def corread(saclist, ipick, timewindow, taperwindow, tapertype):
""" Read data within timewindow+taperwindow (same length for each trace) for cross-correlation.
"""
tw = timewindow[1] - timewindow[0]
taperwidth = taperwindow / (taperwindow + tw)
reftimes = array([sacdh.gethdr(ipick) for sacdh in saclist])
if -12345.0 in reftimes:
print('Not all seismograms has ipick={0:s} set. Exit.'.format(ipick))
sys.exit()
nstart, ntotal = windowIndex(saclist, reftimes, timewindow, taperwindow)
windata = windowData(saclist, nstart, ntotal, taperwidth, tapertype)
return windata, reftimes
def corread(saclist, ipick, timewindow, taperwindow, tapertype):
""" Read data within timewindow+taperwindow (same length for each trace) for cross-correlation.
"""
tw = timewindow[1] - timewindow[0]
taperwidth = taperwindow/(taperwindow+tw)
reftimes = array([ sacdh.gethdr(ipick) for sacdh in saclist])
if -12345.0 in reftimes:
print ('Not all seismograms has ipick={0:s} set. Exit.'.format(ipick))
sys.exit()
nstart, ntotal = windowIndex(saclist, reftimes, timewindow, taperwindow)
windata = windowData(saclist, nstart, ntotal, taperwidth, tapertype)
return windata, reftimes
def plotStack(self):
"""
Calculate mean stack from all traces and plot it.
No taper window is added in.
"""
saclist = self.saclist
mm = self.bmax, self.emin
twplot = axLimit(mm, -0.01)
twp = twplot[1] - twplot[0]
taperwindow = 0
reftimes = [sacdh.reftime for sacdh in saclist]
nstart, ntotal = windowIndex(saclist, reftimes, twplot, taperwindow)
datacut = windowData(saclist, nstart, ntotal, taperwindow / twp)
datamean = mean(datacut, 0)
# copy a sacdh object for stack
stackdh = copy.copy(saclist[0])
stackdh.b = twplot[0] - taperwindow * 0.5
stackdh.npts = len(datamean)
stackdh.data = datamean
stackdh.thdrs = [
0.,
] * 10
stackdh.filename = 'meanstack.sac'
self.sstack = SingleSeis(stackdh, self.opts, self.axss, self.stackbase,
self.stackcolor, self.stacklinew)
# plot 1-std range from mean stack
if self.opts.zero_on and self.opts.std_on:
datastd = std(datacut, 0)
stda = copy.copy(stackdh)
stdb = copy.copy(stackdh)
stda.data = datamean + datastd
stdb.data = datamean - datastd
stda.thdrs = [
0,
] * 10
stdb.thdrs = [
0,
] * 10
stda.filename = 'stackstdplus.sac'
stda.filename = 'stackstdnega.sac'
self.sstda = SingleSeis(stda, self.opts, self.axss, self.stackbase,
self.stackcolor, self.stacklinew / 2.)
self.sstdb = SingleSeis(stdb, self.opts, self.axss, self.stackbase,
self.stackcolor, self.stacklinew / 2.)
self.stdfill = self.axss.fill_between(self.sstack.time,
stda.data,
stdb.data,
color=self.stackcolor,
alpha=.25)
def plotStack(self): """ Calculate mean stack from all traces and plot it. No taper window is added in. """ saclist = self.saclist mm = self.bmax, self.emin twplot = axLimit(mm, -0.01) twp = twplot[1] - twplot[0] taperwindow = 0 reftimes = [ sacdh.reftime for sacdh in saclist ] nstart, ntotal = windowIndex(saclist, reftimes, twplot, taperwindow) datacut = windowData(saclist, nstart, ntotal, taperwindow/twp) datamean = mean(datacut, 0) # copy a sacdh object for stack stackdh = copy.copy(saclist[0]) stackdh.b = twplot[0] - taperwindow*0.5 stackdh.npts = len(datamean) stackdh.data = datamean stackdh.thdrs = [0.,]*10 stackdh.filename = 'meanstack.sac' self.sstack = SingleSeis(stackdh, self.opts, self.axss, self.stackbase, self.stackcolor, self.stacklinew) # plot 1-std range from mean stack if self.opts.zero_on and self.opts.std_on: datastd = std(datacut, 0) stda = copy.copy(stackdh) stdb = copy.copy(stackdh) stda.data = datamean + datastd stdb.data = datamean - datastd stda.thdrs = [0,]*10 stdb.thdrs = [0,]*10 stda.filename = 'stackstdplus.sac' stda.filename = 'stackstdnega.sac' self.sstda = SingleSeis(stda, self.opts, self.axss, self.stackbase, self.stackcolor, self.stacklinew/2.) self.sstdb = SingleSeis(stdb, self.opts, self.axss, self.stackbase, self.stackcolor, self.stacklinew/2.) self.stdfill = self.axss.fill_between(self.sstack.time, stda.data, stdb.data, color=self.stackcolor, alpha=.25)
def ccWeightStack(saclist, opts):
"""
Align seismograms by the iterative cross-correlation and stack algorithm.
