ev_info[key_ev].dep + ev_noise[key_ev][2])\n", " ev_info[key_ev].ortime = ev_info[key_ev].ortime + ev_noise[key_ev][3]\n", "\n", "\n", " return ev_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: generate differential traveltime" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: generate_cs_dif(ev_info, st_info, dis_thd, azi_thd) Generate double-difference arrival times from absolute arrival times, with inter-station distance less than dis_thd and azimuthal difference less than azi_thd.\n", "# function: generate common source differential traveltime data from absolute traveltime data, the stations separation is less than dis_thd, the azimuth difference is less than azi_thd\n", "def generate_cs_dif(ev_info,st_info,dis_thd,azi_thd):\n", " count_t = 0\n", " count_cs_dt = 0\n", "\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", "\n", " lat_ev = ev.lat\n", " lon_ev = ev.lon\n", "\n", " # traverse all arrival times\n", " name_st_list = [] # names of stations\n", " t_list = [] # traveltime\n", " wt_list = [] # weight\n", " for key_t in ev.t:\n", " name_st_list.append(ev.t[key_t][0])\n", " t_list.append(ev.t[key_t][2])\n", " wt_list.append(ev.t[key_t][3])\n", " count_t += 1\n", "\n", " # search for possible double-difference arrival times\n", " for id_st1 in range(len(name_st_list)-1):\n", " name_st1 = name_st_list[id_st1]\n", " lat_st1 = st_info[name_st1].lat\n", " lon_st1 = st_info[name_st1].lon\n", " t_st1 = t_list[id_st1]\n", " wt_st1 = wt_list[id_st1]\n", "\n", " for id_st2 in range(id_st1+1,len(name_st_list)):\n", " name_st2 = name_st_list[id_st2]\n", " lat_st2 = st_info[name_st2].lat\n", " lon_st2 = st_info[name_st2].lon\n", " t_st2 = t_list[id_st2]\n", " wt_st2 = wt_list[id_st2]\n", "\n", " dis = cal_dis(lat_st1,lon_st1,lat_st2,lon_st2)\n", " azi_st1 = cal_azimuth(lat_ev,lon_ev,lat_st1,lon_st1)\n", " azi_st2 = cal_azimuth(lat_ev,lon_ev,lat_st2,lon_st2)\n", "\n", " azi_dif = abs(azi_st1 - azi_st2)\n", "\n", " if(dis < dis_thd and (azi_dif < azi_thd or (360-azi_dif) < azi_thd )):\n", " ev.cs_dt[\"%s+%s+%s\"%(name_st1,name_st2,\"P,cs\")] = [name_st1,name_st2,\"P,cs\",t_st1-t_st2,(wt_st1+wt_st2)/2]\n", " count_cs_dt += 1\n", "\n", " ev_info[key_ev].Ncs_dt = len(ev.cs_dt)\n", "\n", " print('we generate %d common source differential traveltimes from %s absolute traveltimes'%(count_cs_dt,count_t))\n", " return ev_info\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: generate_cr_dif(ev_info, st_info, dis_thd) Generate common station double-difference arrival times from absolute arrival times, with inter-event distance less than dis_thd.\n", "# Function: generate common receiver differential traveltime data from absolute traveltime data, the earthquake separation is less than dis_thd\n", "def generate_cr_dif(ev_info,st_info,dis_thd,azi_thd):\n", "\n", " # Construct mapping：rec2src[name_ev] -> {name_st: [name_ev, name_st, t, wt]; name_st: [name_ev, name_st, t, wt]; ...}\n", " rec2src = build_rec_src_map(ev_info,dis_thd)\n", " print(\"rec to src map generation finished\")\n", "\n", " # Construct double-difference data association mapping：rec2src_pair[key_t]\n", " rec2src_pair = build_rec_src_pair_map(rec2src)\n", " print(\"rec to src_pair map generation finished\")\n", "\n", " for key_t in rec2src_pair:\n", " name_st = key_t.split('+')[0]\n", " lat_st = st_info[name_st].lat\n", " lon_st = st_info[name_st].lon\n", "\n", " for ev_tag in rec2src_pair[key_t]:\n", " name_ev1 = rec2src_pair[key_t][ev_tag][0]\n", " lat_ev1 = ev_info[name_ev1].lat\n", " lon_ev1 = ev_info[name_ev1].lon\n", " dep_ev1 = ev_info[name_ev1].dep\n", "\n", " name_ev2 = rec2src_pair[key_t][ev_tag][1]\n", " lat_ev2 = ev_info[name_ev2].lat\n", " lon_ev2 = ev_info[name_ev2].lon\n", " dep_ev2 = ev_info[name_ev2].dep\n", "\n", " dis_xy = cal_dis(lat_ev1,lon_ev1,lat_ev2,lon_ev2)\n", " dis_z = abs(dep_ev1 - dep_ev2)\n", " dis = math.sqrt(dis_xy**2 + dis_z**2)\n", " if(dis > dis_thd): # limit of the distance between two earthquakes\n", " continue\n", "\n", " azi1 = cal_azimuth(lat_ev1,lon_ev1,lat_st,lon_st)\n", " azi2 = cal_azimuth(lat_ev2,lon_ev2,lat_st,lon_st)\n", " azi_dif = abs(azi1 - azi2)\n", "\n", " if(azi_dif > azi_thd and (360-azi_dif) > azi_thd): # limit of the azimuth difference between two earthquakes\n", " continue\n", "\n", " t_ev1 = ev_info[name_ev1].t[key_t][2]\n", " t_ev2 = ev_info[name_ev2].t[key_t][2]\n", " wt_ev1 = ev_info[name_ev1].t[key_t][3] * ev_info[name_ev1].tag[\"weight\"]\n", " wt_ev2 = ev_info[name_ev2].t[key_t][3] * ev_info[name_ev2].tag[\"weight\"]\n", " # The actual data weight is wt_ev1 + wt_ev2, but in TomoATT calculations, we need to divide it by ev_info[name_ev1].tag[\"weight\"].\n", " wt = (wt_ev1 + wt_ev2)/2/ev_info[name_ev1].tag[\"weight\"]\n", "\n", " ev_info[name_ev1].cr_dt[\"%s+%s+%s\"%(name_st,name_ev2,\"P,cr\")] = [name_st,name_ev2,\"P,cr\",t_ev1-t_ev2,wt]\n", "\n", " # Count the number of double-difference data points.\n", " count_cr_dt = 0\n", " count_t = 0\n", " for key_ev in ev_info:\n", " ev_info[key_ev].Ncr_dt = len(ev_info[key_ev].cr_dt)\n", " count_cr_dt += ev_info[key_ev].Ncr_dt\n", " count_t += ev_info[key_ev].Nt\n", "\n", " print('we generate %d common receiver differential traveltimes from %s absolute traveltimes'%(count_cr_dt,count_t))\n", "\n", " return ev_info\n", "\n", "# Construct mapping: rec2src = {key_t: dict_tag; key_t: dict_tag; ...