Implemented pragmatic picking algorithm developed by TM, JL, and LK
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Ludger Küperkoch
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commit
59930c3238
@ -3,16 +3,16 @@
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Created Dec 2014
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Implementation of the picking algorithms published and described in:
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Küperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
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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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using higher order statistics, Geophys. J. Int., 181, 1159-1170
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Küperkoch, L., Meier, T., Brüstle, A., Lee, J., Friederich, W., & Egelados
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Kueperkoch, L., Meier, T., Bruestle, A., Lee, J., Friederich, W., & Egelados
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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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:author: MAGS2 EP3 working group / Ludger Küperkoch
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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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@ -23,7 +23,7 @@ class AutoPicking(object):
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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, peps=None, Tsmooth=None):
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def __init__(self, cf, Tslope, aerr, TSNR, PickWindow, 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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@ -41,11 +41,14 @@ 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: peps, find local minimum at i if aic(i-1)*(1+peps) >= aic(i)
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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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:type: float
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'''
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#assert isinstance(cf, CharFuns), "%s is not a CharacteristicFunction object" % str(cf)
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@ -58,8 +61,9 @@ class AutoPicking(object):
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self.setaerr(aerr)
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self.setTSNR(TSNR)
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self.setPickWindow(PickWindow)
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self.setpeps(peps)
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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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self.calcPick()
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def __str__(self):
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@ -68,15 +72,17 @@ class AutoPicking(object):
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aerr:\t{aerr}\n
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TSNR:\t\t\t{TSNR}\n
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PickWindow:\t{PickWindow}\n
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peps:\t{peps}\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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TSNR=self.getTSNR(),
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PickWindow=self.getPickWindow(),
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peps=self.getpeps(),
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Tsmooth=self.getTsmooth())
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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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@ -102,11 +108,11 @@ class AutoPicking(object):
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def setPickWindow(self, PickWindow):
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self.PickWindow = PickWindow
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def getpeps(self):
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return self.peps
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def getaus(self):
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return self.aus
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def setpeps(self, peps):
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self.peps = peps
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def setaus(self, aus):
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self.aus = aus
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def setTsmooth(self, Tsmooth):
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self.Tsmooth = Tsmooth
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@ -117,6 +123,12 @@ class AutoPicking(object):
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def getpick(self):
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return self.Pick
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def getpick1(self):
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return self.Pick1
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def setpick1(self, Pick1):
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self.Pick1 = Pick1
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def calcPick(self):
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self.Pick = None
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@ -128,7 +140,7 @@ class AICPicker(AutoPicking):
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def calcPick(self):
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print 'Get onset (pick) from AIC-CF ...'
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print 'Get onset time (pick) from AIC-CF ...'
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self.Pick = -1
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#taper AIC-CF to get rid off side maxima
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@ -155,4 +167,78 @@ class PragPicker(AutoPicking):
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def calcPick(self):
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print 'Get onset (pick) from HOS- or AR-CF using pragmatic picking algorithm ...'
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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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self.Pick = -1
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#smooth CF
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ismooth = round(self.Tsmooth / self.dt);
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cfsmooth = np.zeros(len(self.cf))
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if len(self.cf) < ismooth:
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print 'PragPicker: Tsmooth larger than CF!'
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return
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else:
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for i in range(1, len(self.cf)):
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if i > ismooth:
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ii1 = i - ismooth;
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cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
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else:
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cfsmooth[i] = np.mean(self.cf[1 : i])
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#select picking window
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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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Tcfpick = self.Tcf[ipick]
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cfsmoothipick = cfsmooth[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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#check trend of CF, i.e. differences of CF and adjust aus regarding this trend
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#prominent trend: decrease aus
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#flat: use given aus
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cfdiff = np.diff(cfipick);
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i0diff = np.where(cfdiff > 0)
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cfdiff = cfdiff[i0diff]
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minaus = min(cfdiff * (1 + self.aus));
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aus1 = max([minaus, self.aus]);
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#at first we look to the right until the end of the pick window is reached
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flagpick_r = 0
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flagpick_l = 0
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flagpick = 0
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
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if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
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if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
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if cfpick1 >= self.cf[i]:
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pick_r = self.Tcf[i]
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self.Pick = pick_r
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flagpick_l = 1
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cfpick_r = self.cf[i]
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break
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#now we look to the left
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for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
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if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
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if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
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if cfpick1 >= self.cf[i]:
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pick_l = self.Tcf[i]
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self.Pick = pick_l
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flagpick_r = 1
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cfpick_l = self.cf[i]
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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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else:
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self.Pick = -1
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print 'PragPicker: No initial onset time given! Check input!'
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return
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