Parameters
----------
opts.delta : sample time interval
opts.ccpara : a class instance for ICCS parameters
* qqhdrs : SAC headers to save quality factors: ccc, snr, coh
* maxiter : maximum number of iteration
* converg : convergence critrion
* cchdrs : inputand output picks of cross-correlation
* twcorr : time window for cross-correlation
"""
ccpara = opts.ccpara
delta = opts.delta
maxiter = ccpara.maxiter
convtype = ccpara.convtype
convepsi = ccpara.convepsi
taperwidth = ccpara.taperwidth
tapertype = ccpara.tapertype
xcorr = ccpara.xcorr
shift = ccpara.shift
twhdrs = ccpara.twhdrs
qqhdrs = ccpara.qheaders
cchdrs = ccpara.cchdrs
twcorr = ccpara.twcorr
hdrccc, hdrsnr, hdrcoh, = qqhdrs[:3]
cchdr0, cchdr1 = cchdrs
if convtype == 'coef':
convergence = coConverg
elif convtype == 'resi':
convergence = reConverg
else:
print('Unknown convergence criterion: {:s}. Exit.'.format(convtype))
sys.exit()
out = '\n--> Run ICCS at window [{0:5.1f}, {1:5.1f}] wrt {2:s}. Write to header: {3:s}'
print(out.format(twcorr[0], twcorr[1], cchdr0, cchdr1))
print(' Convergence criterion: {:s}'.format(convtype))
if ccpara.stackwgt == 'coef':
wgtcoef = True
else:
wgtcoef = False
taperwindow = taperWindow(twcorr, taperwidth)
# get initial time picks
tinis = array([sacdh.gethdr(cchdr0) for sacdh in saclist])
tfins = tinis.copy()
nseis = len(saclist)
ccc = zeros(nseis)
snr = zeros(nseis)
coh = zeros(nseis)
wgts = ones(nseis)
stkdata = []
for it in list(range(maxiter)):
# recut data and update array stack
nstart, ntotal = windowIndex(saclist, tfins, twcorr, taperwindow)
windata = windowData(saclist, nstart, ntotal, taperwidth, tapertype)
sdata = normWeightStack(windata, wgts, taperwidth, tapertype)
stkdata.append(sdata)
if it == 0:
print('=== Iteration {0:d} : epsilon'.format(it))
else:
conv = convergence(stkdata[it], stkdata[it - 1])
print('=== Iteration {0:d} : {1:8.6f}'.format(it, conv))
if conv <= convepsi:
print('Array stack converged... Done. Mean corrcoef={0:.3f}'.
