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coda_process_otherdata.py
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coda_process_otherdata.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import glob
import obspy
import numpy as np
from obspy.taup import TauPyModel
import tspca
"""
calculate the autocorrelation of each station and plot cross-correlation figures
"""
def coda_autocorrelation(datafolder, channel='Z', cut_win=[-20, 80], signal_win=[-5, 5], noise_win=[-20, -5], min_SNR=3, ac_view_cut=20, taper_length=5,
freq_pre=[0.1, 2], dw=0.5, data_PWS_order=1, vel_PWS_order=0, freq_ac=[0.2, 2], vp_max=10, vp_scan_num=401, filter_data=True,
taper_data=False, max_scan_time_win=[10, 15], plot_sta_event=True, out_length = 40, gauss = 5, figure_folder='figure'):
# MAIN PROCESSING FOR VERTICAL AUTOCORRELOGRAMS
valid_station = sorted(list(set([obspy.read(sacdata)[0].stats.station for sacdata in glob.glob('%s/*/*.SAC'%(datafolder))])))
print(valid_station)
# information for each station
auto_all_stations = []
auto_corr_all_stations = []
stack_all_stations = obspy.Stream()
stack_corr_all_stations = obspy.Stream()
vel_spec_all_stations = []
station_elevation = []
max_Va_list = []
max_t0_list = []
model = TauPyModel(model="iasp91")
for station in valid_station:
print(station)
# data_file = os.path.join(datafolder, station)
# autocorrelation stream
auto_stream = obspy.Stream()
# autocorrelation stream for velocity analysis
auto_stream_vel = obspy.Stream()
# ray parameter
ray_parameter = []
# selected data
selected_data = []
# station and event coordinates
sta_event_coord = {'evla': [], 'evlo': []}
# select channel data
# sta_data_files = glob.glob('%s/*.sac'%(data_file))
sta_data_files = glob.glob('%s/*/*%s*.SAC'%(datafolder,station))
sta_data_files = [data for data in sta_data_files if obspy.read(data)[0].stats.channel[-1] == channel]
for fname in sorted(sta_data_files):
print(fname)
tr = obspy.read(fname, format='SAC')[0]
print('delta = %f'%tr.stats.delta)
#print('npts = %d'%tr.stats.npts)
# delete data with no data or nan or inf
if np.any(tr.data) == 0 or np.isnan(tr).any() or np.isinf(tr).any():
print('%s has no data and delet!'%fname)
os.system('rm %s'%fname)
if hasattr(tr.stats.sac,'evdp'):
print(tr.stats.sac.evdp)
# delete data with low SNR
tr_cut, SNR, SNR2 = tspca.data_select(tr, cut_win, signal_win, noise_win, freq_pre[0], freq_pre[1], 4)
print(SNR, SNR2)
if SNR > min_SNR and SNR2 > 2:
# station and event coordinates
sta_event_coord['stla'] = tr.stats.sac.stla
sta_event_coord['stlo'] = tr.stats.sac.stlo
sta_event_coord['stel'] = tr.stats.sac.stel
sta_event_coord['evla'].append(tr.stats.sac.evla)
sta_event_coord['evlo'].append(tr.stats.sac.evlo)
# calculate ray parameter
arrival_info = model.get_travel_times(source_depth_in_km=tr.stats.sac.evdp, distance_in_degree=tr.stats.sac.gcarc, phase_list=['P'])[0]
ray_para = arrival_info.ray_param / 6371
ray_parameter.append(ray_para)
# compute velocity analysis
#auto_vel = tspca.compute_auto(tr_cut.copy(), dw, freq_ac[0], freq_ac[1], taper_length, taper_data, filter_data, vel_analysis=True)
#auto_stream_vel.append(auto_vel)
# compute auto-correlation
auto = tspca.comp_autocc(tr_cut.copy(), dw, gauss, freq_ac[0], freq_ac[1], out_length)
print(auto)
auto_stream.append(auto)
selected_data.append(tr_cut.copy().data)
else: print('evdp not exist')
np.save('selected_data_%s.npy'%station,selected_data)
# station and events plot
print('1. auto-selected events (SNR>= %.1f): %d/%d'%(min_SNR, len(auto_stream), len(sta_data_files)))
print('2. calculate autocorrelation for %s station'%station)
if plot_sta_event:
tspca.station_center_map(station, min_SNR, sta_event_coord['stla'], sta_event_coord['stlo'], sta_event_coord['evla'], sta_event_coord['evlo'])
# velocity analysis
print('3. velocity analysis for %s station'%station)
vel_spec = tspca.velocity_analysis(auto_stream, ray_parameter, vp_num=vp_scan_num, vp_max=vp_max, PWS_order=vel_PWS_order)
ac_time_len = out_length
# get max Va and t0
max_Va, max_t0 = tspca.get_max_value(vel_spec.T, max_scan_time_win, vp_max, ac_time_len)
max_Va_list.append(max_Va)
max_t0_list.append(max_t0)
# moveout correction
print('4. moveout correction for %s station, based on Va=%.2f km/s'%(station, max_Va))
auto_corr = tspca.moveout_corr(auto_stream, ray_parameter, max_Va)
stack_corr = tspca.data_stack(auto_corr, data_PWS_order)
auto_corr_all_stations.append(auto_corr)
stack_corr_all_stations.append(stack_corr)
vel_spec_all_stations.append(vel_spec)
auto_all_stations.append(auto_stream)
stack = tspca.data_stack(auto_stream, data_PWS_order)
stack_all_stations.append(stack)
# station elevation
station_elevation.append(auto.stats.sac.stel/1000)
# plot autocorrelation and velocity spectrum
print('5. plot autocorrelation, velocity spectrum and moveout corrected autocorrelation...')