}\n", "# dict_tag = {tag: list_name_ev; tag: list_name_ev; ...}\n", "# list_name_ev = [name_ev1, name_ev2, ...]\n", "# Assign earthquakes to different subregions based on their locations. The subregion size is dlat * dlon * ddep. When performing common station double-difference calculations, only earthquake pairs within the same subregion or adjacent subregions will be considered.\n", "def build_rec_src_map(ev_info,dis_thd):\n", " rec2src = {}\n", " for key_ev in ev_info:\n", " name_ev = ev_info[key_ev].name\n", " lat = ev_info[key_ev].lat\n", " lon = ev_info[key_ev].lon\n", " dep = ev_info[key_ev].dep\n", " tag_dep = math.floor(dep/dis_thd)\n", " tag_lat = math.floor(lat/180*math.pi*R_earth/dis_thd)\n", " tag_lon = math.floor(lon/180*math.pi*R_earth*math.cos(lat)/dis_thd)\n", " tag = \"%d_%d_%d\"%(tag_lon,tag_lat,tag_dep)\n", "\n", "\n", " for key_t in ev_info[key_ev].t:\n", "\n", " # create dictionary\n", " if (not key_t in rec2src):\n", " rec2src[key_t] = {tag:[]}\n", " elif (not tag in rec2src[key_t]):\n", " rec2src[key_t][tag] = []\n", "\n", " # Add data\n", " rec2src[key_t][tag].append(name_ev)\n", "\n", " return rec2src\n", "\n", "# Function: generate_adjacent_tag(tag) Generate tags surrounding the given tag.\n", "def generate_adjacent_tag(tag): # Excluding the tag itself.\n", " adjacent_tag_list = []\n", " tmp = tag.split('_')\n", " tag_lon = int(tmp[0])\n", " tag_lat = int(tmp[1])\n", " tag_dep = int(tmp[2])\n", "\n", " for i in range(-1,2):\n", " for j in range(-1,2):\n", " for k in range(-1,2):\n", " if(i == 0 and j == 0 and k == 0):\n", " continue\n", " adjacent_tag_list.append(\"%d_%d_%d\"%(tag_lon+i,tag_lat+j,tag_dep+k))\n", "\n", " return adjacent_tag_list\n", "\n", "\n", "# construct mapping：rec2src_pair\n", "def build_rec_src_pair_map(rec2src):\n", " rec2src_pair = {}\n", "\n", " for key_t in rec2src:\n", " rec2src_pair[key_t] = {}\n", "\n", " for tag in rec2src[key_t]:\n", " name_ev_list1 = rec2src[key_t][tag]\n", "\n", " name_ev_list2 = rec2src[key_t][tag]\n", " adjacent_tag_list = generate_adjacent_tag(tag)\n", " for adjacent_tag in adjacent_tag_list:\n", " if (adjacent_tag in rec2src[key_t]): # If the surrounding tag's region has earthquakes, add them to the earthquake list.\n", " name_ev_list2 = name_ev_list2 + rec2src[key_t][adjacent_tag]\n", "\n", " # Find possible earthquake pairs.\n", " for id_ev1 in range(len(name_ev_list1)-1):\n", " name_ev1 = name_ev_list1[id_ev1]\n", "\n", " for id_ev2 in range(id_ev1+1,len(name_ev_list2)): # Starting from id_ev1 + 1 already excludes duplicate earthquakes within the tag.\n", " name_ev2 = name_ev_list2[id_ev2]\n", "\n", " ev_tag1 = \"%s+%s\"%(name_ev1,name_ev2)\n", " ev_tag2 = \"%s+%s\"%(name_ev2,name_ev1)\n", "\n", " if(ev_tag1 in rec2src_pair[key_t] or ev_tag2 in rec2src_pair[key_t]):\n", " continue\n", "\n", " rec2src_pair[key_t][ev_tag1] = [name_ev1,name_ev2]\n", "\n", "\n", " return rec2src_pair" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: read and write src_rec.dat file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: reorder_src(ev) Reorder the earthquake IDs. If the earthquake has no data, the ID is -999.\n", "def reorder_src(ev_info):\n", "\n", " ev_id = 0\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", "\n", " if(ev.Nt + ev.Ncs_dt + ev.Ncr_dt == 0):\n", " ev.id = -999\n", " else:\n", " ev_info[key_ev].id = ev_id\n", " ev_id += 1\n", "\n", " return ev_info\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: read_src_rec_file(fname) Read the src_rec.dat file.\n", "#\n", "def read_src_rec_file(fname):\n", " ev_info = {}\n", " st_info = {}\n", "\n", " tmp_ev_info = {}\n", "\n", " doc = open(fname,'r')\n", " doc_input = doc.readlines()\n", " doc.close()\n", "\n", " cc = 0\n", " for info in doc_input:\n", " tmp=info.split()\n", " if (cc == 0): # event line\n", " ev = Event()\n", " # 1 2000 1 2 20 28 37.270 38.2843 39.0241 11.00 3.60 8 1725385\n", " # id_ev = int(tmp[0])\n", " ev.id = int(tmp[0])\n", " year = int(tmp[1])\n", " month = int(tmp[2])\n", " day = int(tmp[3])\n", " hour = int(tmp[4])\n", " minute = int(tmp[5])\n", " second = float(tmp[6])\n", " ev.ortime = UTCDateTime(year,month,day,hour,minute,0) + second\n", " ev.lat = float(tmp[7])\n", " ev.lon = float(tmp[8])\n", " ev.dep = float(tmp[9])\n", " ev.mag = float(tmp[10])\n", " ev.Nt = 0\n", " ev.Ncs_dt = 0\n", " ev.Ncr_dt = 0\n", " ev.t = {}\n", " ev.cs_dt = {}\n", " ev.cr_dt = {}\n", " ndata = int(tmp[11])\n", " name_ev = tmp[12]\n", " ev.name = name_ev\n", " cc += 1\n", " try:\n", " ev.tag[\"weight\"] = float(tmp[13])\n", " except:\n", " pass\n", "\n", " if (ndata == 0):\n", " cc = 0\n", " ev_info[name_ev] = ev\n", "\n", " else: # data line\n", " # 1 1 MYA 38.3261 38.4253 1050.0000 P 52.46 6.630 weight\n", " if(len(tmp) < 10): # absolue traveltime data\n", " name_st = tmp[2]\n", " phase = tmp[6]\n", " if (phase == \"PG\"):\n", " phase = \"Pg\"\n", " if (phase == \"PB\"):\n", " phase = \"Pb\"\n", " if (phase == \"PN\"):\n", " phase = \"Pn\"\n", "\n", " if (not name_st in st_info):\n", " st = Station()\n", " st.name = name_st\n", " st.id = float(tmp[1])\n", " st.lat = float(tmp[3])\n", " st.lon = float(tmp[4])\n", " st.ele = float(tmp[5])\n", " st_info[name_st] = st\n", "\n", " time = float(tmp[7])\n", " if(len(tmp) == 9):\n", " weight = float(tmp[8])\n", " else:\n", " weight = 1.0\n", " ev.t[\"%s+%s\"%(name_st,phase)] = [name_st,phase,time,weight]\n", " ev.Nt += 1\n", "\n", " else: # differential traveltime data\n", " phase = tmp[11]\n", " if (phase.