format(mean(ccc)))
break
# Find time lag at peak correlation between each trace and the array stack.
# Calculate cross correlation coefficient, signal/noise ratio and temporal coherence
sdatanorm = sdata / LA.norm(sdata)
for i in list(range(nseis)):
datai = windata[i]
delay, ccmax, ccpol = corrmax(sdata, datai, delta, xcorr, shift)
tfins[i] += delay
sacdh = saclist[i]
sacdh.sethdr(cchdr1, tfins[i])
if wgtcoef: # update weight only when stackwgt == coef
wgts[i] = ccpol * ccmax
ccc[i] = ccmax
sacdh.sethdr(hdrccc, ccc[i])
snr[i] = snratio(datai, delta, twcorr)
sacdh.sethdr(hdrsnr, snr[i])
coh[i] = coherence(datai * ccpol, sdatanorm)
sacdh.sethdr(hdrcoh, coh[i])
# get maximum time window for plot (excluding taperwindow)
bb, ee = [], []
for i in list(range(nseis)):
sacdh = saclist[i]
b = sacdh.b - tfins[i]
e = b + (sacdh.npts - 1) * delta
bb.append(b + delta)
ee.append(e - delta)
b = max(bb)
e = min(ee)
d = (e - b) * taperwidth / 2
twplot = [b + d, e - d]
# calculate final stack at twplot, save to a sacdh object: stkdh
# set time picks of stkdh as mean of tinis and tfins
taperwindow = taperWindow(twplot, taperwidth)
nstart, ntotal = windowIndex(saclist, tfins, twplot, taperwindow)
windata = windowData(saclist, nstart, ntotal, taperwidth, tapertype)
sdata = normWeightStack(windata, wgts, taperwidth, tapertype)
tinimean = mean(tinis)
tfinmean = mean(tfins)
stkdh = copy.copy(saclist[0])
stkdh.thdrs = [
-12345.,
] * 10
stkdh.users = [
-12345.,
] * 10
stkdh.kusers = [
'-12345 ',
] * 3
stkdh.b = twplot[0] - taperwindow * 0.5 + tfinmean
stkdh.npts = len(sdata)
stkdh.data = sdata
stkdh.sethdr(cchdr0, tinimean)
stkdh.sethdr(cchdr1, tfinmean)
stkdh.knetwk = 'Array '
stkdh.kstnm = 'Stack '
stkdh.netsta = 'Array.Stack'
stkdh.gcarc = -1
stkdh.dist = -1
stkdh.baz = -1
stkdh.az = -1
# set time window
stkdh.sethdr(twhdrs[0], twcorr[0] + tfinmean)
stkdh.sethdr(twhdrs[1], twcorr[1] + tfinmean)
if opts.fstack is None:
stkdh.filename = ccpara.fstack
else:
stkdh.filename = opts.fstack
for sacdh, tfin in zip(saclist, tfins):
sacdh.sethdr(twhdrs[0], tfin + twcorr[0])
sacdh.sethdr(twhdrs[1], tfin + twcorr[1])
quas = array([ccc, snr, coh])
return stkdh, stkdata, quas
def ccWeightStack(saclist, opts):
"""
Align seismograms by the iterative cross-correlation and stack algorithm.
Parameters
----------
opts.delta : sample time interval
opts.ccpara : a class instance for ICCS parameters
* qqhdrs : SAC headers to save quality factors: ccc, snr, coh
* maxiter : maximum number of iteration
* converg : convergence critrion
* cchdrs : inputand output picks of cross-correlation
* twcorr : time window for cross-correlation
"""
ccpara = opts.ccpara
delta = opts.delta
maxiter = ccpara.maxiter
convtype = ccpara.convtype
convepsi = ccpara.convepsi
taperwidth = ccpara.taperwidth
tapertype = ccpara.tapertype
xcorr = ccpara.xcorr
shift = ccpara.shift
twhdrs = ccpara.twhdrs
qqhdrs = ccpara.qheaders
cchdrs = ccpara.cchdrs
twcorr = ccpara.twcorr
hdrccc, hdrsnr, hdrcoh, = qqhdrs[:3]
cchdr0, cchdr1 = cchdrs
if convtype == 'coef':
convergence = coConverg
elif convtype == 'resi':
convergence = reConverg
else:
print('Unknown convergence criterion: {:s}. Exit.'.format(convtype))
sys.exit()
out = '\n--> Run ICCS at window [{0:5.1f}, {1:5.1f}] wrt {2:s}. Write to header: {3:s}'
print(out.format(twcorr[0], twcorr[1], cchdr0, cchdr1))
print(' Convergence criterion: {:s}'.format(convtype))
if ccpara.stackwgt == 'coef':
wgtcoef = True
else:
wgtcoef = False
taperwindow = taperWindow(twcorr, taperwidth)
# get initial time picks
tinis = array([ sacdh.gethdr(cchdr0) for sacdh in saclist])
tfins = tinis.copy()
nseis = len(saclist)
ccc = zeros(nseis)
snr = zeros(nseis)
coh = zeros(nseis)
wgts = ones(nseis)
stkdata = []
for it in range(maxiter):
# recut data and update array stack
nstart, ntotal = windowIndex(saclist, tfins, twcorr, taperwindow)
windata = windowData(saclist, nstart, ntotal, taperwidth, tapertype)
sdata = normWeightStack(windata, wgts, taperwidth, tapertype)
stkdata.append(sdata)
if it == 0:
print ('=== Iteration {0:d} : epsilon'.format(it))
else:
conv = convergence(stkdata[it], stkdata[it-1])
print ('=== Iteration {0:d} : {1:8.6f}'.format(it, conv))
if conv <= convepsi:
print ('Array stack converged... Done. Mean corrcoef={0:.3f}'.format(mean(ccc)))
