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Merge branch 'develop' of 134.147.164.251:/data/git/pylot into develop

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Sebastian Wehling-Benatelli 2015-06-24 14:24:20 +02:00
commit 4548f361e4
5 changed files with 137 additions and 27 deletions
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18
autoPyLoT.py
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@ -13,6 +13,7 @@ from pylot.core.read import Data, AutoPickParameter
from pylot.core.pick.run_autopicking import run_autopicking
from pylot.core.util.structure import DATASTRUCTURE
from pylot.core.pick.utils import wadaticheck
import pdb
__version__ = _getVersionString()
@ -30,6 +31,18 @@ def autoPyLoT(inputfile):
.. rubric:: Example
'''
print '************************************'
print '*********autoPyLoT starting*********'
print 'The Python picking and Location Tool'
print ' Version ', _getVersionString(), '2015'
print '**Authors:'
print '**S. Wehling-Benatelli'
print '** Ruhr-University Bochum'
print '**L. Kueperkoch'
print '** BESTEC GmbH'
print '**K. Olbert'
print '** Christian-Albrechts University Kiel'
print '************************************'
# reading parameter file
@ -115,7 +128,6 @@ def autoPyLoT(inputfile):
station = wfdat[0].stats.station
allonsets = {station: picks}
for i in range(len(wfdat)):
#for i in range(0,5):
stationID = wfdat[i].stats.station
#check if station has already been processed
if stationID not in procstats:
@ -139,6 +151,10 @@ def autoPyLoT(inputfile):
print '-------Finished event %s!-------' % parameter.getParam('eventID')
print '------------------------------------------'
print '************************************'
print '*********autoPyLoT terminates*******'
print 'The Python picking and Location Tool'
print '************************************'
if __name__ == "__main__":
# parse arguments

1
autoPyLoT_local.in
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@ -1,6 +1,7 @@
%This is a parameter input file for autoPyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%Parameters are optimized for local data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#

33
autoPyLoT_regional.in
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@ -1,13 +1,14 @@
%This is a parameter input file for autoPyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%Parameters are optimized for regional data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/DATA/Egelados #rootpath# %project path
EVENT_DATA/LOCAL #datapath# %data path
2006.02_Nisyros #database# %name of data base
e0032.033.06 #eventID# %event ID for single event processing
2006.01_Nisyros #database# %name of data base
e1412.008.06 #eventID# %event ID for single event processing
PILOT #datastructure# %choose data structure
2 #iplot# %flag for plotting: 0 none, 1, partly, >1 everything
AUTOPHASES_AIC_HOS4_ARH #phasefile# %name of autoPILOT output phase file
@ -30,8 +31,8 @@ HYPOSAT #locrt# %location routine used ("HYPO
#common settings picker#
20 #pstart# %start time [s] for calculating CF for P-picking
160 #pstop# %end time [s] for calculating CF for P-picking
1.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
50 #sstop# %end time [s] after P-onset for calculating CF for S-picking
3.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
100 #sstop# %end time [s] after P-onset for calculating CF for S-picking
3 10 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
3 12 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
3 8 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
@ -48,13 +49,12 @@ HOS #algoP# %choose algorithm for P-onset
0.6 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
0.2 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
0.001 #addnoise# %add noise to seismogram for stable AR prediction
4 0.1 1.0 0.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
4 #pickwinP# %for initial AIC pick, length of P-pick window [s]
4 0.2 2.0 1.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
4 #pickwinP# %for initial AIC and refined pick, length of P-pick window [s]
8 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
0 #peps4aic# %for HOS/AR, artificial uplift of samples of AIC-function (P)
0.2 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
0.1 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
0.001 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
3.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
0.3 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
0.3 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
1.3 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
#H-components#
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
@ -63,18 +63,17 @@ ARH #algoS# %choose algorithm for S-onset
0.6 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
0.3 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
4 #Sarorder# %for AR-picker, order of AR process of H-components
20 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
5 #pickwinS# %for initial AIC pick, length of S-pick window [s]
6 0.2 2.0 1.5 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
0.05 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
0.02 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
0.2 #pepsS# %for AR-picker, artificial uplift of samples of CF (S)
10 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
6 #pickwinS# %for initial AIC and refined pick, length of S-pick window [s]
5 0.2 3.0 3.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
3.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
1.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
0.4 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
%first-motion picker%
1 #minfmweight# %minimum required p weight for first-motion determination
2 #minFMSNR# %miniumum required SNR for first-motion determination
5.0 #fmpickwin# %pick window around P onset for calculating zero crossings
6.0 #fmpickwin# %pick window around P onset for calculating zero crossings
%quality assessment%
#inital AIC onset#
0.04 0.08 0.16 0.32 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P