__contains__(\"cr\")): # common receiver differential traveltime\n", " # evid stid1 stname1 lat1 lon1 eve1 evid2 evname2 lat2 lon2 dep2 phase,cr diftime weight\n", "\n", " name_st1 = tmp[2]\n", " if (not name_st1 in st_info): # add station to the station list\n", " st = Station()\n", " st.name = name_st1\n", " st.id = float(tmp[1])\n", " st.lat = float(tmp[3])\n", " st.lon = float(tmp[4])\n", " st.ele = float(tmp[5])\n", " st_info[name_st1] = st\n", "\n", " name_ev2 = tmp[7]\n", " # add earthquake to the temp earthquake list\n", " ev2 = Event()\n", " ev2.name = name_ev2\n", " ev2.id = float(tmp[6])\n", " ev2.lat = float(tmp[8])\n", " ev2.lon = float(tmp[9])\n", " ev2.dep = float(tmp[10])\n", " tmp_ev_info[name_ev2] = ev2\n", "\n", "\n", " dif_time = float(tmp[12])\n", " if(len(tmp) == 14):\n", " weight = float(tmp[13])\n", " else:\n", " weight = 1.0\n", " ev.cr_dt[\"%s+%s+%s\"%(name_st1,name_ev2,phase)] = [name_st1,name_ev2,phase,dif_time,weight]\n", " ev.Ncr_dt += 1\n", "\n", " else: # common source differential traveltime\n", " # evid stid1 stname1 lat1 lon1 eve1 stid2 stname2 lat2 lon2 ele2 phase,cs diftime weight\n", "\n", " name_st1 = tmp[2]\n", " if (not name_st1 in st_info):\n", " st = Station()\n", " st.name = name_st1\n", " st.id = len(st_info)\n", " st.lat = float(tmp[3])\n", " st.lon = float(tmp[4])\n", " st.ele = float(tmp[5])\n", " st_info[name_st1] = st\n", "\n", " name_st2 = tmp[7]\n", " if (not name_st2 in st_info):\n", " st = Station()\n", " st.name = name_st2\n", " st.id = float(tmp[6])\n", " st.lat = float(tmp[8])\n", " st.lon = float(tmp[9])\n", " st.ele = float(tmp[10])\n", " st_info[name_st2] = st\n", "\n", " dif_time = float(tmp[12])\n", " if(len(tmp) == 14):\n", " weight = float(tmp[13])\n", " else:\n", " weight = 1.0\n", " ev.cs_dt[\"%s+%s+%s\"%(name_st1,name_st2,phase)] = [name_st1,name_st2,phase,dif_time,weight]\n", " ev.Ncs_dt += 1\n", "\n", " if (cc == ndata): # end of the event data\n", " cc = 0\n", " ev_info[name_ev] = ev\n", " else:\n", " cc += 1\n", "\n", " # Add earthquakes from the temporary earthquake list to the main earthquake list.\n", " for key_ev in tmp_ev_info:\n", " if (not key_ev in ev_info):\n", " ev_info[key_ev] = tmp_ev_info[key_ev]\n", "\n", " return [ev_info,st_info]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: write_src_rec_file(fname, ev_info, st_info) Output the src_rec.dat file.\n", "def write_src_rec_file(fname,ev_info,st_info):\n", " ev_info = reorder_src(ev_info)\n", " doc_src_rec = open(fname,'w')\n", "\n", " min_lat = 9999\n", " max_lat = -9999\n", " min_lon = 9999\n", " max_lon = -9999\n", " min_dep = 9999\n", " max_dep = -9999\n", "\n", " record_ev = {}\n", " record_st = {}\n", " Nt_total = 0\n", " Ncs_dt_total = 0\n", " Ncr_dt_total = 0\n", "\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", " evid = ev.id\n", " year = ev.ortime.year\n", " month = ev.ortime.month\n", " day = ev.ortime.day\n", " hour = ev.ortime.hour\n", " minute = ev.ortime.minute\n", " second = ev.ortime.second\n", " msec = ev.ortime.microsecond\n", " lat_ev = ev.lat\n", " lon_ev = ev.lon\n", " dep_ev = ev.dep\n", " mag = ev.mag\n", " ndata = ev.Nt + ev.Ncs_dt + ev.Ncr_dt\n", " name_ev = ev.name\n", " try:\n", " weight_ev = ev.tag[\"weight\"]\n", " except:\n", " weight_ev = 1.0\n", "\n", " if(ndata == 0): # if the earthquake has no data, do not output it\n", " continue\n", "\n", " doc_src_rec.write('%7d %6d %2d %2d %2d %2d %5.2f %9.4f %9.4f %9.4f %5.2f %7d %s %7.3f\\n'%(\\\n", " evid,year,month,day,hour,minute,second+msec/1000000,lat_ev,lon_ev,dep_ev,mag,ndata,name_ev,weight_ev))\n", "\n", " min_lat = min(min_lat, lat_ev)\n", " max_lat = max(max_lat, lat_ev)\n", " min_lon = min(min_lon, lon_ev)\n", " max_lon = max(max_lon, lon_ev)\n", " min_dep = min(min_dep, dep_ev)\n", " max_dep = max(max_dep, dep_ev)\n", "\n", " record_ev[name_ev] = 1 # record this earthquake\n", " Nt_total += ev.Nt\n", " Ncs_dt_total += ev.Ncs_dt\n", " Ncr_dt_total += ev.Ncr_dt\n", "\n", " for key_t in ev.t:\n", " data = ev.t[key_t]\n", " st = st_info[data[0]]\n", " stid = st.id\n", " name_st = st.name\n", " lat_st = st.lat\n", " lon_st = st.lon\n", " ele_st = st.ele\n", " phase = data[1]\n", " time = data[2]\n", " try:\n", " weight_data = data[3]\n", " except:\n", " weight_data = 1.0\n", " doc_src_rec.write('%7d %7d %6s %9.4f %9.4f %9.4f %s %8.4f %7.3f \\n'%(evid,stid,name_st,lat_st,lon_st,ele_st,phase,time,weight_data))\n", "\n", " min_lat = min(min_lat, lat_st)\n", " max_lat = max(max_lat, lat_st)\n", " min_lon = min(min_lon, lon_st)\n", " max_lon = max(max_lon, lon_st)\n", " min_dep = min(min_dep, -ele_st/1000)\n", " max_dep = max(max_dep, -ele_st/1000)\n", "\n", " record_st[name_st] = 1 # record this station\n", "\n", " for key_t in ev.cs_dt:\n", " data = ev.cs_dt[key_t]\n", " st1 = st_info[data[0]]\n", " stid1 = st1.id\n", " name_st1= st1.name\n", " lat_st1 = st1.lat\n", " lon_st1 = st1.lon\n", " ele_st1 = st1.ele\n", " st2 = st_info[data[1]]\n", " stid2 = st2.id\n", " name_st2= st2.name\n", " lat_st2 = st2.lat\n", " lon_st2 = st2.lon\n", " ele_st2 = st2.ele\n", " phase = data[2]\n", " time = data[3]\n", " try:\n", " weight_data = data[4]\n", " except:\n", " weight_data = 1.0\n", " doc_src_rec.write('%7d %7d %6s %9.4f %9.4f %9.4f %7d %6s %9.4f %9.4f %9.4f %s %8.4f %7.3f \\n'%(\\\n", " evid,stid1,name_st1,lat_st1,lon_st1,ele_st1,stid2,name_st2,lat_st2,lon_st2,ele_st2,phase,time,weight_data))\n", "\n", " min_lat = min(min_lat, lat_st1)\n", " max_lat = max(max_lat, lat_st1)\n", " min_lon = min(min_lon, lon_st1)\n", " max_lon = max(max_lon, lon_st1)\n", " min_dep = min(min_dep, -ele_st1/1000)\n", " max_dep = max(max_dep, -ele_st1/1000)\n", "\n", " min_lat = min(min_lat, lat_st2)\n", " max_lat = max(max_lat, lat_st2)\n", " min_lon = min(min_lon, lon_st2)\n", " max_lon = max(max_lon, lon_st2)\n", " min_dep = min(min_dep, -ele_st2/1000)\n", " max_dep = max(max_dep, -ele_st2/1000)\n", "\n", " record_st[name_st1] = 1 # record this station\n", " record_st[name_st2] = 1 # record this station\n", "\n", " for key_t in ev.cr_dt:\n", " data = ev.cr_dt[key_t]\n", " st = st_info[data[0]]\n", " stid = st.id\n", " name_st = st.name\n", " lat_st = st.lat\n", " lon_st = st.lon\n", " ele_st = st.ele\n", " ev2 = ev_info[data[1]]\n", " evid2 = ev2.id\n", " name_ev2= ev2.name\n", " lat_ev2 = ev2.lat\n", " lon_ev2 = ev2.lon\n", " dep_ev2 = ev2.dep\n", " phase = data[2]\n", " time = data[3]\n", " try:\n", " weight_data = data[4]\n", " except:\n", " weight_data = 1.0\n", " doc_src_rec.write('%7d %7d %6s %9.4f %9.4f %9.4f %7d %6s %9.4f %9.4f %9.4f %s %8.4f %7.3f \\n'%(\\\n", " evid,stid,name_st,lat_st,lon_st,ele_st,evid2,name_ev2,lat_ev2,lon_ev2,dep_ev2,phase,time,weight_data))\n", "\n", " min_lat = min(min_lat, lat_st)\n", " max_lat = max(max_lat, lat_st)\n", " min_lon = min(min_lon, lon_st)\n", " max_lon = max(max_lon, lon_st)\n", " min_dep = min(min_dep, -ele_st/1000)\n", " max_dep = max(max_dep, -ele_st/1000)\n", "\n", " min_lat = min(min_lat, lat_ev2)\n", " max_lat = max(max_lat, lat_ev2)\n", " min_lon = min(min_lon, lon_ev2)\n", " max_lon = max(max_lon, lon_ev2)\n", " min_dep = min(min_dep, dep_ev2)\n", " max_dep = max(max_dep, dep_ev2)\n", "\n", " record_ev[name_ev2] = 1 # record this station\n", " record_st[name_st] = 1 # record this station\n", "\n", " doc_src_rec.close()\n", "\n", " print(\"src_rec.dat has been outputed: %d events, %d stations, %d abs traveltime, %d cs_dif traveltime, %d cr_dif traveltime. \" \\\n", " %(len(record_ev),len(record_st),Nt_total,Ncs_dt_total,Ncr_dt_total))\n", " print(\"earthquake and station region, lat: %6.1f - %6.1f, lon: %6.1f - %6.1f, dep: %6.1f - %6.1f\"%(min_lat,max_lat,min_lon,max_lon,min_dep,max_dep) )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: write_src_list_file(fname, ev_info) Output the event list file.\n", "def write_src_list_file(fname,ev_info):\n", " doc_ev_list = open(fname,'w')\n", "\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", " evid = ev.id\n", " lat_ev = ev.lat\n", " lon_ev = ev.lon\n", " dep_ev = ev.dep\n", " mag = ev.mag\n", " name_ev = ev.name\n", " if (ev.id == -999): # if the earthquake has no data, do not output it\n", " continue\n", " doc_ev_list.write(\"%7d %s %s %9.4f %9.4f %9.4f %5.2f \\n\"%(evid,name_ev,ev.ortime,lat_ev,lon_ev,dep_ev,mag))\n", " doc_ev_list.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: write_rec_list_file(fname, ev_info, st_info) Output the station list file.\n", "def write_rec_list_file(fname,ev_info,st_info):\n", " doc_st_list = open(fname,'w')\n", "\n", " st_list = {}\n", " for key_ev in ev_info:\n", " ev = ev_info[key_ev]\n", "\n", " for key_t in ev.t:\n", " data = ev.t[key_t]\n", " st = st_info[data[0]]\n", " name_st = st.name\n", " lat_st = st.lat\n", " lon_st = st.lon\n", " ele_st = st.ele\n", " if(not name_st in st_list):\n", " doc_st_list.write(\"%6s %9.4f %9.4f %10.4f \\n\"%(name_st,lat_st,lon_st,ele_st))\n", " st_list[name_st] = 1\n", "\n", " for key_t in ev.cs_dt:\n", " data = ev.cs_dt[key_t]\n", " st1 = st_info[data[0]]\n", " name_st1= st1.name\n", " lat_st1 = st1.lat\n", " lon_st1 = st1.lon\n", " ele_st1 = st1.ele\n", " st2 = st_info[data[0]]\n", " name_st2= st2.name\n", " lat_st2 = st2.lat\n", " lon_st2 = st2.lon\n", " ele_st2 = st2.ele\n", " if(not name_st1 in st_list):\n", " doc_st_list.write(\"%6s %9.4f %9.4f %10.4f \\n\"%(name_st1,lat_st1,lon_st1,ele_st1))\n", " st_list[name_st1] = 1\n", " if(not name_st2 in st_list):\n", " doc_st_list.write(\"%6s %9.4f %9.4f %10.4f \\n\"%(name_st2,lat_st2,lon_st2,ele_st2))\n", " st_list[name_st2] = 1\n", "\n", " for key_t in ev.cr_dt:\n", " data = ev.cr_dt[key_t]\n", " st = st_info[data[0]]\n", " name_st = st.name\n", " lat_st = st.lat\n", " lon_st = st.lon\n", " ele_st = st.ele\n", " if(not name_st in st_list):\n", " doc_st_list.write(\"%6s %9.4f %9.4f %10.4f \\n\"%(name_st,lat_st,lon_st,ele_st))\n", " st_list[name_st] = 1\n", "\n", " doc_st_list.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: read objective function file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: read_objective_function_file(path)\n", "def read_objective_function_file(path):\n", "\n", " full_curve = []\n", " location_curve = []\n", " model_curve = []\n", "\n", " with open('%s/objective_function.txt'%(path)) as f:\n", " for i,line in enumerate(f):\n", " tmp = line.split(',')\n", " if (tmp[0].