def test_xcorrPickCorrection(self):
"""
Test cross correlation pick correction on a set of two small local
earthquakes.
"""
st1 = read(os.path.join(self.path,
'BW.UH1._.EHZ.D.2010.147.a.slist.gz'))
st2 = read(os.path.join(self.path,
'BW.UH1._.EHZ.D.2010.147.b.slist.gz'))
tr1 = st1.select(component="Z")[0]
tr2 = st2.select(component="Z")[0]
t1 = UTCDateTime("2010-05-27T16:24:33.315000Z")
t2 = UTCDateTime("2010-05-27T16:27:30.585000Z")
dt, coeff = xcorrPickCorrection(t1, tr1, t2, tr2, 0.05, 0.2, 0.1)
self.assertAlmostEqual(dt, -0.014459080288833711)
self.assertAlmostEqual(coeff, 0.91542878457939791)
dt, coeff = xcorrPickCorrection(t2, tr2, t1, tr1, 0.05, 0.2, 0.1)
self.assertAlmostEqual(dt, 0.014459080288833711)
self.assertAlmostEqual(coeff, 0.91542878457939791)
dt, coeff = xcorrPickCorrection(
t1, tr1, t2, tr2, 0.05, 0.2, 0.1, filter="bandpass",
filter_options={'freqmin': 1, 'freqmax': 10})
self.assertAlmostEqual(dt, -0.013025086360067755)
self.assertAlmostEqual(coeff, 0.98279277273758803)
def test_xcorrPickCorrection(self):
"""
Test cross correlation pick correction on a set of two small local
earthquakes.
"""
st1 = read(
os.path.join(self.path, 'BW.UH1._.EHZ.D.2010.147.a.slist.gz'))
st2 = read(
os.path.join(self.path, 'BW.UH1._.EHZ.D.2010.147.b.slist.gz'))
tr1 = st1.select(component="Z")[0]
tr2 = st2.select(component="Z")[0]
t1 = UTCDateTime("2010-05-27T16:24:33.315000Z")
t2 = UTCDateTime("2010-05-27T16:27:30.585000Z")
dt, coeff = xcorrPickCorrection(t1, tr1, t2, tr2, 0.05, 0.2, 0.1)
self.assertAlmostEqual(dt, -0.014459080288833711)
self.assertAlmostEqual(coeff, 0.91542878457939791)
dt, coeff = xcorrPickCorrection(t2, tr2, t1, tr1, 0.05, 0.2, 0.1)
self.assertAlmostEqual(dt, 0.014459080288833711)
self.assertAlmostEqual(coeff, 0.91542878457939791)
dt, coeff = xcorrPickCorrection(t1,
tr1,
t2,
tr2,
0.05,
0.2,
0.1,
filter="bandpass",
filter_options={
'freqmin': 1,
'freqmax': 10
})
self.assertAlmostEqual(dt, -0.013025086360067755)
self.assertAlmostEqual(coeff, 0.98279277273758803)
def align_trace(std_trace, traces):
t1 = 3
t_std = std_trace.stats.starttime + t1
# std_trace.plot()
print "t_std", t_std
for trace in traces:
# trace.plot()
starttime = trace.stats.starttime + t1
print 'starttime', starttime
dt, coeff = xcorrPickCorrection(
starttime, trace, t_std, std_trace, 0.0, 15.0, t1, plot=True)
def subsample_xcorr_shift(d, s):
"""
Calculate the correlation time shift around the maximum amplitude of the
synthetic trace with subsample accuracy.
"""
# Estimate shift and use it as a guideline for the subsample accuracy
# shift.
time_shift = _xcorr_shift(d.data, s.data) * d.stats.delta
# Align on the maximum amplitude of the synthetics.
pick_time = s.stats.starttime + s.data.argmax() * s.stats.delta
# Will raise a warning if the trace ids don't match which we don't care
# about here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return xcorrPickCorrection(
pick_time, s, pick_time, d, 20.0 * time_shift,
20.0 * time_shift, 10.0 * time_shift)[0]
def subsample_xcorr_shift(d, s):
"""
Calculate the correlation time shift around the maximum amplitude of the
synthetic trace with subsample accuracy.
