Added new function wadaticheck to test certainty of S-onsets using Wadati diagram.
This commit is contained in:
Ludger Küperkoch
parent
b91f4c9619
commit
aa624c0358
@ -10,7 +10,7 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from obspy.core import Stream
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from obspy.core import Stream, UTCDateTime
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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@ -66,9 +66,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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#get earliest possible pick
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#determine all zero crossings in signal window
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# remove mean from signal window
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signal = x[isignal] - x[isignal].mean()
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zc = crossings_nonzero_all(signal)
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zc = crossings_nonzero_all(x[isignal])
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#calculate mean half period T0 of signal as the average of the
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T0 = np.mean(np.diff(zc)) * X[0].stats.delta #this is half wave length!
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#T0/4 is assumed as time difference between most likely and earliest possible pick!
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@ -161,7 +159,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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index1 = []
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i = 0
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for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
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i += 1
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i = i + 1
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if xraw[j - 1] <= 0 and xraw[j] >= 0:
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zc1.append(t[ipick][i])
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index1.append(i)
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@ -196,7 +194,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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index2 = []
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i = 0
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for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
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i += 1
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i = i + 1
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if xfilt[j - 1] <= 0 and xfilt[j] >= 0:
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zc2.append(t[ipick][i])
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index2.append(i)
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@ -382,4 +380,117 @@ def getsignalwin(t, t1, tsignal):
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return isignal
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def wadaticheck(pickdic, dttolerance, iplot):
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'''
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Function to calculate Wadati-diagram from given P and S onsets in order
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to detect S pick outliers. If a certain S-P time deviates from regression
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of S-P time the S pick is marked and down graded.
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: param: pickdic, dictionary containing picks and quality parameters
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: type: dictionary
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: param: dttolerance, maximum adjusted deviation of S-P time from
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S-P time regression
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: type: float
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: param: iplot, if iplot > 1, Wadati diagram is shown
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: type: int
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'''
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checkedonsets = pickdic
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# search for good quality picks and calculate S-P time
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Ppicks = []
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Spicks = []
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SPtimes = []
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vpvs = []
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for key in pickdic:
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if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
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# calculate S-P time
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spt = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(pickdic[key]['P']['mpp'])
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# add S-P time to dictionary
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pickdic[key]['SPt'] = spt
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# add P onsets and corresponding S-P times to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,1,1,0,0,0)
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Ppicks.append(UTCPpick)
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Spicks.append(UTCSpick)
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SPtimes.append(spt)
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vpvs.append(UTCPpick/UTCSpick)
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# calculate average vp/vs ratio before check
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vpvsr = np.mean(vpvs)
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print 'wadaticheck: Average Vp/Vs ratio before check:', vpvsr
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if len(SPtimes) >= 3:
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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wdfit = np.polyval(p1, Ppicks)
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wfitflag = 0
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checkedPpicks = []
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checkedSpicks = []
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checkedSPtimes = []
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checkedvpvs = []
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# calculate deviations from Wadati regression
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for key in pickdic:
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if pickdic[key].has_key('SPt'):
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ii = 0
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wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
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ii += 1
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# check, if deviation is larger than adjusted
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if wddiff >= dttolerance:
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# mark onset and downgrade S-weight to 4
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# (not used anymore)
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marker = 'badWadatiCheck'
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pickdic[key]['S']['weight'] = 4
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else:
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marker = 'goodWadatiCheck'
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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) - \
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UTCDateTime(1970,1,1,0,0,0)
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checkedPpicks.append(checkedPpick)
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) - \
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UTCDateTime(1970,1,1,0,0,0)
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checkedSpicks.append(checkedSpick)
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checkedSPtime = UTCDateTime(pickdic[key]['S']['mpp']) - \
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UTCDateTime(pickdic[key]['P']['mpp'])
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checkedSPtimes.append(checkedSPtime)
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checkedvpvs.append(checkedPpick/checkedSpick)
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pickdic[key]['S']['marked'] = marker
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# calculate average vp/vs ratio after check
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cvpvsr = np.mean(checkedvpvs)
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print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
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# calculate new slope
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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wdfit2 = np.polyval(p2, checkedPpicks)
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checkedonsets = pickdic
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else:
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print 'wadaticheck: Not enough S-P times available for reliable regression!'
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print 'Skip wadati check!'
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wfitflag = 1
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# plot results
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iplot = 2
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if iplot > 1:
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f = plt.figure(iplot)
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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if wfitflag == 0:
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f2, = plt.plot(Ppicks, wdfit, 'k')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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plt.ylabel('S-P Times [s]')
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plt.xlabel('P Times [s]')
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plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(old)=%5.2f, Vp/Vs(checked)=%5.2f' \
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% (len(SPtimes), vpvsr, cvpvsr))
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plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', 'Reliable S-Picks', \
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'Wadati 2'], loc='best')
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plt.show()
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raw_input()
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plt.close(f)
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return checkedonsets
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