New module containing some helpful functions, replaces getSNR, fmpicker, and earllatepicker.
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Ludger Küperkoch
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bebe3a3c45
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359
pylot/core/pick/utils.py
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359
pylot/core/pick/utils.py
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#!/usr/bin/env python
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#
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# -*- coding: utf-8 -*-
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"""
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Created Mar/Apr 2015
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Collection of helpful functions for manual and automatic picking.
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:author: Ludger Kueperkoch / MAGS2 EP3 working group
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"""
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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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import argparse
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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'''
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Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
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as reasonable uncertainties. Latest possible pick is based on noise level,
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earliest possible pick is half a signal wavelength in front of most likely
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pick given by PragPicker or manually set by analyst. Most likely pick
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(initial pick Pick1) must be given.
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:param: X, time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
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:type: int
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:param: TSNR, length of time windows around pick used to determine SNR [s]
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:type: tuple (T_noise, T_gap, T_signal)
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:param: Pick1, initial (most likely) onset time, starting point for earllatepicker
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:type: float
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:param: iplot, if given, results are plotted in figure(iplot)
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:type: int
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'''
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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LPick = None
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EPick = None
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PickError = None
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print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
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x = X[0].data
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate, X[0].stats.delta)
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#some parameters needed:
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tnoise = TSNR[0] #noise window length for calculating noise level
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tsignal = TSNR[2] #signal window length
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tsafety = TSNR[1] #safety gap between signal onset and noise window
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#get latest possible pick
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#get noise window
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inoise = np.where((t <= max([Pick1 - tsafety, 0])) \
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& (t >= max([Pick1 - tnoise - tsafety, 0])))
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#get signal window
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isignal = np.where((t <= min([Pick1 + tsignal + tsafety, len(x)])) \
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& (t >= Pick1))
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#calculate noise level
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nlevel = max(abs(x[inoise])) * nfac
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#get time where signal exceeds nlevel
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ilup = np.where(x[isignal] > nlevel)
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ildown = np.where(x[isignal] < -nlevel)
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if len(ilup[0]) <= 1 and len(ildown[0]) <= 1:
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print 'earllatepicker: Signal lower than noise level, misspick?'
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return
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il = min([ilup[0][0], ildown[0][0]])
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LPick = t[isignal][il]
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#get earliest possible pick
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#get next 2 zero crossings after most likely pick
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#initial onset is assumed to be the first zero crossing
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zc = []
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zc.append(Pick1)
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i = 0
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for j in range(isignal[0][1],isignal[0][len(t[isignal]) - 1]):
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i = i+ 1
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if x[j-1] <= 0 and x[j] >= 0:
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zc.append(t[isignal][i])
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elif x[j-1] > 0 and x[j] <= 0:
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zc.append(t[isignal][i])
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if len(zc) == 3:
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break
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#calculate maximum period T0 of signal out of zero crossings
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T0 = max(np.diff(zc)) #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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EPick = Pick1 - T0/2
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#get symmetric pick error as mean from earliest and latest possible pick
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#by weighting latest possible pick two times earliest possible pick
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diffti_tl = LPick - Pick1
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diffti_te = Pick1 - EPick
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PickError = (diffti_te + 2 * diffti_tl) / 3
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if iplot is not None:
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plt.figure(iplot)
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p1, = plt.plot(t, x, 'k')
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p2, = plt.plot(t[inoise], x[inoise])
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p3, = plt.plot(t[isignal], x[isignal], 'r')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
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loc='best')
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plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
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plt.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2)
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plt.plot([LPick, LPick], [max(x)/2, -max(x)/2], '--k')
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plt.plot([EPick, EPick], [max(x)/2, -max(x)/2], '--k')
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plt.plot([Pick1 + PickError, Pick1 + PickError], [max(x)/2, -max(x)/2], 'r--')
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plt.plot([Pick1 - PickError, Pick1 - PickError], [max(x)/2, -max(x)/2], 'r--')
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plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
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plt.yticks([])
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ax = plt.gca()
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ax.set_xlim([t[inoise[0][0]] - 2, t[isignal[0][len(isignal) - 1]] + 3])
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plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % X[0].stats.station)
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plt.show()
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raw_input()
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plt.close(iplot)
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return EPick, LPick, PickError
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--X', type=~obspy.core.stream.Stream, help='time series (seismogram) read with obspy module read')
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parser.add_argument('--nfac', type=int, help='(noise factor), nfac times noise level to calculate latest possible pick')
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parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
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[s] (Tnoise, Tgap, Tsignal)')
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parser.add_argument('--Pick1', type=float, help='Onset time of most likely pick')
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parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
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args = parser.parse_args()
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earllatepicker(args.X, args.nfac, args.TSNR, args.Pick1, args.iplot)
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def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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'''
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Function to derive first motion (polarity) of given phase onset Pick.