__contains__(\"#\")):\n", " continue # skip the comment line\n", " \n", " iter = int(tmp[0])\n", " tag = tmp[1]\n", " obj = float(tmp[2])\n", " obj_abs = float(tmp[3])\n", " obj_cs = float(tmp[4])\n", " obj_cr = float(tmp[5])\n", " obj_tele = float(tmp[6])\n", " tmp2 = tmp[7].split('/')\n", " mean = float(tmp2[0])\n", " std = float(tmp2[1])\n", " tmp2 = tmp[8].split('/')\n", " mean_abs = float(tmp2[0])\n", " std_abs = float(tmp2[1])\n", " tmp2 = tmp[9].split('/')\n", " mean_cs = float(tmp2[0])\n", " std_cs = float(tmp2[1])\n", " tmp2 = tmp[10].split('/')\n", " mean_cr = float(tmp2[0])\n", " std_cr = float(tmp2[1])\n", " tmp2 = tmp[11].split('/')\n", " mean_tele = float(tmp2[0])\n", " std_tele = float(tmp2[1])\n", "\n", " full_curve.append([obj,obj_abs,obj_cs,obj_cr,obj_tele,mean,std,mean_abs,std_abs,mean_cs,std_cs,mean_cr,std_cr,mean_tele,std_tele])\n", " if tag.__contains__(\"relocation\"):\n", " location_curve.append([obj,obj_abs,obj_cs,obj_cr,obj_tele,mean,std,mean_abs,std_abs,mean_cs,std_cs,mean_cr,std_cr,mean_tele,std_tele])\n", " if tag.__contains__(\"model\"):\n", " model_curve.append([obj,obj_abs,obj_cs,obj_cr,obj_tele,mean,std,mean_abs,std_abs,mean_cs,std_cs,mean_cr,std_cr,mean_tele,std_tele])\n", "\n", " return np.array(full_curve),np.array(location_curve),np.array(model_curve)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: read inversion grid file" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: read the inversion grid file\n", "def read_inversion_grid_file(path):\n", "\n", " inv_grid_vel = []\n", " inv_grid_ani = []\n", "\n", " switch = False\n", " igrid = -1\n", " with open('%s/inversion_grid.txt'%(path)) as f:\n", " tmp_inv_grid = []\n", " for i,line in enumerate(f):\n", "\n", " # read the number of inversion grid in dep, lat, lon directions\n", " if(i==0):\n", " tmp = line.split()\n", " ndep = int(tmp[1])\n", " nlines = 3*ndep+1 # The number of rows for each inversion grid is 3*ndep+1\n", "\n", " iline = i % nlines\n", "\n", " if(iline == 0): # info: number of inversion grid\n", " tmp = line.split()\n", " if (int(tmp[0]) > igrid):\n", " igrid = int(tmp[0])\n", " else: # change from vel to ani\n", " switch = True\n", " igrid = int(tmp[0])\n", "\n", " else: # info location of inversion grid\n", " iline_sub = (iline-1) % 3\n", " if(iline_sub == 0): # dep\n", " tmp = line.split()\n", " dep = float(tmp[0])\n", " if(iline_sub == 1): # list of lat\n", " lat_list = line.split()\n", " if(iline_sub == 2): # list of lon\n", " lon_list = line.split()\n", "\n", " # add inversion grid\n", " for lat in lat_list:\n", " for lon in lon_list:\n", " tmp_inv_grid.append([float(lon), float(lat), dep])\n", "\n", " if(iline == nlines-1): # the last line of inversion grid\n", " if(switch):\n", " inv_grid_ani.append(tmp_inv_grid)\n", " else:\n", " inv_grid_vel.append(tmp_inv_grid)\n", " tmp_inv_grid = []\n", "\n", " return [np.array(inv_grid_vel),np.array(inv_grid_ani)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: for plotting" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: fig_ev_st_distribution_dep(ev_info, st_info) Plot the distribution of the earthquakes and stations, color-coded by earthquake depth.\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "\n", "def fig_ev_st_distribution_dep(ev_info,st_info):\n", "\n", " [lon_ev,lat_ev,dep_ev,wt_ev] = data_lon_lat_dep_wt_ev(ev_info)\n", " [lon_st,lat_st,ele_st,wt_st] = data_lon_lat_ele_wt_st(ev_info,st_info)\n", "\n", " min_lon = min(min(lon_ev),min(lon_st))\n", " max_lon = max(max(lon_ev),max(lon_st))\n", "\n", " min_lat = min(min(lat_ev),min(lat_st))\n", " max_lat = max(max(lat_ev),max(lat_st))\n", "\n", " max_dep = max(dep_ev)\n", "\n", " # Insert a value that does not affect the plot to make the colorbar range look better.\n", " lon_ev = np.insert(lon_ev, 0, 9999); lat_ev = np.insert(lat_ev, 0, 9999); dep_ev = np.insert(dep_ev, 0, 0);\n", "\n", " fig = plt.figure(figsize=(12,12))\n", " gridspace = GridSpec(12,12,figure = fig)\n", "\n", " xrange = max_lon - min_lon + 1.0\n", " yrange = max_lat - min_lat + 1.0\n", "\n", " if (xrange > yrange):\n", " fig_x_size = 6\n", " fig_y_size = round(6*yrange/xrange)\n", " else:\n", " fig_x_size = round(6*xrange/yrange)\n", " fig_y_size = 6\n", "\n", "\n", " ax1 = fig.add_subplot(gridspace[0:fig_y_size,0:fig_x_size])\n", "\n", " bar_ev = ax1.scatter(lon_ev,lat_ev,c=dep_ev,cmap=\"jet\",label = \"src\",s = 3)\n", " # ax1.plot(lon_st,lat_st,'rv',label = \"rec\",markersize = 6)\n", " bar_st = ax1.scatter(lon_st,lat_st,c=\"red\",label = \"rec\",s = 100,marker='v',edgecolors='white')\n", "\n", " ax1.legend(fontsize = 14)\n", " ax1.tick_params(axis='x',labelsize=18)\n", " ax1.tick_params(axis='y',labelsize=18)\n", " ax1.set_xlabel('Lon',fontsize=18)\n", " ax1.set_ylabel('Lat',fontsize=18)\n", " ax1.set_xlim((min_lon - (max_lon - min_lon)*0.1,max_lon + (max_lon - min_lon)*0.1))\n", " ax1.set_ylim((min_lat - (max_lat - min_lat)*0.1,max_lat + (max_lat - min_lat)*0.1))\n", "\n", "\n", " ax2 = fig.add_subplot(gridspace[0:fig_y_size, fig_x_size+1 : fig_x_size+3])\n", "\n", " ax2.scatter(dep_ev,lat_ev,c=dep_ev,cmap=\"jet\",label = \"src\",s = 3)\n", "\n", " ax2.tick_params(axis='x',labelsize=18)\n", " ax2.tick_params(axis='y',labelsize=18)\n", " ax2.set_xlabel('Dep',fontsize=18)\n", " ax2.set_ylabel('Lat',fontsize=18)\n", " ax2.set_xlim((-max_dep*0.05,max_dep*1.1))\n", " ax2.set_ylim((min_lat - (max_lat - min_lat)*0.1,max_lat + (max_lat - min_lat)*0.1))\n", "\n", "\n", " ax3 = fig.add_subplot(gridspace[fig_y_size+1:fig_y_size+3,0:fig_x_size])\n", "\n", " ax3.scatter(lon_ev,dep_ev,c=dep_ev,cmap=\"jet\",label = \"src\",s = 3)\n", "\n", " ax3.tick_params(axis='x',labelsize=18)\n", " ax3.tick_params(axis='y',labelsize=18)\n", " ax3.set_xlabel('Lon',fontsize=18)\n", " ax3.set_ylabel('Dep',fontsize=18)\n", " ax3.set_xlim((min_lon - (max_lon - min_lon)*0.1,max_lon + (max_lon - min_lon)*0.1))\n", " ax3.set_ylim((-max_dep*0.05,max_dep*1.1))\n", " ax3.invert_yaxis()\n", "\n", " # Place the colorbar on a new axis.