"""
# Estimate shift and use it as a guideline for the subsample accuracy
# shift.
time_shift = _xcorr_shift(d.data, s.data) * d.stats.delta
# Align on the maximum amplitude of the synthetics.
pick_time = s.stats.starttime + s.data.argmax() * s.stats.delta
# Will raise a warning if the trace ids don't match which we don't care
# about here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return xcorrPickCorrection(pick_time, s, pick_time, d,
20.0 * time_shift, 20.0 * time_shift,
10.0 * time_shift)[0]
# read example data of two small earthquakes
st1 = read("http://examples.obspy.org/BW.UH1..EHZ.D.2010.147.a.slist.gz")
st2 = read("http://examples.obspy.org/BW.UH1..EHZ.D.2010.147.b.slist.gz")
# select the single traces to use in correlation.
# to avoid artifacts from preprocessing there should be some data left and
# right of the short time window actually used in the correlation.
tr1 = st1.select(component="Z")[0]
tr2 = st2.select(component="Z")[0]
# these are the original pick times set during routine analysis
t1 = UTCDateTime("2010-05-27T16:24:33.315000Z")
t2 = UTCDateTime("2010-05-27T16:27:30.585000Z")
# estimate the time correction for pick 2 without any preprocessing and open
# a plot window to visually validate the results
dt, coeff = xcorrPickCorrection(t1, tr1, t2, tr2, 0.05, 0.2, 0.1, plot=True)
print "No preprocessing:"
print " Time correction for pick 2: %.6f" % dt
print " Correlation coefficient: %.2f" % coeff
# estimate the time correction with bandpass prefiltering
dt, coeff = xcorrPickCorrection(t1,
tr1,
t2,
tr2,
0.05,
0.2,
0.1,
plot=True,
filter="bandpass",
filter_options={
'freqmin': 1,
def write_correlations(event_list, wavbase, extract_len, pre_pick, shift_len,\
lowcut=1.0, highcut=10.0, max_sep=4, min_link=8, \
coh_thresh=0.0):
"""
Function to write a dt.cc file for hypoDD input - takes an input list of
events and computes pick refienements by correlation.
Note that this is **NOT** fast.
:type event_list: List of tuple
:param event_list: List of tuples of event_id (int) and sfile (String)
:type wavbase: string
:param wavbase: Path to the seisan wave directory that the wavefiles in the
S-files are stored
:type extract_len: float
:param extract_len: Length in seconds to extract around the pick
:type pre_pick: float
:param pre_pick: Time before the pick to start the correclation window
:type shift_len: float
:param shift_len: Time to allow pick to vary
:type lowcut: float
:param lowcut: Lowcut in Hz - default=1.0
:type highcut: float
:param highcut: Highcut in Hz - deafult=10.0
:type max_sep: float
:param max_sep: Maximum seperation between event pairs in km
:type min_link: int
:param min_link: Minimum links for an event to be paired
"""
from obspy.signal.cross_correlation import xcorrPickCorrection
import matplotlib.pyplot as plt
from obspy import read
from eqcorrscan.utils.mag_conv import dist_calc
import glob
corr_list=[]
f=open('dt.cc','w')
for i in xrange(len(event_list)):
master_sfile=event_list[i][1]
master_event_id=event_list[i][0]
master_picks=Sfile_util.readpicks(master_sfile)
master_ori_time=Sfile_util.readheader(master_sfile).time
master_location=(Sfile_util.readheader(master_sfile).latitude,\
Sfile_util.readheader(master_sfile).longitude,\
Sfile_util.readheader(master_sfile).depth)
master_wavefiles=Sfile_util.readwavename(master_sfile)
masterpath=glob.glob(wavbase+os.sep+'????'+os.sep+'??'+os.sep+master_wavefiles[0])
if masterpath:
masterstream=read(masterpath[0])
if len(master_wavefiles)>1:
for wavefile in master_wavefiles:
wavepath=glob.glob(wavbase+os.sep+'*'+os.sep+'*'+os.sep+wavefile)
if wavepath:
masterstream+=read(wavepath[0])
else:
raise IOError("Couldn't find wavefile")
for j in xrange(i+1,len(event_list)):
# Use this tactic to only output unique event pairings
slave_sfile=event_list[j][1]
slave_event_id=event_list[j][0]
