[add] check second maximum of HOS/AR-CF
Use the second maximum if it is larger than first maximum * minfactor. This helps when two onsets are close together (eg. S and SKS at 90°) and the second onset is stronger.
This commit is contained in:
Darius Arnold
parent
cd83c99034
commit
2db6fde709
@ -273,6 +273,26 @@ defaults = {'rootpath': {'type': str,
|
||||
'value': 1.5,
|
||||
'namestring': 'Noise factor S'},
|
||||
|
||||
'checkwindowP': {'type': float,
|
||||
'tooltip': 'time window before HOS/AR-maximum to check for smaller maxima [s]',
|
||||
'value': 10.0,
|
||||
'namestring': 'Check Window P'},
|
||||
|
||||
'minfactorP': {'type': float,
|
||||
'tooltip': 'Second maximum must be at least minfactor * first maximum [-]',
|
||||
'value': 0.7,
|
||||
'namestring': 'Minimum Factor P'},
|
||||
|
||||
'checkwindowS': {'type': float,
|
||||
'tooltip': 'time window before AR-maximum to check for smaller maxima [s]',
|
||||
'value': 10.0,
|
||||
'namestring': 'Check Window S'},
|
||||
|
||||
'minfactorS': {'type': float,
|
||||
'tooltip': 'Second maximum must be at least minfactor * first maximum [-]',
|
||||
'value': 0.7,
|
||||
'namestring': 'Minimum Factor S'},
|
||||
|
||||
'minfmweight': {'type': int,
|
||||
'tooltip': 'minimum required P weight for first-motion determination',
|
||||
'value': 1,
|
||||
@ -450,7 +470,9 @@ settings_special_pick = {
|
||||
'aictsmooth',
|
||||
'tsmoothP',
|
||||
'ausP',
|
||||
'nfacP'],
|
||||
'nfacP',
|
||||
'checkwindowP',
|
||||
'minfactorP'],
|
||||
'h': [
|
||||
'algoS',
|
||||
'tdet1h',
|
||||
@ -464,7 +486,9 @@ settings_special_pick = {
|
||||
'aictsmoothS',
|
||||
'tsmoothS',
|
||||
'ausS',
|
||||
'nfacS'],
|
||||
'nfacS',
|
||||
'checkwindowS',
|
||||
'minfactorS'],
|
||||
'fm': [
|
||||
'minfmweight',
|
||||
'minFMSNR',
|
||||
|
||||
@ -175,6 +175,10 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
# parameter to check for spuriously picked S onset
|
||||
zfac = pickparam.get('zfac')
|
||||
# path to inventory-, dataless- or resp-files
|
||||
checkwindowP = pickparam.get('checkwindowP')
|
||||
minfactorP = pickparam.get('minfactorP')
|
||||
checkwindowS = pickparam.get('checkwindowS')
|
||||
minfactorS = pickparam.get('minfactorS')
|
||||
|
||||
# initialize output
|
||||
Pweight = 4 # weight for P onset
|
||||
@ -322,7 +326,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
fig = fig_dict[key]
|
||||
else:
|
||||
fig = None
|
||||
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP, fig=fig)
|
||||
aicpick = AICPicker(aiccf, tsnrz, pickwinP, checkwindow=checkwindowP, minfactor=minfactorP,
|
||||
iplot=iplot, Tsmooth=tsmoothP, fig=fig)
|
||||
# add pstart and pstop to aic plot
|
||||
if fig:
|
||||
for ax in fig.axes:
|
||||
@ -442,8 +447,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
fig = fig_dict['refPpick']
|
||||
else:
|
||||
fig = None
|
||||
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
|
||||
aicpick.getpick(), fig)
|
||||
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot=iplot, aus=ausP, Tsmooth=tsmoothP,
|
||||
Pick1 = aicpick.getpick(), fig=fig)
|
||||
mpickP = refPpick.getpick()
|
||||
#############################################################
|
||||
if mpickP is not None:
|
||||
@ -624,8 +629,9 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
fig = fig_dict['aicARHfig']
|
||||
else:
|
||||
fig = None
|
||||
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
|
||||
aictsmoothS, fig=fig)
|
||||
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, checkwindow=checkwindowS,
|
||||
minfactor=minfactorS, iplot=iplot, Tsmooth=aictsmoothS,
|
||||
fig=fig)
|
||||
###############################################################
|
||||
# go on with processing if AIC onset passes quality control
|
||||
slope = aicarhpick.getSlope()
|
||||
@ -686,8 +692,8 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
fig = fig_dict['refSpick']
|
||||
else:
|
||||
