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Modofied checksignallength: uses RMS trace of all components (if available) to check signal length. This avoids skipping of P pick, if P coda is very weak. If only vertical trace is available, rms of vertical trace is used instead with smaller required minimum signal length.

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This commit is contained in:
Ludger Küperkoch 2015-09-04 11:16:34 +02:00
parent de608798b9
commit 23430c9d90
1 changed files with 27 additions and 20 deletions
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47
pylot/core/pick/utils.py
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@ -7,7 +7,7 @@
:author: Ludger Kueperkoch / MAGS2 EP3 working group :author: Ludger Kueperkoch / MAGS2 EP3 working group
""" """
import pdb
import numpy as np import numpy as np
import scipy as sc import scipy as sc
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -562,9 +562,9 @@ def wadaticheck(pickdic, dttolerance, iplot):
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot): def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
''' '''
Function to detect spuriously picked noise peaks. Function to detect spuriously picked noise peaks.
Uses envelope to determine, how many samples [per cent] after Uses RMS trace of all 3 components (if available) to determine,
P onset are below certain threshold, calculated from noise how many samples [per cent] after P onset are below certain
level times noise factor. threshold, calculated from noise level times noise factor.
: param: X, time series (seismogram) : param: X, time series (seismogram)
: type: `~obspy.core.stream.Stream` : type: `~obspy.core.stream.Stream`
@ -594,23 +594,32 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
print ("Checking signal length ...") print ("Checking signal length ...")
x = X[0].data if len(X) > 1:
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate, # all three components available
# make sure, all components have equal lengths
ilen = min([len(X[0].data), len(X[1].data), len(X[2].data)])
x1 = X[0][0:ilen]
x2 = X[1][0:ilen]
x3 = X[2][0:ilen]
# get RMS trace
rms = np.sqrt((np.power(x1, 2) + np.power(x2, 2) + np.power(x3, 2)) / 3)
else:
x1 = X[0].data
rms = np.sqrt(np.power(2, x1))
t = np.arange(0, ilen / X[0].stats.sampling_rate,
X[0].stats.delta) X[0].stats.delta)
# generate envelope function from Hilbert transform
y = np.imag(sc.signal.hilbert(x))
e = np.sqrt(np.power(x, 2) + np.power(y, 2))
# get noise window in front of pick plus saftey gap # get noise window in front of pick plus saftey gap
inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1]) inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1])
# get signal window # get signal window
isignal = getsignalwin(t, pick, TSNR[2]) isignal = getsignalwin(t, pick, minsiglength)
# calculate minimum adjusted signal level # calculate minimum adjusted signal level
minsiglevel = max(e[inoise]) * nfac minsiglevel = max(rms[inoise]) * nfac
# minimum adjusted number of samples over minimum signal level # minimum adjusted number of samples over minimum signal level
minnum = len(isignal) * minpercent/100 minnum = len(isignal) * minpercent/100
# get number of samples above minimum adjusted signal level # get number of samples above minimum adjusted signal level
numoverthr = len(np.where(e[isignal] >= minsiglevel)[0]) numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
if numoverthr >= minnum: if numoverthr >= minnum:
print ("checksignallength: Signal reached required length.") print ("checksignallength: Signal reached required length.")
@ -623,16 +632,14 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
if iplot == 2: if iplot == 2:
plt.figure(iplot) plt.figure(iplot)
p1, = plt.plot(t,x, 'k') p1, = plt.plot(t,rms, 'k')
p2, = plt.plot(t[inoise], e[inoise], 'c') p2, = plt.plot(t[inoise], rms[inoise], 'c')
p3, = plt.plot(t[isignal],e[isignal], 'r') p3, = plt.plot(t[isignal],rms[isignal], 'r')
p2, = plt.plot(t[inoise], e[inoise])
p3, = plt.plot(t[isignal],e[isignal], 'r')
p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \ p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
[minsiglevel, minsiglevel], 'g', linewidth=2) [minsiglevel, minsiglevel], 'g', linewidth=2)
p5, = plt.plot([pick, pick], [min(x), max(x)], 'b', linewidth=2) p5, = plt.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \ plt.legend([p1, p2, p3, p4, p5], ['RMS Data', 'RMS Noise Window', \
'Envelope Signal Window', 'Minimum Signal Level', \ 'RMS Signal Window', 'Minimum Signal Level', \
'Onset'], loc='best') 'Onset'], loc='best')
plt.xlabel('Time [s] since %s' % X[0].stats.starttime) plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
plt.ylabel('Counts') plt.ylabel('Counts')