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Ludger Küperkoch
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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Created Mar 2015
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Transcription of the rezipe of Diehl et al. (2009) for consistent phase
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picking. For a given inital (the most likely) pick, the corresponding earliest
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and latest possible pick is calculated based on noise measurements in front of
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the most likely pick and signal wavelength derived from zero crossings.
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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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