fig = None
|
||||
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
|
||||
tsmoothS, aicarhpick.getpick(), fig)
|
||||
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot=iplot, aus=ausS,
|
||||
Tsmooth=tsmoothS, Pick1=aicarhpick.getpick(), fig=fig)
|
||||
mpickS = refSpick.getpick()
|
||||
#############################################################
|
||||
if mpickS is not None:
|
||||
|
||||
@ -24,7 +24,7 @@ import warnings
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from pylot.core.pick.charfuns import CharacteristicFunction
|
||||
from pylot.core.pick.utils import getnoisewin, getsignalwin
|
||||
from pylot.core.pick.utils import getnoisewin, getsignalwin, get_maximum_index
|
||||
|
||||
|
||||
class AutoPicker(object):
|
||||
@ -35,7 +35,7 @@ class AutoPicker(object):
|
||||
|
||||
warnings.simplefilter('ignore')
|
||||
|
||||
def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None):
|
||||
def __init__(self, cf, TSNR, PickWindow, checkwindow=None, minfactor=None, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None):
|
||||
'''
|
||||
:param: cf, characteristic function, on which the picking algorithm is applied
|
||||
:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
|
||||
@ -74,6 +74,8 @@ class AutoPicker(object):
|
||||
self.setTsmooth(Tsmooth)
|
||||
self.setpick1(Pick1)
|
||||
self.fig = fig
|
||||
self.setCheckWindow(checkwindow)
|
||||
self.minfactor = minfactor
|
||||
self.calcPick()
|
||||
|
||||
def __str__(self):
|
||||
@ -89,6 +91,10 @@ class AutoPicker(object):
|
||||
aus=self.getaus(),
|
||||
Tsmooth=self.getTsmooth(),
|
||||
Pick1=self.getpick1())
|
||||
def setCheckWindow(self, checkwindow):
|
||||
'''convert checkwindow to samples'''
|
||||
if checkwindow:
|
||||
self.checkwindow = int(checkwindow / self.Data[0].stats.delta)
|
||||
|
||||
def getTSNR(self):
|
||||
return self.TSNR
|
||||
@ -187,8 +193,8 @@ class AICPicker(AutoPicker):
|
||||
offset = abs(min(aic) - min(aicsmooth))
|
||||
aicsmooth = aicsmooth - offset
|
||||
# get maximum of HOS/AR-CF as startimg point for searching
|
||||
# minimum in AIC function
|
||||
icfmax = np.argmax(self.Data[0].data)
|
||||
# minimum in AIC function
|
||||
icfmax = get_maximum_index(self.Data[0].data, self.checkwindow, self.minfactor)
|
||||
|
||||
# find minimum in AIC-CF front of maximum of HOS/AR-CF
|
||||
lpickwindow = int(round(self.PickWindow / self.dt))
|
||||
|
||||
@ -13,6 +13,7 @@ import warnings
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from obspy.core import Stream, UTCDateTime
|
||||
from scipy.signal import argrelextrema
|
||||
|
||||
|
||||
def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
|
||||
@ -1171,6 +1172,29 @@ def removePicksAbove(pickDic, minWeight):
|
||||
newdic[eventKey][station].update({phasename: phaseinfo})
|
||||
return newdic
|
||||
|
||||
|
||||
def get_maximum_index(data, checkwindow, minfactor):
|
||||
'''get maximum of CF as starting point, then check for highest local maximum
|
||||
in front of it.
|
||||
return second maximum if its larger than first maximum * minfactor, else
|
||||
return first maximum'''
|
||||
icfmax1 = np.argmax(data)
|
||||
imax_local = argrelextrema(data[icfmax1 - checkwindow:icfmax1], np.greater) # indices of local maxima
|
||||
if (len(imax_local[0]) > 0):
|
||||
imax_local = imax_local[0] + icfmax1 - checkwindow
|
||||
local_maxima = (imax_local, data[imax_local])
|
||||
icfmax2 = local_maxima[0][np.where(local_maxima[1] == max(local_maxima[1]))][0]
|
||||
if data[icfmax2] > data[icfmax1] * minfactor:
|
||||
print("Found valid local maximum in front of first maximum")
|
||||
return icfmax2
|
||||
else:
|
||||
print("First maximum is the largest: {}>{}".format(data[icfmax1],
|
||||
data[icfmax2]))
|
||||
return icfmax1
|
||||
else:
|
||||
print("No local maxima found in check window")
|
||||
return icfmax1
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user