Major changes: 1) Implemented new class EarlLatePicker for calculating earliest and lates possible pick from initial (most likely) onset, based on cook book for consistent phase picking by Diehl & Kissling 2) Modified AICPicker, uses now unsmoothed and smoothed CF for not sticking in some local minima 3) Implemented optional plotting of interims results
This commit is contained in:
Ludger Küperkoch
parent
4a48874f88
commit
b953377c58
@ -1,7 +1,7 @@
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# -*- coding: utf-8 -*-
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"""
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Created Dec 2014
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Implementation of the picking algorithms published and described in:
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Created Dec 2014 to Feb 2015
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Implementation of the automated picking algorithms published and described in:
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Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
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Automated determination of P-phase arrival times at regional and local distances
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@ -12,29 +12,28 @@ Working Group, 2012: Automated determination of S-phase arrival times using
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autoregressive prediction: application ot local and regional distances, Geophys. J. Int.,
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188, 687-702.
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The picks with the above described algorithms are assumed to be the most likely picks.
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For each most likely pick the corresponding earliest and latest possible picks are
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calculated after Diehl & Kissling (2009).
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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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 CharFuns import *
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#from pylot.core.pick.CharFuns import CharacteristicFunction
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import pdb
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from pylot.core.pick.CharFuns import CharacteristicFunction
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class AutoPicking(object):
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'''
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Superclass of different, automated picking algorithms applied on a CF determined
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using AIC, HOS, or AR prediction.
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'''
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def __init__(self, cf, Tslope, aerr, TSNR, PickWindow, aus=None, Tsmooth=None, Pick1=None):
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def __init__(self, cf, nfac, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
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'''
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:param: cf, characteristic function, on which the picking algorithm is applied
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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:param: Tslope, length of time window after pick used to determine slope
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for quality estimation [s]
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:type: float
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:param: aerr (adjusted error), percentage of maximum of CF to determine slope for quality estimation
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:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
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in EarlLatePick
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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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@ -43,25 +42,31 @@ class AutoPicking(object):
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:param: PickWindow, length of pick window [s]
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:type: float
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:param: iplot, no. of figure window for plotting interims results
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:type: integer
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:param: aus ("artificial uplift of samples"), find local minimum at i if aic(i-1)*(1+aus) >= aic(i)
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:type: float
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:param: Tsmooth, length of moving smoothing window to calculate smoothed CF [s]
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:type: float
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:param: Pick1, initial (prelimenary) onset time, starting point for PragPicker
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:param: Pick1, initial (prelimenary) onset time, starting point for PragPicker and
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EarlLatePick
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:type: float
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'''
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assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
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self.cf = cf.getCF()
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self.Tcf = cf.getTimeArray()
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self.Data = cf.getXCF()
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self.dt = cf.getIncrement()
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self.setTslope(Tslope)
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self.setaerr(aerr)
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self.setnfac(nfac)
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self.setTSNR(TSNR)
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self.setPickWindow(PickWindow)
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self.setiplot(iplot)
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self.setaus(aus)
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self.setTsmooth(Tsmooth)
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self.setpick1(Pick1)
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@ -69,33 +74,25 @@ class AutoPicking(object):
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def __str__(self):
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return '''\n\t{name} object:\n
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TSlope:\t{Tslope}\n
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aerr:\t{aerr}\n
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nfac:\t{nfac}\n
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TSNR:\t\t\t{TSNR}\n
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PickWindow:\t{PickWindow}\n
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aus:\t{aus}\n
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Tsmooth:\t{Tsmooth}\n
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Pick1:\t{Pick1}\n
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'''.format(name=type(self).__name__,
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Tslope=self.getTslope(),
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aerr=self.getaerr(),
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nfac=self.getnfac(),
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TSNR=self.getTSNR(),
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PickWindow=self.getPickWindow(),
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aus=self.getaus(),
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Tsmooth=self.getTsmooth(),
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Pick1=self.getpick1())
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def getTslope(self):
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return self.Tslope
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def getnfac(self):
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return self.nfac
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def setTslope(self, Tslope):
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self.Tslope = Tslope
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def getaerr(self):
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return self.aerr
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def setaerr(self, aerr):
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self.aerr = aerr
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def setnfac(self, nfac):
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self.nfac = nfac
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def getTSNR(self):
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return self.TSNR
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@ -124,6 +121,18 @@ class AutoPicking(object):
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def getpick(self):
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return self.Pick
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def getLpick(self):
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return self.LPick
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def getEpick(self):
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return self.EPick
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def getiplot(self):
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return self.iplot
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def setiplot(self, iplot):
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self.iplot = iplot
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def getpick1(self):
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return self.Pick1
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@ -133,6 +142,7 @@ class AutoPicking(object):
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def calcPick(self):
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self.Pick = None
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class AICPicker(AutoPicking):
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'''
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Method to derive onset time of arriving phase based on CF
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@ -144,23 +154,59 @@ class AICPicker(AutoPicking):
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print 'Get onset time (pick) from AIC-CF ...'