\n", " ax4 = fig.add_subplot(gridspace[fig_y_size+2:fig_y_size+3,fig_x_size+1:fig_x_size+3])\n", " cbar1 = plt.colorbar(bar_ev, ax=ax4,orientation='horizontal')\n", " cbar1.set_label('Depth of earthquakes',fontsize=16)\n", " cbar1.ax.tick_params(axis='x', labelsize=16) # Colorbar font size.\n", "\n", " # Hide the borders of the axes.\n", " ax4.spines['top'].set_visible(False)\n", " ax4.spines['right'].set_visible(False)\n", " ax4.spines['bottom'].set_visible(False)\n", " ax4.spines['left'].set_visible(False)\n", "\n", " # Hide the tick values of the axes.\n", " ax4.set_xticks([])\n", " ax4.set_yticks([])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Function: fig_ev_st_distribution_wt(ev_info, st_info) Plot the distribution of the earthquakes and stations, color-coded by weight.\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "from matplotlib.colors import ListedColormap\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "import math\n", "\n", "def fig_ev_st_distribution_wt(ev_info,st_info):\n", "\n", "\n", " [lon_ev,lat_ev,dep_ev,wt_ev] = data_lon_lat_dep_wt_ev(ev_info)\n", " [lon_st,lat_st,ele_st,wt_st] = data_lon_lat_ele_wt_st(ev_info,st_info)\n", "\n", " # Insert a value that does not affect the plot to make the colorbar range look better.\n", " lon_ev = np.insert(lon_ev, 0, lon_ev[0]); lat_ev = np.insert(lat_ev, 0, lat_ev[0]); dep_ev = np.insert(dep_ev, 0, dep_ev[0]); wt_ev = np.insert(wt_ev, 0, 0.0)\n", " lon_ev = np.insert(lon_ev, 0, lon_ev[0]); lat_ev = np.insert(lat_ev, 0, lat_ev[0]); dep_ev = np.insert(dep_ev, 0, dep_ev[0]); wt_ev = np.insert(wt_ev, 0, 1.0)\n", " lon_st = np.insert(lon_st, 0, lon_st[0]); lat_st = np.insert(lat_st, 0, lat_st[0]); ele_st = np.insert(ele_st, 0, ele_st[0]); wt_st = np.insert(wt_st, 0, 0.0)\n", " lon_st = np.insert(lon_st, 0, lon_st[0]); lat_st = np.insert(lat_st, 0, lat_st[0]); ele_st = np.insert(ele_st, 0, ele_st[0]); wt_st = np.insert(wt_st, 0, 1.0)\n", "\n", " min_lon = min(min(lon_ev),min(lon_st))\n", " max_lon = max(max(lon_ev),max(lon_st))\n", "\n", " min_lat = min(min(lat_ev),min(lat_st))\n", " max_lat = max(max(lat_ev),max(lat_st))\n", "\n", " max_dep = max(dep_ev)\n", "\n", " fig = plt.figure(figsize=(12,12))\n", " gridspace = GridSpec(12,12,figure = fig)\n", "\n", " xrange = max_lon - min_lon + 1.0\n", " yrange = max_lat - min_lat + 1.0\n", "\n", " if (xrange > yrange):\n", " fig_x_size = 6\n", " fig_y_size = round(6*yrange/xrange)\n", " else:\n", " fig_x_size = round(6*xrange/yrange)\n", " fig_y_size = 6\n", "\n", "\n", " ax1 = fig.add_subplot(gridspace[0:fig_y_size,0:fig_x_size])\n", "\n", " bar_ev = ax1.scatter(lon_ev,lat_ev,c=wt_ev,cmap=\"jet\",label = \"src\",s = 3)\n", " bar_st = ax1.scatter(lon_st,lat_st,c=wt_st,cmap=\"jet\",label = \"rec\",s = 100,marker='^',edgecolors='white')\n", "\n", " ax1.legend(fontsize = 14)\n", " ax1.tick_params(axis='x',labelsize=18)\n", " ax1.tick_params(axis='y',labelsize=18)\n", " ax1.set_xlabel('Lon',fontsize=18)\n", " ax1.set_ylabel('Lat',fontsize=18)\n", " ax1.set_xlim((min_lon - (max_lon - min_lon)*0.1,max_lon + (max_lon - min_lon)*0.1))\n", " ax1.set_ylim((min_lat - (max_lat - min_lat)*0.1,max_lat + (max_lat - min_lat)*0.1))\n", "\n", "\n", " ax2 = fig.add_subplot(gridspace[0:fig_y_size, fig_x_size+1 : fig_x_size+3])\n", "\n", " ax2.scatter(dep_ev,lat_ev,c=wt_ev,cmap=\"jet\",label = \"src\",s = 3)\n", "\n", " ax2.tick_params(axis='x',labelsize=18)\n", " ax2.tick_params(axis='y',labelsize=18)\n", " ax2.set_xlabel('Dep',fontsize=18)\n", " ax2.set_ylabel('Lat',fontsize=18)\n", " ax2.set_xlim((-max_dep*0.05,max_dep*1.1))\n", " ax2.set_ylim((min_lat - (max_lat - min_lat)*0.1,max_lat + (max_lat - min_lat)*0.1))\n", "\n", "\n", " ax3 = fig.add_subplot(gridspace[fig_y_size+1:fig_y_size+3,0:fig_x_size])\n", "\n", " ax3.scatter(lon_ev,dep_ev,c=wt_ev,cmap=\"jet\",label = \"src\",s = 3)\n", "\n", " ax3.tick_params(axis='x',labelsize=18)\n", " ax3.tick_params(axis='y',labelsize=18)\n", " ax3.set_xlabel('Lon',fontsize=18)\n", " ax3.set_ylabel('Dep',fontsize=18)\n", " ax3.set_xlim((min_lon - (max_lon - min_lon)*0.1,max_lon + (max_lon - min_lon)*0.1))\n", " ax3.set_ylim((-max_dep*0.05,max_dep*1.1))\n", " ax3.invert_yaxis()\n", "\n", " # Place the colorbar on a new axis.\n", " ax4 = fig.add_subplot(gridspace[fig_y_size+2:fig_y_size+3,fig_x_size+1:fig_x_size+3])\n", " cbar1 = plt.colorbar(bar_st, ax=ax4,orientation='horizontal')\n", " cbar1.set_label('Weight of stations',fontsize=16)\n", " cbar1.ax.tick_params(axis='x', labelsize=16) # colorbar font size.\n", " # Hide the borders of the axes.\n", " ax4.spines['top'].set_visible(False)\n", " ax4.spines['right'].set_visible(False)\n", " ax4.spines['bottom'].set_visible(False)\n", " ax4.spines['left'].set_visible(False)\n", "\n", " # Hide the tick values of the axes.