New function invoked by autoPyLoT for automated picking of onset times. Main tool for automatic picking!
This commit is contained in:
Ludger Küperkoch
parent
5be662524f
commit
74682952e7
401
pylot/core/pick/run_autopicking.py
Executable file
401
pylot/core/pick/run_autopicking.py
Executable file
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Function to run automated picking algorithms using AIC,
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HOS and AR prediction. Uses object CharFuns and Picker and
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function conglomerate utils.
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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"""
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from obspy.core import read
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import matplotlib.pyplot as plt
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import numpy as np
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from pylot.core.pick.CharFuns import *
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from pylot.core.pick.Picker import *
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from pylot.core.pick.CharFuns import *
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from pylot.core.pick import utils
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import pdb
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def run_autopicking(wfstream, pickparam):
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'''
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param: wfstream
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:type: `~obspy.core.stream.Stream`
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param: pickparam
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:type: container of picking parameters from input file,
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usually autoPyLoT.in
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'''
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# declaring pickparam variables (only for convenience)
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# read your autoPyLoT.in for details!
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#special parameters for P picking
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algoP = pickparam.getParam('algoP')
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iplot = pickparam.getParam('iplot')
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pstart = pickparam.getParam('pstart')
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pstop = pickparam.getParam('pstop')
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thosmw = pickparam.getParam('tlta')
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hosorder = pickparam.getParam('hosorder')
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tsnrz = pickparam.getParam('tsnrz')
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hosorder = pickparam.getParam('hosorder')
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bpz1 = pickparam.getParam('bpz1')
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bpz2 = pickparam.getParam('bpz2')
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pickwinP = pickparam.getParam('pickwinP')
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tsmoothP = pickparam.getParam('tsmoothP')
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ausP = pickparam.getParam('ausP')
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nfacP = pickparam.getParam('nfacP')
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tpred1z = pickparam.getParam('tpred1z')
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tdet1z = pickparam.getParam('tdet1z')
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Parorder = pickparam.getParam('Parorder')
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addnoise = pickparam.getParam('addnoise')
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Precalcwin = pickparam.getParam('Precalcwin')
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minAICPslope = pickparam.getParam('minAICPslope')
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minAICPSNR = pickparam.getParam('minAICPSNR')
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timeerrorsP = pickparam.getParam('timeerrorsP')
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#special parameters for S picking
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algoS = pickparam.getParam('algoS')
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sstart = pickparam.getParam('sstart')
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sstop = pickparam.getParam('sstop')
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bph1 = pickparam.getParam('bph1')
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bph2 = pickparam.getParam('bph2')
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tsnrh = pickparam.getParam('tsnrh')
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pickwinS = pickparam.getParam('pickwinS')
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tpred1h = pickparam.getParam('tpred1h')
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tdet1h = pickparam.getParam('tdet1h')
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tpred2h = pickparam.getParam('tpred2h')
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tdet2h = pickparam.getParam('tdet2h')
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Sarorder = pickparam.getParam('Sarorder')
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aictsmoothS = pickparam.getParam('aictsmoothS')
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tsmoothS = pickparam.getParam('tsmoothS')
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ausS = pickparam.getParam('ausS')
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minAICSslope = pickparam.getParam('minAICSslope')
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minAICSSNR = pickparam.getParam('minAICSSNR')
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Srecalcwin = pickparam.getParam('Srecalcwin')
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nfacS = pickparam.getParam('nfacS')
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timeerrorsS = pickparam.getParam('timeerrorsS')
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# split components
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zdat = wfstream.select(component="Z")
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edat = wfstream.select(component="E")
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ndat = wfstream.select(component="N")
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if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
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print '##########################################'
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print 'run_autopicking: Working on P onset of station %s' % zdat[0].stats.station
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print 'Filtering vertical trace ...'
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print zdat
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z_copy = zdat.copy()
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#filter and taper data
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tr_filt = zdat[0].copy()
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tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1], zerophase=False)
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tr_filt.taper(max_percentage=0.05, type='hann')
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z_copy[0].data = tr_filt.data
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##############################################################
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#check length of waveform and compare with cut times
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Lc = pstop - pstart
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Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
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Ldiff = Lwf - Lc
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if Ldiff < 0:
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print 'run_autopicking: Cutting times are too large for actual waveform!'
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print 'Use entire waveform instead!'