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self.Pick = None
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#find NaN's
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nn = np.isnan(self.cf)
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if len(nn) > 1:
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self.cf[nn] = 0
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#taper AIC-CF to get rid off side maxima
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tap = np.hanning(len(self.cf))
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aic = tap * self.cf + max(abs(self.cf))
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#smooth AIC-CF
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ismooth = int(round(self.Tsmooth / self.dt))
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aicsmooth = np.zeros(len(aic))
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if len(aic) < ismooth:
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print 'AICPicker: Tsmooth larger than CF!'
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return
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else:
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for i in range(1, len(aic)):
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if i > ismooth:
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ii1 = i - ismooth;
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aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
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else:
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aicsmooth[i] = np.mean(aic[1 : i])
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#remove offset
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offset = abs(min(aic) - min(aicsmooth))
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aicsmooth = aicsmooth - offset
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#get maximum of CF as starting point
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icfmax = np.argmax(aic)
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#find minimum in front of maximum
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
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if aic[i - 1] >= aic[i]:
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if aicsmooth[i - 1] >= aicsmooth[i]:
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self.Pick = self.Tcf[i]
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break
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if self.iplot is not None:
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plt.figure(self.iplot)
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x = self.Data[0].data
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p1, = plt.plot(self.Tcf, x / max(x), 'k')
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p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
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p3, = plt.plot([self.Pick, self.Pick], [-1 , 1], 'b', linewidth=2)
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plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.yticks([])
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plt.title(self.Data[0].stats.station)
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plt.show()
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raw_input()
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plt.close(self.iplot)
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if self.Pick == None:
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print 'AICPicker: Could not find minimum, picking window too short?'
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return self.Pick
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class PragPicker(AutoPicking):
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'''
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Method of pragmatic picking exploiting information given by CF.
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@ -169,7 +215,7 @@ class PragPicker(AutoPicking):
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def calcPick(self):
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if self.getpick1() is not None:
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print 'Get onset time (pick) from HOS- or AR-CF using pragmatic picking algorithm ...'
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print 'Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...'
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self.Pick = None
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#smooth CF
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@ -190,9 +236,9 @@ class PragPicker(AutoPicking):
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#which is centered around tpick1
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ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
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& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
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cfipick = self.cf[ipick]
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cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
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Tcfpick = self.Tcf[ipick]
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cfsmoothipick = cfsmooth[ipick]
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cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick])
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ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
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cfpick1 = 2 * self.cf[ipick1]
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@ -232,14 +278,255 @@ class PragPicker(AutoPicking):
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break
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#now decide which pick: left or right?
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if flagpick_l > 0 and flagpick_r > 0:
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if cfpick_l <= cfpick_r:
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self.Pick = pick_l
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else:
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self.Pick = pick_r
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if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
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self.Pick = pick_l
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elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
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self.Pick = pick_r
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if self.getiplot() is not None:
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plt.figure(self.getiplot())
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p1, = plt.plot(Tcfpick,cfipick, 'k')
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p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
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p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
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plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.yticks([])
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plt.title(self.Data[0].stats.station)
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plt.show()
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raw_input()
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plt.close(self.getiplot())
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else:
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self.Pick = None
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print 'PragPicker: No initial onset time given! Check input!'
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return
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class EarlLatePicker(AutoPicking):
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'''
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Method 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. Most likely pick (initial pick) must be given.
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'''
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def calcPick(self):
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self.LPick = None
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self.EPick = None
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if self.getpick1() is not None:
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print 'Get earliest and latest possible pick relative to most likely pick ...'