\n", " ax4.set_xticks([])\n", " ax4.set_yticks([])\n", "\n", " # Place the colorbar on a new axis.\n", " ax5 = fig.add_subplot(gridspace[fig_y_size+1:fig_y_size+2,fig_x_size+1:fig_x_size+3])\n", " cbar1 = plt.colorbar(bar_ev, ax=ax5,orientation='horizontal')\n", " cbar1.set_label('Weight of earthquakes',fontsize=16)\n", " cbar1.ax.tick_params(axis='x', labelsize=16) # colorbar font size.\n", " # Hide the borders of the axes.\n", " ax5.spines['top'].set_visible(False)\n", " ax5.spines['right'].set_visible(False)\n", " ax5.spines['bottom'].set_visible(False)\n", " ax5.spines['left'].set_visible(False)\n", "\n", " # Hide the tick values of the axes.\n", " ax5.set_xticks([])\n", " ax5.set_yticks([])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot and function: plot the distance-time scatter plot, remove the outliers.\n", "# Limit the data within the range defined by the line time = dis * slope + intercept and the bounds up and down.\n", "# Remove outliers, only retain data satisfying: slope * dis + intercept + down < time < slope * dis + intercept + up.\n", "\n", "def fig_data_plot_remove_outliers(ev_info,st_info,slope,intercept,up,down,dis_min,dis_max):\n", "\n", " fig = plt.figure(figsize=(10,10))\n", " gridspace = GridSpec(6,6,figure = fig)\n", " ax2 = fig.add_subplot(gridspace[0:6, 0:6])\n", "\n", " # plot original data\n", " [dis_obs,time_obs] = data_dis_time(ev_info,st_info)\n", " ax2.plot(dis_obs,time_obs,'r.',markersize=1.5,label = \"discarded\")\n", "\n", " # remove outliers, only retain data satisfying: slope * dis + intercept + down < time < slope * dis + intercept + up\n", " ev_info = limit_data_residual(ev_info,st_info,slope,intercept,up,down)\n", "\n", " [dis_obs,time_obs] = data_dis_time(ev_info,st_info)\n", " ax2.plot(dis_obs,time_obs,'b.',markersize=1.5,label = \"retained\")\n", "\n", " ax2.plot([dis_min,dis_max],[slope*dis_min+intercept+up,slope*dis_max+intercept+up],'b-',linewidth=2)\n", " ax2.plot([dis_min,dis_max],[slope*dis_min+intercept+down,slope*dis_max+intercept+down],'b-',linewidth=2)\n", " ax2.plot([dis_min,dis_max],[slope*dis_min+intercept,slope*dis_max+intercept],'k-',linewidth=2)\n", "\n", " ax2.legend(fontsize = 14)\n", " ax2.tick_params(axis='x',labelsize=18)\n", " ax2.tick_params(axis='y',labelsize=18)\n", " ax2.set_xlabel('Distance (km)',fontsize=18)\n", " ax2.set_ylabel('Traveltime',fontsize=18)\n", " ax2.set_xlim((dis_min,dis_max))\n", " ax2.set_ylim((intercept+down-5,slope*dis_max+intercept+up+5))\n", "\n", " return ev_info" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot: distance-time scatter plot of given phases.\n", "\n", "def fig_data_plot_phase(ev_info,st_info,phase_list,color_list,dis_min,dis_max):\n", "\n", " [dis_obs_phase,time_obs_phase] = data_dis_time_phase(ev_info,st_info,phase_list)\n", "\n", " regression = {}\n", " # Calculate the least squares y = ax+b\n", " for key_phase in phase_list:\n", " X = dis_obs_phase[key_phase]\n", " Y = time_obs_phase[key_phase]\n", "\n", " if(len(X)>20):\n", " regression[key_phase] = linear_regression(X,Y)\n", " else:\n", " print(\"No enough data: %d, for %s\"%(len(X),key_phase))\n", " regression[key_phase] = [0,0,0]\n", "\n", "\n", " # draw\n", " fig = plt.figure(figsize=(10,10))\n", " gridspace = GridSpec(6,6,figure = fig)\n", " ax2 = fig.add_subplot(gridspace[0:6, 0:6])\n", " y1 = 99999; y2 = -99999\n", "\n", " # scatter plot\n", " for iphase in range(len(phase_list)):\n", " phase = phase_list[iphase]\n", " color = color_list[iphase]\n", " ax2.plot(dis_obs_phase[phase],time_obs_phase[phase],'%s.'%(color),markersize=1)\n", "\n", " # linear regression plot\n", " for iphase in range(len(phase_list)):\n", " phase = phase_list[iphase]\n", " color = color_list[iphase]\n", " (slope,intercept,SEE)= regression[phase]\n", " ax2.plot([dis_min,dis_max],[dis_min*slope+intercept,dis_max*slope+intercept],'%s-'%(color),linewidth=2,label = \"%s: a,b,SEE=%5.2f,%5.2f,%5.2f\"%(phase,slope,intercept,SEE))\n", " y1 = min(y1,intercept-5)\n", " y2 = max(y2,dis_max*slope+intercept+5)\n", "\n", " ax2.legend(fontsize = 14)\n", " ax2.tick_params(axis='x',labelsize=18)\n", " ax2.tick_params(axis='y',labelsize=18)\n", " ax2.set_xlabel('Distance (km)',fontsize=18)\n", " ax2.set_ylabel('Traveltime (s)',fontsize=18)\n", " ax2.set_xlim((dis_min,dis_max))\n", "\n", "\n", " for iphase in range(len(phase_list)):\n", " try:\n", " y1 = min(y1,min(time_obs_phase[phase]))\n", " y2 = max(y2,max(time_obs_phase[phase]))\n", " except:\n", " pass\n", " ax2.set_ylim((y1,y2))\n", "\n", " title = \"\"\n", " for phase in dis_obs_phase:\n", " title = title + \"%s(%d) \"%(phase,len(dis_obs_phase[phase]))\n", "\n", " ax2.set_title(title)\n", "\n", " print(\"a is slope, b is intercept, SEE is standard error of estimate\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions: results analysis and evaluation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# plot: plot the residual histogram of the initial and final model\n", "def fig_residual_histogram(fn_syn_init,fn_syn_final,fn_obs,range_l,range_r,Nbar,tag1 = \"initial\",tag2 = \"final\"):\n", "\n", " # read synthetic traveltime data in the initial model\n", " [ev_info_syn_init, st_info_syn_init] = read_src_rec_file(fn_syn_init)\n", " time_syn_init = data_dis_time(ev_info_syn_init,st_info_syn_init)[1]\n", "\n", " # read synthetic traveltime data in the final model\n", " [ev_info_syn_final, st_info_syn_final] = read_src_rec_file(fn_syn_final)\n", " time_syn_final = data_dis_time(ev_info_syn_final,st_info_syn_final)[1]\n", "\n", " # read observed traveltime data\n", " [ev_info_obs, st_info_obs] = read_src_rec_file(fn_obs)\n", " time_obs = data_dis_time(ev_info_obs,st_info_obs)[1]\n", "\n", " fig = plt.figure(figsize=(6,6))\n", " gridspace = GridSpec(6,6,figure = fig)\n", "\n", " ax2 = fig.add_subplot(gridspace[0:6, 0:6])\n", "\n", " bins=np.linspace(range_l,range_r,Nbar)\n", " error_init = time_syn_init - time_obs\n", " error_final = time_syn_final - time_obs\n", "\n", " hist_init, _, _ = ax2.hist(error_init,bins=bins,histtype='step', edgecolor = \"red\", linewidth = 2,\n", " label = \"%s: std = %5.3f s, mean = %5.3f s\"%(tag1,np.std(error_init),np.mean(error_init)))\n", "\n", " hist_final, _, _ = ax2.hist(error_final,bins=bins,alpha = 0.5, color = \"blue\",\n", " label = \"%s: std = %5.3f s, mean = %5.3f s\"%(tag2,np.std(error_final),np.mean(error_final)))\n", "\n", " print(\"residual for \",tag1,\" model is: \",\"mean: \",np.mean(error_init),\"sd: \",np.std(error_init))\n", " print(\"residual for \",tag2,\" model is: \",\"mean: \",np.mean(error_final),\"sd: \",np.std(error_final))\n", " ax2.legend(fontsize=14)\n", "\n", " ax2.set_xlim(range_l - abs(range_l)*0.1,range_r + abs(range_r)*0.1)\n", " ax2.set_ylim(0,1.3*max(max(hist_init),max(hist_final)))\n", "\n", " ax2.tick_params(axis='x',labelsize=18)\n", " ax2.tick_params(axis='y',labelsize=18)\n", " ax2.set_ylabel('Number of data',fontsize=18)\n", " ax2.set_xlabel('Traveltime residuals (s)',fontsize=18)\n", " ax2.set_title(\"$t_{syn} - t_{obs}$\",fontsize=18)\n", " ax2.grid()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# plot: plot the residual histogram of the initial and final model\n", "def fig_data_difference_histogram(fn_A,fn_B,range_l,range_r,Nbar):\n", "\n", " # read data A\n", " [ev_info_A, st_info_A] = read_src_rec_file(fn_A)\n", "\n", " # read data B\n", " [ev_info_B, st_info_B] = read_src_rec_file(fn_B)\n", "\n", " # absolute traveltime residual\n", " error_t = []\n", " for key_ev in ev_info_A:\n", " for key_t in ev_info_A[key_ev].t:\n", " data_A = ev_info_A[key_ev].t[key_t]\n", " if (key_ev in ev_info_B and key_t in ev_info_B[key_ev].t):\n", " data_B = ev_info_B[key_ev].t[key_t]\n", " error_t.append(data_A[2] - data_B[2])\n", "\n", " # common-source differential traveltime residual\n", " error_cs_dt = []\n", " for key_ev in ev_info_A:\n", " for key_dt in ev_info_A[key_ev].cs_dt:\n", " data_A = ev_info_A[key_ev].cs_dt[key_dt]\n", " if (key_ev in ev_info_B and key_dt in ev_info_B[key_ev].cs_dt):\n", " data_B = ev_info_B[key_ev].cs_dt[key_dt]\n", " error_cs_dt.append(data_A[3] - data_B[3])\n", " else:\n", " print(key_ev,key_dt)\n", "\n", " # common-receiver differential traveltime residual\n", " error_cr_dt = []\n", " for key_ev in ev_info_A:\n", " for key_dt in ev_info_A[key_ev].cr_dt:\n", " data_A = ev_info_A[key_ev].cr_dt[key_dt]\n", " if (key_ev in ev_info_B and key_dt in ev_info_B[key_ev].cr_dt):\n", " data_B = ev_info_B[key_ev].cr_dt[key_dt]\n", " error_cr_dt.append(data_A[3] - data_B[3])\n", "\n", " # plot\n", " fig = plt.figure(figsize=(14,6))\n", " gridspace = GridSpec(6,14,figure = fig)\n", "\n", "\n", " ax2 = fig.add_subplot(gridspace[0:6, 0:6])\n", " bins=np.linspace(range_l,range_r,Nbar)\n", " # hist_t, _, _ = ax2.hist(error_t,bins=bins,histtype='step', edgecolor = \"red\", linewidth = 2,\n", " # label = \"noise: std = %5.3f s, mean = %5.3f s\"%(np.std(error_t),np.mean(error_t)))\n", " hist_t, _, _ = ax2.hist(error_t,bins=bins,alpha = 0.5, color = \"blue\",\n", " label = \"noise: std = %5.3f s, mean = %5.3f s\"%(np.std(error_t),np.mean(error_t)))\n", "\n", " ax2.legend(fontsize=14)\n", " ax2.set_xlim(range_l - abs(range_l)*0.1,range_r + abs(range_r)*0.1)\n", " try:\n", " ax2.set_ylim(0,1.3*max(hist_t))\n", " except:\n", " ax2.set_ylim(0,1.0)\n", " ax2.tick_params(axis='x',labelsize=18)\n", " ax2.tick_params(axis='y',labelsize=18)\n", " ax2.set_ylabel('Number of data',fontsize=18)\n", " ax2.set_xlabel('Noise (s)',fontsize=18)\n", " ax2.set_title(\"Noise of traveltime\",fontsize=18)\n", "\n", "\n", " ax3 = fig.add_subplot(gridspace[0:6,8:14])\n", " bins=np.linspace(range_l,range_r,Nbar)\n", " # hist_t, _, _ = ax3.hist(error_t,bins=bins,histtype='step', edgecolor = \"red\", linewidth = 2,\n", " # label = \"noise: std = %5.3f s, mean = %5.3f s\"%(np.std(error_t),np.mean(error_t)))\n", " hist_cs_dt, _, _ = ax3.hist(error_cs_dt,bins=bins,alpha = 0.5, color = \"blue\",\n", " label = \"noise: std = %5.3f s, mean = %5.3f s\"%(np.std(error_cs_dt),np.mean(error_cs_dt)))\n", "\n", " ax3.legend(fontsize=14)\n", " ax3.set_xlim(range_l - abs(range_l)*0.1,range_r + abs(range_r)*0.1)\n", " try:\n", " ax3.set_ylim(0,1.3*max(hist_cs_dt))\n", " except:\n", " ax3.set_ylim(0,1.0)\n", " ax3.tick_params(axis='x',labelsize=18)\n", " ax3.tick_params(axis='y',labelsize=18)\n", " ax3.set_ylabel('Number of data',fontsize=18)\n", " ax3.set_xlabel('Noise (s)',fontsize=18)\n", " ax3.set_title(\"Noise of differential traveltime\",fontsize=18)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" } }, "nbformat": 4, "nbformat_minor": 2 }