Modified earllatepicker: Mean is removed from trace calculated from noise + signal window.
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@ -53,6 +53,9 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
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inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
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# get signal window
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# get signal window
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isignal = getsignalwin(t, Pick1, TSNR[2])
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isignal = getsignalwin(t, Pick1, TSNR[2])
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# remove mean
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meanwin = np.hstack((inoise, isignal))
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x = x - np.mean(x[meanwin])
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# calculate noise level
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# calculate noise level
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nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
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nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
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# get time where signal exceeds nlevel
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# get time where signal exceeds nlevel
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@ -412,28 +415,22 @@ def wadaticheck(pickdic, dttolerance, iplot):
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SPtimes = []
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SPtimes = []
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for key in pickdic:
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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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if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
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# calculate S-P time
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# calculate S-P time
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spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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# add S-P time to dictionary
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# add S-P time to dictionary
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pickdic[key]['SPt'] = spt
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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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# add P onsets and corresponding S-P times to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp']) - UTCDateTime(1970,
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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1, 1,
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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0, 0,
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Ppicks.append(UTCPpick.timestamp)
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0)
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Spicks.append(UTCSpick.timestamp)
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp']) - UTCDateTime(1970,
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SPtimes.append(spt)
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1, 1,
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0, 0,
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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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if len(SPtimes) >= 3:
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if len(SPtimes) >= 3:
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# calculate slope
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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wdfit = np.polyval(p1, Ppicks)
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wdfit = np.polyval(p1, Ppicks)
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wfitflag = 0
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wfitflag = 0
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# calculate vp/vs ratio before check
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# calculate vp/vs ratio before check
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@ -457,23 +454,19 @@ def wadaticheck(pickdic, dttolerance, iplot):
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pickdic[key]['S']['weight'] = 9
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pickdic[key]['S']['weight'] = 9
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else:
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else:
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marker = 'goodWadatiCheck'
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marker = 'goodWadatiCheck'
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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp']) - \
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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.timestamp)
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checkedPpicks.append(checkedPpick)
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp']) - \
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checkedSpicks.append(checkedSpick.timestamp)
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UTCDateTime(1970, 1, 1, 0, 0, 0)
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checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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checkedSpicks.append(checkedSpick)
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checkedSPtime = pickdic[key]['S']['mpp'] - \
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pickdic[key]['P']['mpp']
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checkedSPtimes.append(checkedSPtime)
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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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pickdic[key]['S']['marked'] = marker
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# calculate new slope
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# calculate new slope
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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wdfit2 = np.polyval(p2, checkedPpicks)
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wdfit2 = np.polyval(p2, checkedPpicks)
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# calculate vp/vs ratio after check
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# calculate vp/vs ratio after check
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cvpvsr = p2[0] + 1
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cvpvsr = p2[0] + 1
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@ -482,26 +475,24 @@ def wadaticheck(pickdic, dttolerance, iplot):
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checkedonsets = pickdic
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checkedonsets = pickdic
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else:
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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 'wadaticheck: Not enough S-P times available for reliable regression!'
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print 'Skip wadati check!'
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print 'Skip wadati check!'
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wfitflag = 1
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wfitflag = 1
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# plot results
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# plot results
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if iplot > 1:
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if iplot > 1:
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plt.figure(iplot)
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plt.figure(iplot)
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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if wfitflag == 0:
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if wfitflag == 0:
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f2, = plt.plot(Ppicks, wdfit, 'k')
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f2, = plt.plot(Ppicks, wdfit, 'k')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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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.ylabel('S-P Times [s]')
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plt.xlabel('P Times [s]')
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plt.xlabel('P Times [s]')
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plt.title(
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plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f, Vp/Vs(checked)=%5.2f' \
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'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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% (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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plt.legend([f1, f2, f3, f4],
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'Wadati 2'], loc='best')
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['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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plt.show()
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raw_input()
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raw_input()
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plt.close(iplot)
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plt.close(iplot)
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