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Calculation is based on zero crossings determined within time window pickwin
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after given onset time.
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:param: Xraw, unfiltered time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: Xfilt, filtered time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: pickwin, time window after onset Pick within zero crossings are calculated
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:type: float
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:param: Pick, initial (most likely) onset time, starting point for fmpicker
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:type: float
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:param: iplot, if given, results are plotted in figure(iplot)
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:type: int
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'''
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assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
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assert isinstance(Xfilt, Stream), "%s is not a stream object" % str(Xfilt)
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FM = None
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if Pick is not None:
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print 'fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...'
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xraw = Xraw[0].data
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xfilt = Xfilt[0].data
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t = np.arange(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate, Xraw[0].stats.delta)
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#get pick window
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ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
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#remove mean
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xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
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xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
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#get next zero crossing after most likely pick
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#initial onset is assumed to be the first zero crossing
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#first from unfiltered trace
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zc1 = []
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zc1.append(Pick)
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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 = 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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elif 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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if len(zc1) == 3:
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break
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#if time difference betweeen 1st and 2cnd zero crossing
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#is too short, get time difference between 1st and 3rd
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#to derive maximum
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if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
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li1 = index1[1]
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else:
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li1 = index1[0]
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if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
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print 'earllatepicker: Onset on unfiltered trace too emergent for first motion determination!'
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P1 = None
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else:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
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islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
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#calculate slope as polynomal fit of order 1
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xslope1 = np.arange(0, len(xraw[islope1]), 1)
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P1 = np.polyfit(xslope1, xraw[islope1], 1)
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datafit1 = np.polyval(P1, xslope1)
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#now using filterd trace
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#next zero crossing after most likely pick
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zc2 = []
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zc2.append(Pick)
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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 = 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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elif 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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if len(zc2) == 3:
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break
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#if time difference betweeen 1st and 2cnd zero crossing
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#is too short, get time difference between 1st and 3rd
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#to derive maximum
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if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
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li2 = index2[1]
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else:
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li2 = index2[0]
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if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
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print 'earllatepicker: Onset on filtered trace too emergent for first motion determination!'