17
pylot/core/pick/run_autopicking.py
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@ -74,6 +74,10 @@ def run_autopicking(wfstream, pickparam):
minFMSNR = pickparam.getParam('minFMSNR')
fmpickwin = pickparam.getParam('fmpickwin')
minfmweight = pickparam.getParam('minfmweight')
# parameters for checking signal length
minsiglength = pickparam.getParam('minsiglength')
minpercent = pickparam.getParam('minpercent')
nfacsl = pickparam.getParam('noisefactor')
# initialize output
Pweight = 4 # weight for P onset
@ -94,6 +98,7 @@ def run_autopicking(wfstream, pickparam):
aicSflag = 0
aicPflag = 0
Pflag = 0
Sflag = 0
# split components
@ -152,9 +157,15 @@ def run_autopicking(wfstream, pickparam):
# of class AutoPicking
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
##############################################################
# check signal length to detect spuriously picked noise peaks
z_copy[0].data = tr_filt.data
Pflag = checksignallength(z_copy, aicpick.getpick(), tsnrz, minsiglength, \
nfacsl, minpercent, iplot)
##############################################################
# go on with processing if AIC onset passes quality control
if (aicpick.getSlope() >= minAICPslope and
aicpick.getSNR() >= minAICPSNR):
aicpick.getSNR() >= minAICPSNR and
Pflag == 1):
aicPflag = 1
print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
(aicpick.getSlope(), aicpick.getSNR())
@ -227,8 +238,8 @@ def run_autopicking(wfstream, pickparam):
Sflag = 0
else:
print 'run_autopicking: No vertical component data available, ' \
'skipping station!'
print 'run_autopicking: No vertical component data availabler!, ' \
'Skip station!'
if edat is not None and ndat is not None and len(edat) > 0 and len(
ndat) > 0 and Pweight < 4:

93
pylot/core/pick/utils.py
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@ -9,6 +9,7 @@
"""
import numpy as np
import scipy as sc
import matplotlib.pyplot as plt
from obspy.core import Stream, UTCDateTime
import warnings
@ -47,14 +48,11 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
x = X[0].data
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
X[0].stats.delta)
# get latest possible pick
# get noise window
inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, Pick1, TSNR[2])
# remove mean
meanwin = np.hstack((inoise, isignal))
x = x - np.mean(x[meanwin])
x = x - np.mean(x[inoise])
# calculate noise level
nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
# get time where signal exceeds nlevel
@ -337,7 +335,7 @@ def getSNR(X, TSNR, t1):
return
# demean over entire snr window
x -= x[inoise[0]:isignal[-1]].mean()
x = x - np.mean(x[np.hstack([inoise, isignal])])
# calculate ratios
noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
@ -514,3 +512,88 @@ def wadaticheck(pickdic, dttolerance, iplot):
plt.close(iplot)
return checkedonsets
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
'''
Function to detect spuriously picked noise peaks.
Uses envelope to determine, how many samples [per cent] after
P onset are below certain threshold, calculated from noise
level times noise factor.
: param: X, time series (seismogram)
: type: `~obspy.core.stream.Stream`
: param: pick, initial (AIC) P onset time
: type: float
: param: TSNR, length of time windows around initial pick [s]
: type: tuple (T_noise, T_gap, T_signal)
: param: minsiglength, minium required signal length [s] to
declare pick as P onset
: type: float
: param: nfac, noise factor (nfac * noise level = threshold)
: type: float
: param: minpercent, minimum required percentage of samples
above calculated threshold
: type: float
: param: iplot, if iplot > 1, results are shown in figure
: type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print 'Checking signal length ...'
x = X[0].data
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
X[0].stats.delta)
# generate envelope function from Hilbert transform
y = np.imag(sc.signal.hilbert(x))
e = np.sqrt(np.power(x, 2) + np.power(y, 2))
# get noise window
inoise = getnoisewin(t, pick, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, pick, TSNR[2])
# calculate minimum adjusted signal level
minsiglevel = max(e[inoise]) * nfac
# minimum adjusted number of samples over minimum signal level
minnum = len(isignal) * minpercent/100
# get number of samples above minimum adjusted signal level
numoverthr = len(np.where(e[isignal] >= minsiglevel)[0])
if numoverthr >= minnum:
print 'checksignallength: Signal reached required length.'
returnflag = 1
else:
print 'checksignallength: Signal shorter than required minimum signal length!'
print 'Presumably picked picked noise peak, pick is rejected!'
returnflag = 0
if iplot == 2:
plt.figure(iplot)
p1, = plt.plot(t,x, 'k')
p2, = plt.plot(t[inoise], e[inoise])
p3, = plt.plot(t[isignal],e[isignal], 'r')
p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
[minsiglevel, minsiglevel], 'g')
p5, = plt.plot([pick, pick], [min(x), max(x)], 'c')
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
'Envelope Signal Window', 'Minimum Signal Level', \
'Onset'], loc='best')
plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
plt.ylabel('Counts')
plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
plt.yticks([])
plt.show()
raw_input()
plt.close(iplot)
return returnflag