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ti = self.getpick1()
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x = self.Data
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t = self.Tcf
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#some parmaters needed:
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tnoise = self.TSNR[0] #noise window length for calculating noise level
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tsignal = self.TSNR[2] #signal window length
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tsafety = self.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((self.Tcf <= ti - tsafety) & (self.Tcf >= ti - tnoise - tsafety))
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#get signal window
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isignal = np.where((self.Tcf <= ti + tsignal) & (self.Tcf >= ti))
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#calculate noise level
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if len(x) == 1:
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nlevel = max(abs(x[0].data[inoise])) * self.nfac
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#get time where signal exceeds nlevel
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ilup = np.where(x[0].data[isignal] > nlevel)
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ildown = np.where(x[0].data[isignal] < -nlevel)
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if len(ilup[0]) <= 1 and len(ildown[0]) <= 1:
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print 'EarlLatePick: 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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self.LPick = t[isignal][il]
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elif len(x) == 2:
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nlevel = max(np.sqrt(np.power(x[0].data[inoise], 2) + np.power(x[1].data[inoise], 2)))
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#get earliest time where signal exceeds nlevel
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ilup1 = np.where(x[0].data[isignal] > nlevel)
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ilup2 = np.where(x[1].data[isignal] > nlevel)
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ildown1 = np.where(x[0].data[isignal] < -nlevel)
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ildown2 = np.where(x[1].data[isignal] < -nlevel)
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ilup = min([ilup1[0][0], ilup2[0][0]])
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ildown = min([ildown1[0][0], ildown2[0][0]])
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if np.size(ilup) < 1 and np.size(ildown) < 1:
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print 'EarlLatePick: Signal lower than noise level, misspick?'
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return
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il = min([ilup, ildown])
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self.LPick = t[isignal][il]
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elif len(x) == 3:
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nlevel = max(np.sqrt(np.power(x[0].data[inoise], 2) + np.power(x[1].data[inoise], 2) + \
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np.power(x[2].data[inoise], 2)))
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#get earliest time where signal exceeds nlevel
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ilup1 = np.where(x[0].data[isignal] > nlevel)
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ilup2 = np.where(x[1].data[isignal] > nlevel)
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ilup3 = np.where(x[2].data[isignal] > nlevel)
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ildown1 = np.where(x[0].data[isignal] < -nlevel)
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ildown2 = np.where(x[1].data[isignal] < -nlevel)
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ildown3 = np.where(x[2].data[isignal] < -nlevel)
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ilup = min([ilup1[0][0], ilup2[0][0], ilup3[0][0]])
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ildown = min([ildown1[0][0], ildown2[0][0], ildown3[0][0]])
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if np.size(ilup) < 1 and np.size(ildown) < 1:
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print 'EarlLatePick: Signal lower than noise level, misspick?'
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return
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il = min([ilup, ildown])
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self.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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#if there is one trace in stream
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if len(x) == 1:
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zc = []
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zc.append(ti)
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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[0].data[j-1] <= 0 and x[0].data[j] >= 0:
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zc.append(t[isignal][i])
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elif x[0].data[j-1] > 0 and x[0].data[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 of signal out of zero crossings
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Ts = max(np.diff(zc))
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#if there are two traces in stream
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#get maximum of two signal periods
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if len(x) == 2:
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zc1 = []
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zc2 = []
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zc1.append(ti)
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zc2.append(ti)
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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[0].data[j-1] <= 0 and x[0].data[j] >= 0:
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zc1.append(t[isignal][i])
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elif x[0].data[j-1] > 0 and x[0].data[j] <= 0:
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zc1.append(t[isignal][i])
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if x[1].data[j-1] <= 0 and x[1].data[j] >= 0:
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zc2.append(t[isignal][i])
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elif x[1].data[j-1] > 0 and x[1].data[j] <= 0:
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zc2.append(t[isignal][i])
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if len(zc1) >= 3 and len(zc2) >= 3:
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break
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Ts = max([max(np.diff(zc1)), max(np.diff(zc2))])
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#if there are three traces in stream
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#get maximum of three signal periods
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if len(x) == 3:
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zc1 = []
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zc2 = []
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zc3 = []
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zc1.append(ti)
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zc2.append(ti)
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zc3.append(ti)
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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[0].data[j-1] <= 0 and x[0].data[j] >= 0:
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zc1.append(t[isignal][i])
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elif x[0].data[j-1] > 0 and x[0].data[j] <= 0:
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zc1.append(t[isignal][i])
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if x[1].data[j-1] <= 0 and x[1].data[j] >= 0:
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zc2.append(t[isignal][i])
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elif x[1].data[j-1] > 0 and x[1].data[j] <= 0:
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zc2.append(t[isignal][i])
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if x[2].data[j-1] <= 0 and x[2].data[j] >= 0:
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zc3.append(t[isignal][i])
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elif x[2].data[j-1] > 0 and x[2].data[j] <= 0:
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zc3.append(t[isignal][i])
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if len(zc1) >= 3 and len(zc2) >= 3 and len(zc3) >= 3:
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break
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Ts = max([max(np.diff(zc1)), max(np.diff(zc2)), max(np.diff(zc3))])
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#Ts/4 is assumed as time difference between most likely and earliest possible pick!