slave_wavefiles=Sfile_util.readwavename(slave_sfile)
try:
slavestream=read(wavbase+'/*/*/'+slave_wavefiles[0])
except:
raise IOError('No wavefile found: '+slave_wavefiles[0]+' '+slave_sfile)
if len(slave_wavefiles)>1:
for wavefile in slave_wavefiles:
slavestream+=read(wavbase+'/*/*/'+wavefile)
# Write out the header line
event_text='#'+str(master_event_id).rjust(10)+\
str(slave_event_id).rjust(10)+' 0.0 \n'
slave_picks=Sfile_util.readpicks(slave_sfile)
slave_ori_time=Sfile_util.readheader(slave_sfile).time
slave_location=(Sfile_util.readheader(slave_sfile).latitude,\
Sfile_util.readheader(slave_sfile).longitude,\
Sfile_util.readheader(slave_sfile).depth)
if dist_calc(master_location, slave_location) > max_sep:
break
links=0
phases=0
for pick in master_picks:
if pick.phase not in ['P','S']:
continue # Only use P and S picks, not amplitude or 'other'
# Find station, phase pairs
slave_matches=[p for p in slave_picks if p.station==pick.station\
and p.phase==pick.phase]
if masterstream.select(station=pick.station, \
channel='*'+pick.channel[-1]):
mastertr=masterstream.select(station=pick.station, \
channel='*'+pick.channel[-1])[0]
else:
print 'No waveform data for '+pick.station+'.'+pick.channel
print pick.station+'.'+pick.channel+' '+slave_sfile+' '+master_sfile
break
# Loop through the matches
for slave_pick in slave_matches:
if slavestream.select(station=slave_pick.station,\
channel='*'+slave_pick.channel[-1]):
slavetr=slavestream.select(station=slave_pick.station,\
channel='*'+slave_pick.channel[-1])[0]
else:
print 'No slave data for '+slave_pick.station+'.'+\
slave_pick.channel
print pick.station+'.'+pick.channel+' '+slave_sfile+' '+master_sfile
break
# Correct the picks
try:
correction, cc = xcorrPickCorrection(pick.time, mastertr,\
slave_pick.time,\
slavetr,pre_pick,\
extract_len-pre_pick, shift_len,\
filter="bandpass",\
filter_options={'freqmin':lowcut,
'freqmax':highcut},plot=False)
# Get the differntial travel time using the corrected time.
dt=(pick.time-master_ori_time)-\
(slave_pick.time+correction-slave_ori_time)
links+=1
if cc*cc >= coh_thresh:
phases+=1
#added by Caro
event_text+=pick.station.ljust(4)+\
_cc_round(correction,3).rjust(11)+\
_cc_round(cc,3).rjust(8)+\
' '+pick.phase+'\n'
# links+=1
corr_list.append(cc*cc)
except:
continue
if links >= min_link and phases > 0:
f.write(event_text)
plt.hist(corr_list, 150)
plt.show()
# f.write('\n')
f.close()
f2.close()
return
from __future__ import print_function
from obspy.core import read, UTCDateTime
from obspy.signal.cross_correlation import xcorrPickCorrection
# read example data of two small earthquakes
st1 = read("http://examples.obspy.org/BW.UH1..EHZ.D.2010.147.a.slist.gz")
st2 = read("http://examples.obspy.org/BW.UH1..EHZ.D.2010.147.b.slist.gz")
# select the single traces to use in correlation.
# to avoid artifacts from preprocessing there should be some data left and
# right of the short time window actually used in the correlation.
tr1 = st1.select(component="Z")[0]
tr2 = st2.select(component="Z")[0]
# these are the original pick times set during routine analysis
t1 = UTCDateTime("2010-05-27T16:24:33.315000Z")
t2 = UTCDateTime("2010-05-27T16:27:30.585000Z")
# estimate the time correction for pick 2 without any preprocessing and open
# a plot window to visually validate the results
dt, coeff = xcorrPickCorrection(t1, tr1, t2, tr2, 0.05, 0.2, 0.1, plot=True)
print("No preprocessing:")
print(" Time correction for pick 2: %.6f" % dt)
print(" Correlation coefficient: %.2f" % coeff)
# estimate the time correction with bandpass prefiltering
dt, coeff = xcorrPickCorrection(t1, tr1, t2, tr2, 0.05, 0.2, 0.1, plot=True,
filter="bandpass",
filter_options={'freqmin': 1, 'freqmax': 10})
print("Bandpass prefiltering:")
print(" Time correction for pick 2: %.6f" % dt)
print(" Correlation coefficient: %.2f" % coeff)