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pstart = 0
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pstop = len(zdat[0].data) * zdat[0].stats.delta
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cuttimes = [pstart, pstop]
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if algoP == 'HOS':
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#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
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cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) #instance of HOScf
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elif algoP == 'ARZ':
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#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
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cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
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##############################################################
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#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
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#class needs stream object => build it
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tr_aic = tr_filt.copy()
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tr_aic.data =cf1.getCF()
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z_copy[0].data = tr_aic.data
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aiccf = AICcf(z_copy, cuttimes) #instance of AICcf
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##############################################################
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#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
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aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
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##############################################################
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#go on with processing if AIC onset passes quality control
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if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
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print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
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(aicpick.getSlope(), aicpick.getSNR())
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print 'Go on with refined picking ...'
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#re-filter waveform with larger bandpass
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print 'run_autopicking: re-filtering vertical trace ...'
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z_copy = zdat.copy()
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tr_filt = zdat[0].copy()
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tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1], zerophase=False)
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tr_filt.taper(max_percentage=0.05, type='hann')
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z_copy[0].data = tr_filt.data
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#############################################################
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#re-calculate CF from re-filtered trace in vicinity of initial onset
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cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])), \
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round(min([len(zdat[0].data) * zdat[0].stats.delta, \
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aicpick.getpick() + Precalcwin]))]
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if algoP == 'HOS':
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#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
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cf2 = HOScf(z_copy, cuttimes2, thosmw, hosorder) #instance of HOScf
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elif algoP == 'ARZ':
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#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
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cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
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##############################################################
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#get refined onset time from CF2 using class Picker
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refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP, aicpick.getpick())
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#############################################################
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#quality assessment
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#get earliest and latest possible pick and symmetrized uncertainty
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[lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, refPpick.getpick(), iplot)
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#get SNR
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[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, refPpick.getpick())
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#weight P-onset using symmetric error
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if Perror <= timeerrorsP[0]:
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Pweight = 0
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elif Perror > timeerrorsP[0] and Perror <= timeerrorsP[1]:
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Pweight = 1
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elif Perror > timeerrorsP[1] and Perror <= timeerrorsP[2]:
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Pweight = 2
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elif Perror > timeerrorsP[2] and Perror <= timeerrorsP[3]:
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Pweight = 3
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elif Perror > timeerrorsP[3]:
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Pweight = 4
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print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f' % (Pweight, SNRP, SNRPdB)
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else:
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print 'Bad initial (AIC) P-pick, skip this onset!'
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Pweight = 4
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else:
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print 'run_autopicking: No vertical component data available, skipping station!'
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return
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if edat is not None and ndat is not None and Pweight < 4:
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print 'Go on picking S onset ...'
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print '##################################################'
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print 'Working on S onset of station %s' % edat[0].stats.station
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print 'Filtering horizontal traces ...'
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#determine time window for calculating CF after P onset
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#cuttimesh = [round(refPpick.getpick() + sstart), round(refPpick.getpick() + sstop)]
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cuttimesh = [round(max([refPpick.getpick() + sstart, 0])), \
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round(min([refPpick.getpick() + sstop, Lwf]))]
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if algoS == 'ARH':
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print edat, ndat
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#re-create stream object including both horizontal components
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hdat = edat.copy()
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hdat += ndat
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h_copy = hdat.copy()
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#filter and taper data
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trH1_filt = hdat[0].copy()
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trH2_filt = hdat[1].copy()
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trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
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trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
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trH1_filt.taper(max_percentage=0.05, type='hann')
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trH2_filt.taper(max_percentage=0.05, type='hann')
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h_copy[0].data = trH1_filt.data
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h_copy[1].data = trH2_filt.data
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elif algoS == 'AR3':
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print zdat, edat, ndat
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#re-create stream object including both horizontal components
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hdat = zdat.copy()
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hdat += edat
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hdat += ndat
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h_copy = hdat.copy()
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#filter and taper data
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trH1_filt = hdat[0].copy()
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trH2_filt = hdat[1].copy()
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trH3_filt = hdat[2].copy()
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trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
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trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
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trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
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trH1_filt.taper(max_percentage=0.05, type='hann')
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trH2_filt.taper(max_percentage=0.05, type='hann')
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trH3_filt.taper(max_percentage=0.05, type='hann')
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h_copy[0].data = trH1_filt.data
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h_copy[1].data = trH2_filt.data
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h_copy[2].data = trH3_filt.data
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##############################################################
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if algoS == 'ARH':
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#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
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arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
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elif algoS == 'AR3':
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#calculate ARH-CF using subclass AR3cf of class CharcteristicFunction
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arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
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##############################################################
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#calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
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#class needs stream object => build it
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tr_arhaic = trH1_filt.copy()
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tr_arhaic.data = arhcf1.getCF()
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h_copy[0].data = tr_arhaic.data
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#calculate ARH-AIC-CF
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haiccf = AICcf(h_copy, cuttimesh) #instance of AICcf
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##############################################################
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#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
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aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None, aictsmoothS)
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###############################################################
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#go on with processing if AIC onset passes quality control
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if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
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print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
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% (aicarhpick.getSlope(), aicarhpick.getSNR())
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print 'Go on with refined picking ...'
