marcel/pylot
3
0
Fork 0
Code Issues 7 Pull Requests 1 Releases Wiki Activity

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

Browse Source
This commit is contained in:
Marcel Paffrath 2018-07-04 10:56:44 +02:00
commit 27e425844a
3 changed files with 54 additions and 40 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
autoPyLoT.py
View File

@ -346,6 +346,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
picks[stats]['P'].update(props) picks[stats]['P'].update(props)
evt = moment_mag.updated_event() evt = moment_mag.updated_event()
net_mw = moment_mag.net_magnitude() net_mw = moment_mag.net_magnitude()
if net_mw is not None:
print("Network moment magnitude: %4.1f" % net_mw.mag) print("Network moment magnitude: %4.1f" % net_mw.mag)
# calculate local (Richter) magntiude # calculate local (Richter) magntiude
WAscaling = parameter.get('WAscaling') WAscaling = parameter.get('WAscaling')
@ -363,6 +364,13 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
evt = local_mag.updated_event(magscaling) evt = local_mag.updated_event(magscaling)
net_ml = local_mag.net_magnitude(magscaling) net_ml = local_mag.net_magnitude(magscaling)
print("Network local magnitude: %4.1f" % net_ml.mag) print("Network local magnitude: %4.1f" % net_ml.mag)
if magscaling == None:
scaling = False
elif magscaling[0] != 0 and magscaling[1] != 0:
scaling = False
else:
scaling = True
if scaling:
print("Network local magnitude scaled with:") print("Network local magnitude scaled with:")
print("%f * Ml + %f" % (magscaling[0], magscaling[1])) print("%f * Ml + %f" % (magscaling[0], magscaling[1]))
else: else:
@ -421,6 +429,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
picks[stats]['P'].update(props) picks[stats]['P'].update(props)
evt = moment_mag.updated_event() evt = moment_mag.updated_event()
net_mw = moment_mag.net_magnitude() net_mw = moment_mag.net_magnitude()
if net_mw is not None:
print("Network moment magnitude: %4.1f" % net_mw.mag) print("Network moment magnitude: %4.1f" % net_mw.mag)
# calculate local (Richter) magntiude # calculate local (Richter) magntiude
WAscaling = parameter.get('WAscaling') WAscaling = parameter.get('WAscaling')