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self.EPick = ti - Ts/4
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if self.iplot is not None:
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plt.figure(self.iplot)
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if len(x) == 1:
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p1, = plt.plot(t, x[0].data, 'k')
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p2, = plt.plot(t[inoise], x[0].data[inoise])
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p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
|
||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'])
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
|
||||
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
||||
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station)
|
||||
elif len(x) == 2:
|
||||
plt.subplot(2,1,1)
|
||||
p1, = plt.plot(t, x[0].data, 'k')
|
||||
p2, = plt.plot(t[inoise], x[0].data[inoise])
|
||||
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
|
||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'])
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
|
||||
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
||||
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
||||
plt.plot(zc1[0:3], [0, 0, 0], '*g')
|
||||
plt.yticks([])
|
||||
plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station)
|
||||
plt.subplot(2,1,2)
|
||||
plt.plot(t, x[1].data, 'k')
|
||||
plt.plot(t[inoise], x[1].data[inoise])
|
||||
plt.plot(t[isignal], x[1].data[isignal], 'r')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
|
||||
plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
||||
plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
||||
plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
elif len(x) == 3:
|
||||
plt.subplot(3,1,1)
|
||||
p1, = plt.plot(t, x[0].data, 'k')
|
||||
p2, = plt.plot(t[inoise], x[0].data[inoise])
|
||||
p3, = plt.plot(t[isignal], x[0].data[isignal], 'r')
|
||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
p5, = plt.plot(zc1[0:3], [0, 0, 0], '*g', markersize=14)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'])
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([ti, ti], [max(x[0].data), -max(x[0].data)], 'b', linewidth=2)
|
||||
plt.plot([self.LPick, self.LPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
||||
plt.plot([self.EPick, self.EPick], [max(x[0].data)/2, -max(x[0].data)/2], '--k')
|
||||
plt.yticks([])
|
||||
plt.title('Earliest-/Latest Possible and Most Likely Pick, %s' % self.Data[0].stats.station)
|
||||
plt.subplot(3,1,2)
|
||||
plt.plot(t, x[1].data, 'k')
|
||||
plt.plot(t[inoise], x[1].data[inoise])
|
||||
plt.plot(t[isignal], x[1].data[isignal], 'r')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([ti, ti], [max(x[1].data), -max(x[1].data)], 'b', linewidth=2)
|
||||
plt.plot([self.LPick, self.LPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
||||
plt.plot([self.EPick, self.EPick], [max(x[1].data)/2, -max(x[1].data)/2], '--k')
|
||||
plt.plot(zc2[0:3], [0, 0, 0], '*g', markersize=14)
|
||||
plt.yticks([])
|
||||
plt.subplot(3,1,3)
|
||||
plt.plot(t, x[2].data, 'k')
|
||||
plt.plot(t[inoise], x[2].data[inoise])
|
||||
plt.plot(t[isignal], x[2].data[isignal], 'r')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([ti, ti], [max(x[2].data), -max(x[2].data)], 'b', linewidth=2)
|
||||
plt.plot([self.LPick, self.LPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
|
||||
plt.plot([self.EPick, self.EPick], [max(x[2].data)/2, -max(x[2].data)/2], '--k')
|
||||
plt.plot(zc3[0:3], [0, 0, 0], '*g', markersize=14)
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(self.iplot)
|
||||
|
||||
elif self.getpick1() == None:
|
||||
print 'EarlLatePick: No initial onset time given! Check input!'
|
||||
return
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user