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#re-calculate CF from re-filtered trace in vicinity of initial onset
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cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin), \
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round(aicarhpick.getpick() + Srecalcwin)]
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#re-filter waveform with larger bandpass
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print 'run_autopicking: re-filtering horizontal traces...'
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h_copy = hdat.copy()
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#filter and taper data
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if algoS == 'ARH':
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trH1_filt = hdat[0].copy()
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trH2_filt = hdat[1].copy()
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trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
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trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
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trH1_filt.taper(max_percentage=0.05, type='hann')
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trH2_filt.taper(max_percentage=0.05, type='hann')
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h_copy[0].data = trH1_filt.data
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h_copy[1].data = trH2_filt.data
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#############################################################
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arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
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elif algoS == 'AR3':
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trH1_filt = hdat[0].copy()
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trH2_filt = hdat[1].copy()
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trH3_filt = hdat[2].copy()
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trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
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trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
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trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
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trH1_filt.taper(max_percentage=0.05, type='hann')
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trH2_filt.taper(max_percentage=0.05, type='hann')
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trH3_filt.taper(max_percentage=0.05, type='hann')
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h_copy[0].data = trH1_filt.data
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h_copy[1].data = trH2_filt.data
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h_copy[2].data = trH3_filt.data
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#############################################################
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arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
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#get refined onset time from CF2 using class Picker
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refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS, tsmoothS, aicarhpick.getpick())
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#############################################################
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#quality assessment
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#get earliest and latest possible pick and symmetrized uncertainty
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h_copy[0].data = trH1_filt.data
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[lpickS1, epickS1, Serror1] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
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h_copy[0].data = trH2_filt.data
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[lpickS2, epickS2, Serror2] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
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if algoS == 'ARH':
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#get earliest pick of both earliest possible picks
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epick = [epickS1, epickS2]
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lpick = [lpickS1, lpickS2]
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pickerr = [Serror1, Serror2]
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ipick =np.argmin([epickS1, epickS2])
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elif algoS == 'AR3':
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[lpickS3, epickS3, Serror3] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
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#get earliest pick of all three picks
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epick = [epickS1, epickS2, epickS3]
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lpick = [lpickS1, lpickS2, lpickS3]
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pickerr = [Serror1, Serror2, Serror3]
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ipick =np.argmin([epickS1, epickS2, epickS3])
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epickS = epick[ipick]
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lpickS = lpick[ipick]
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Serror = pickerr[ipick]
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#get SNR
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[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, refSpick.getpick())
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#weight S-onset using symmetric error
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if Serror <= timeerrorsS[0]:
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Sweight = 0
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elif Serror > timeerrorsS[0] and Serror <= timeerrorsS[1]:
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Sweight = 1
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elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
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Sweight = 2
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elif Serror > timeerrorsS[2] and Serror <= timeerrorsS[3]:
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Sweight = 3
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elif Serror > timeerrorsS[3]:
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Sweight = 4
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print 'run_autopicking: S-weight: %d, SNR: %f, SNR[dB]: %f' % (Sweight, SNRS, SNRSdB)
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else:
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print 'Bad initial (AIC) S-pick, skip this onset!'
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Sweight = 4
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else:
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print 'run_autopicking: No horizontal component data available, skipping S picking!'
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return
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##############################################################
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if iplot > 0:
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#plot vertical trace
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plt.figure()
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plt.subplot(3,1,1)
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tdata = np.arange(0, tr_filt.stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
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p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
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p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
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p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
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p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
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plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
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plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
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p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
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plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
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plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
|
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plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
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||||
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
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plt.yticks([])
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||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (tr_filt.stats.station, \
|
||||
tr_filt.stats.channel, Pweight, SNRP, SNRPdB))
|
||||
plt.suptitle(tr_filt.stats.starttime)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
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||||
#plot horizontal traces
|
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plt.subplot(3,1,2)
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th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
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||||
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
|
||||
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
|
||||
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
|
||||
Sweight, SNRS, SNRSdB))
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
|
||||
plt.subplot(3,1,3)
|
||||
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
|
||||
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
|
||||
plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
|
||||
plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
|
||||
plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title(trH2_filt.stats.channel)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close()
|
||||
Loading…
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Reference in New Issue
Block a user