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P2 = None
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else:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
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islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
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#calculate slope as polynomal fit of order 1
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xslope2 = np.arange(0, len(xfilt[islope2]), 1)
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P2 = np.polyfit(xslope2, xfilt[islope2], 1)
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datafit2 = np.polyval(P2, xslope2)
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#compare results
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if P1 is not None and P2 is not None:
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if P1[0] < 0 and P2[0] < 0:
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FM = 'D'
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elif P1[0] >= 0 and P2[0] < 0:
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FM = '-'
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elif P1[0] < 0 and P2[0]>= 0:
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FM = '-'
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elif P1[0] > 0 and P2[0] > 0:
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FM = 'U'
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elif P1[0] <= 0 and P2[0] > 0:
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FM = '+'
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elif P1[0] > 0 and P2[0] <= 0:
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FM = '+'
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if iplot is not None:
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plt.figure(iplot)
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plt.subplot(2,1,1)
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plt.plot(t, xraw, 'k')
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p1, = plt.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2)
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if P1 is not None:
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p2, = plt.plot(t[islope1], xraw[islope1])
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p3, = plt.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14)
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p4, = plt.plot(t[islope1], datafit1, '--g', linewidth=2)
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plt.legend([p1, p2, p3, p4], ['Pick', 'Slope Window', 'Zero Crossings', 'Slope'], \
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loc='best')
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plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
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ax = plt.gca()
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ax.set_xlim([t[islope1[0][0]] - 0.1, t[islope1[0][len(islope1) - 1]] + 0.3])
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plt.yticks([])
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plt.title('First-Motion Determination, %s, Unfiltered Data' % Xraw[0].stats.station)
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plt.subplot(2,1,2)
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plt.title('First-Motion Determination, Filtered Data')
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plt.plot(t, xfilt, 'k')
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p1, = plt.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b', linewidth=2)
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if P2 is not None:
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p2, = plt.plot(t[islope2], xfilt[islope2])
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p3, = plt.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
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p4, = plt.plot(t[islope2], datafit2, '--g', linewidth=2)
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plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
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ax = plt.gca()
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ax.set_xlim([t[islope2[0][0]] - 0.1, t[islope2[0][len(islope2) - 1]] + 0.3])
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plt.xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
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plt.yticks([])
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plt.show()
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raw_input()
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plt.close(iplot)
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return FM
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--Xraw', type=~obspy.core.stream.Stream, help='unfiltered time series (seismogram) read with obspy module read')
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parser.add_argument('--Xfilt', type=~obspy.core.stream.Stream, help='filtered time series (seismogram) read with obspy module read')
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parser.add_argument('--pickwin', type=float, help='length of pick window [s] for first motion determination')
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parser.add_argument('--Pick', type=float, help='Onset time of most likely pick')
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parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
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args = parser.parse_args()
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earllatepicker(args.Xraw, args.Xfilt, args.pickwin, args.Pick, args.iplot)
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def getSNR(X, TSNR, t1):
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'''
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Function to calculate SNR of certain part of seismogram relative to
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given time (onset) out of given noise and signal windows. A safety gap
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between noise and signal part can be set. Returns SNR and SNR [dB].
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:param: X, time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
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:type: tuple (T_noise, T_gap, T_signal)
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:param: t1, initial time (onset) from which noise and signal windows are calculated
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:type: float
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'''
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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SNR = None
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SNRdB = None
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x = X[0].data
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate, X[0].stats.delta)
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#some parameters needed:
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tnoise = TSNR[0] #noise window length for calculating noise level
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tsignal = TSNR[2] #signal window length
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tsafety = TSNR[1] #safety gap between signal onset and noise window
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#get noise window
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inoise = np.where((t <= max([t1 - tsafety, 0])) \
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& (t >= max([t1 - tnoise - tsafety, 0])))
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#get signal window
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isignal = np.where((t <= min([t1 + tsignal + tsafety, len(x)])) \
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& (t >= t1))
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if np.size(inoise) < 1:
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print 'getSNR: Empty array inoise, check noise window!'
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return
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elif np.size(isignal) < 1:
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print 'getSNR: Empty array isignal, check signal window!'
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return
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#calculate ratios
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SNR = max(abs(x[isignal])) / np.mean(abs(x[inoise]))
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SNRdB = 20 * np.log10(SNR)
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return SNR, SNRdB
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--X', type=~obspy.core.stream.Stream, help='time series (seismogram) read with obspy module read')
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parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
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[s] (Tnoise, Tgap, Tsignal)')
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parser.add_argument('--t1', type=float, help='initial time from which noise and signal windows are calculated')
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args = parser.parse_args()
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getSNR(args.X, args.TSNR, args.t1)
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