break
# Find time lag at peak correlation between each trace and the array stack.
# Calculate cross correlation coefficient, signal/noise ratio and temporal coherence
sdatanorm = sdata/l2norm(sdata)
for i in range(nseis):
datai = windata[i]
delay, ccmax, ccpol = corrmax(sdata, datai, delta, xcorr, shift)
tfins[i] += delay
sacdh = saclist[i]
sacdh.sethdr(cchdr1, tfins[i])
if wgtcoef: # update weight only when stackwgt == coef
wgts[i] = ccpol * ccmax
ccc[i] = ccmax
sacdh.sethdr(hdrccc, ccc[i])
snr[i] = snratio(datai, delta, twcorr)
sacdh.sethdr(hdrsnr, snr[i])
coh[i] = coherence(datai*ccpol, sdatanorm)
sacdh.sethdr(hdrcoh, coh[i])
# get maximum time window for plot (excluding taperwindow)
bb, ee = [], []
for i in range(nseis):
sacdh = saclist[i]
b = sacdh.b - tfins[i]
e = b + (sacdh.npts-1)* delta
bb.append(b+delta)
ee.append(e-delta)
b = max(bb)
e = min(ee)
d = (e-b)*taperwidth/2
twplot = [b+d, e-d]
# calculate final stack at twplot, save to a sacdh object: stkdh
# set time picks of stkdh as mean of tinis and tfins
taperwindow = taperWindow(twplot, taperwidth)
nstart, ntotal = windowIndex(saclist, tfins, twplot, taperwindow)
windata = windowData(saclist, nstart, ntotal, taperwidth, tapertype)
sdata = normWeightStack(windata, wgts, taperwidth, tapertype)
tinimean = mean(tinis)
tfinmean = mean(tfins)
stkdh = copy.copy(saclist[0])
stkdh.thdrs = [-12345.,] * 10
stkdh.users = [-12345.,] * 10
stkdh.kusers = ['-12345 ',] * 3
stkdh.b = twplot[0] - taperwindow*0.5 + tfinmean
stkdh.npts = len(sdata)
stkdh.data = sdata
stkdh.sethdr(cchdr0, tinimean)
stkdh.sethdr(cchdr1, tfinmean)
stkdh.knetwk = 'Array '
stkdh.kstnm = 'Stack '
stkdh.netsta = 'Array.Stack'
stkdh.gcarc = -1
stkdh.dist = -1
stkdh.baz = -1
stkdh.az = -1
# set time window
stkdh.sethdr(twhdrs[0], twcorr[0]+tfinmean)
stkdh.sethdr(twhdrs[1], twcorr[1]+tfinmean)
if opts.fstack is None:
stkdh.filename = ccpara.fstack
else:
stkdh.filename = opts.fstack
for sacdh, tfin in zip(saclist, tfins):
sacdh.sethdr(twhdrs[0], tfin+twcorr[0])
sacdh.sethdr(twhdrs[1], tfin+twcorr[1])
quas = array([ ccc, snr, coh ])
return stkdh, stkdata, quas