tspca.plot_vel_spec(auto_stream, vel_spec, max_Va, max_t0, vp_max, ac_view_cut, figure_folder)
tspca.plot_ac(auto_stream, stack, ac_view_cut, taper_length, taper_data, figure_folder)
tspca.plot_ac(auto_corr, stack_corr, ac_view_cut, taper_length, taper_data, figure_folder, moveout_corr=True)
# binned stack
binned_stack(auto_stream,8,station)
# save autocorrelation and velocity spectrum
print('6. save autocorrelation and velocity spectrum...')
data_total_info = {'stations': valid_station, 'auto_all_stations': auto_all_stations, 'auto_corr_all_stations': auto_corr_all_stations, 'stack_all_stations': stack_all_stations,
'stack_corr_all_stations': stack_corr_all_stations, 'vel_spec_all_stations': vel_spec_all_stations, 'station_elevation': station_elevation,
'max_Va_list': max_Va_list, 'max_t0_list': max_t0_list, }
np.save('data_total_info.npy', data_total_info)
def binned_stack(tr, nbins,station):
"""
"""
# ev_depth=tr.stats.sac.evdp
npts=tr[0].stats.npts
model = TauPyModel(model="iasp91")
rayp=np.zeros([len(tr),1])
obs=np.zeros([npts,len(tr)])
baz=np.zeros([len(tr),1])
k=0
for auto in tr:
ev_depth=auto.stats.sac.evdp
epi_dist=auto.stats.sac.gcarc
arrivals = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=epi_dist,
phase_list=["P"])
arrs=arrivals[0]
rad2km=6371
rayp[k]=arrs.ray_param/rad2km
obs[:,k]=auto.data
baz[k]=auto.stats.sac.baz
k+=1
bins = np.linspace(0.04,0.08,nbins+1)
print(bins)
inds = np.digitize(rayp,bins)
print(inds)
zac_bins=np.zeros([npts,nbins])
zac_std=np.zeros([npts,nbins])
rayp_bins=np.zeros(nbins)
events_num=np.zeros(nbins)
for j in range(nbins):
inx=[i for i,x in enumerate(inds) if x==j+1]
print(len(inx))
if len(inx)>0:
rayp_bins[j]=np.mean(rayp[inx])
events_num[j]=len(inx)
ac_bins=obs[:,inx]
baz_bins=baz[inx]
slow_bins=rayp[inx]
np.save('baz_bins_%s_%s'%(str(j),station),arr=baz_bins)
np.save('zac_bins_%s_%s'%(str(j),station),arr=ac_bins)
np.save('slow_bins_%s_%s'%(str(j),station),arr=slow_bins)
zac_bins[:,j]=np.mean(ac_bins,axis=1)
zac_std[:,j]=np.std(ac_bins,axis=1)
np.save('zac_bins_%s.npy'%station,arr=zac_bins)
np.save('zac_std_%s.npy'%station,arr=zac_std)
np.save('rayp_bins_%s.npy'%station,arr=rayp_bins)
np.save('events_num_%s.npy'%station,arr=events_num)
if __name__ == '__main__':
### parameters setting
data_wave_folder = "/mnt/d/work/DownloadSeisData-main/Bash/DATA/SAC_event/II" # folder of waveforms
# data preprocessing setting
channel = 'Z' # channel of waveforms
cut_win = [-20, 80] # cut window for autocorrelation, before 20s and after 80s of P wave. unit: second
signal_win = [-5, 5] # signal window for calculating SNR, 5s before and after P wave. unit: second
noise_win = [-20, -10] # noise window for calculating SNR, 20s before and 5s before P wave. unit: second
freq_pre = [0.1, 2] # frequency band for prefiltering data, 0.1-2Hz
min_SNR = 10 # minimum SNR for autocorrelation
# spectral whitening and autocorrelation setting
dw = 0.1 # spectral whitening width
filter_data = True # filter autocorrelation data or not
freq_ac = [0.2, 1] # frequency band for autocorrelation data, 0.1-1Hz
taper_data = True # taper autocorrelation data or not
taper_length = 5 # taper length for autocorrelation data, 5s
data_PWS_order = 1 # order of phase-weighted stack for autocorrelation data
# out_length = 40 # length of output autocorrelation
# velocity analysis setting
vel_PWS_order = 1 # order of phase-weighted stack for velocity analysis data, 0 for linear stack
vp_max = 10 # maximum velocity for velocity analysis, default: 10km/s
vp_scan_num = 401 # number of velocity for velocity analysis
max_scan_time_win = [10, 20] # time window for scanning maximum velocity and t0, 10-15s of autocorrelation data
# plot figure setting
figure_folder = 'figure' # data folder for saving figures
ac_view_cut = 20 # time window for plotting autocorrelation and velocity spectrum, 20s of autocorrelation data
plot_sta_event = False #True # plot station and event distribution or not
out_length = 40
gauss = 3
### output:
# 1. autocorrelation and velocity spectrum figures for each station (under figure folder)
# 2. autocorrelation and velocity spectrum data for each station (data_total_info.npy)
# calculate autocorrelation and velocity analysis
coda_autocorrelation(data_wave_folder, channel, cut_win, signal_win, noise_win, min_SNR, ac_view_cut, taper_length,
freq_pre, dw, data_PWS_order, vel_PWS_order, freq_ac, vp_max, vp_scan_num, filter_data,
taper_data, max_scan_time_win, plot_sta_event, out_length, gauss, figure_folder)