66
inputs/pylot_local.in
View File

@ -4,19 +4,19 @@
%Parameters are optimized for %extent data sets! %Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings# #main settings#
#rootpath# %project path /DATA/Insheim #rootpath# %project path
#datapath# %data path EVENT_DATA/LOCAL #datapath# %data path
#database# %name of data base 2018.02_Insheim #database# %name of data base
#eventID# %event ID for single event processing (* for all events found in database) e0006.038.18 #eventID# %event ID for single event processing (* for all events found in database)
#invdir# %full path to inventory or dataless-seed file /DATA/Insheim/STAT_INFO #invdir# %full path to inventory or dataless-seed file
PILOT #datastructure# %choose data structure PILOT #datastructure# %choose data structure
True #apverbose# %choose 'True' or 'False' for terminal output True #apverbose# %choose 'True' or 'False' for terminal output
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#NLLoc settings# #NLLoc settings#
None #nllocbin# %path to NLLoc executable /home/ludger/NLLOC #nllocbin# %path to NLLoc executable
None #nllocroot# %root of NLLoc-processing directory /home/ludger/NLLOC/Insheim #nllocroot# %root of NLLoc-processing directory
None #phasefile# %name of autoPyLoT-output phase file for NLLoc AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
None #ctrfile# %name of autoPyLoT-output control file for NLLoc Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
ttime #ttpatter# %pattern of NLLoc ttimes from grid ttime #ttpatter# %pattern of NLLoc ttimes from grid
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@ -27,31 +27,31 @@ AUTOLOC_nlloc #outpatter# %pattern of
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#settings local magnitude# #settings local magnitude#
1.11 0.0009 -2.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated! 1.11 0.0009 -2.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
1.0382 -0.447 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied! 0.0 0.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#filter settings# #filter settings#
1.0 1.0 #minfreq# %Lower filter frequency [P, S] 2.0 2.0 #minfreq# %Lower filter frequency [P, S]
10.0 10.0 #maxfreq# %Upper filter frequency [P, S] 30.0 15.0 #maxfreq# %Upper filter frequency [P, S]
2 2 #filter_order# %filter order [P, S] 3 3 #filter_order# %filter order [P, S]
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S] bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#common settings picker# #common settings picker#
local #extent# %extent of array ("local", "regional" or "global") local #extent# %extent of array ("local", "regional" or "global")
15.0 #pstart# %start time [s] for calculating CF for P-picking 7.0 #pstart# %start time [s] for calculating CF for P-picking
60.0 #pstop# %end time [s] for calculating CF for P-picking 16.0 #pstop# %end time [s] for calculating CF for P-picking
-1.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking -0.5 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
10.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking 10.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF False #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
iasp91 #taup_model# %define TauPy model for traveltime estimation iasp91 #taup_model# %define TauPy model for traveltime estimation
2.0 10.0 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz] 2.0 20.0 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
2.0 12.0 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz] 2.0 30.0 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
2.0 8.0 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz] 2.0 10.0 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
2.0 10.0 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz] 2.0 15.0 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
#special settings for calculating CF# #special settings for calculating CF#
%!!Edit the following only if you know what you are doing!!% %!!Edit the following only if you know what you are doing!!%
#Z-component# #Z-component#
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3) HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
7.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s] 4.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
4 #hosorder# %for HOS-picker, order of Higher Order Statistics 4 #hosorder# %for HOS-picker, order of Higher Order Statistics
2 #Parorder# %for AR-picker, order of AR process of Z-component 2 #Parorder# %for AR-picker, order of AR process of Z-component
1.2 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick 1.2 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
@ -59,12 +59,12 @@ HOS #algoP# %choose algo
0.6 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick 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 0.2 #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 0.001 #addnoise# %add noise to seismogram for stable AR prediction
3.0 0.1 0.5 1.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s] 3.0 0.0 1.0 0.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
3.0 #pickwinP# %for initial AIC pick, length of P-pick window [s] 3.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
6.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick) 6.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
0.2 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s] 0.4 #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] 0.1 #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) 0.4 #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) 1.3 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
#H-components# #H-components#
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3) ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
@ -75,7 +75,7 @@ ARH #algoS# %choose algo
4 #Sarorder# %for AR-picker, order of AR process of H-components 4 #Sarorder# %for AR-picker, order of AR process of H-components
5.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H) 5.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
4.0 #pickwinS# %for initial AIC pick, length of S-pick window [s] 4.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
2.0 0.3 1.5 1.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s] 2.0 0.2 1.5 1.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
1.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s] 1.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
0.7 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S) 0.7 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
0.9 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S) 0.9 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
@ -85,16 +85,16 @@ ARH #algoS# %choose algo
2.0 #minFMSNR# %miniumum required SNR for first-motion determination 2.0 #minFMSNR# %miniumum required SNR for first-motion determination
0.2 #fmpickwin# %pick window around P onset for calculating zero crossings 0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
#quality assessment# #quality assessment#
0.02 0.04 0.08 0.16 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P 0.04 0.08 0.16 0.32 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S 0.05 0.10 0.20 0.40 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
0.8 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected 0.8 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected 1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected 1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
1.5 #minAICSSNR# %below this SNR the initial S pick is rejected 1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
1.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s] 1.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
1.0 #noisefactor# %noiselevel*noisefactor=threshold 1.1 #noisefactor# %noiselevel*noisefactor=threshold
10.0 #minpercent# %required percentage of amplitudes exceeding threshold 50.0 #minpercent# %required percentage of amplitudes exceeding threshold
1.5 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude 1.1 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
6.0 #mdttolerance# %maximum allowed deviation of P picks from median [s] 5.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram 1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
5.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor 2.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor

9
pylot/core/pick/picker.py
View File

@ -240,12 +240,17 @@ class AICPicker(AutoPicker):
return return
# calculate SNR from CF # calculate SNR from CF
self.SNR = max(abs(self.Data[0].data[isignal])) / \ self.SNR = max(abs(self.Data[0].data[isignal])) / \
max(abs(self.Data[0].data[inoise] - np.mean(self.Data[0].data[inoise]))) abs(np.mean(self.Data[0].data[inoise]))
# calculate slope from CF after initial pick # calculate slope from CF after initial pick
# get slope window # get slope window
tslope = self.TSNR[3] # slope determination window tslope = self.TSNR[3] # slope determination window
islope = np.where((self.Tcf <= min([self.Pick + tslope, self.Tcf[-1]])) \ tsafety = self.TSNR[1] # safety gap, AIC is usually a little bit too late
if tsafety >= 0:
islope = np.where((self.Tcf <= min([self.Pick + tslope + tsafety, self.Tcf[-1]])) \
& (self.Tcf >= self.Pick)) # TODO: put this in a seperate function like getsignalwin & (self.Tcf >= self.Pick)) # TODO: put this in a seperate function like getsignalwin
else:
islope = np.where((self.Tcf <= min([self.Pick + tslope, self.Tcf[-1]])) \
& (self.Tcf >= self.Pick + tsafety)) # TODO: put this in a seperate function like getsignalwin
# find maximum within slope determination window # find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only! # 'cause slope should be calculated up to first local minimum only!
try: try: