New function to derive automatically first motion (polarity) of phase onset based on zero crossings and slope determination.
This commit is contained in:
Ludger Küperkoch
parent
787cac7d68
commit
a606b030e2
183
pylot/core/pick/fmpicker.py
Executable file
183
pylot/core/pick/fmpicker.py
Executable file
@ -0,0 +1,183 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created Mar 2015
|
||||
Function to derive first motion (polarity) for given phase onset based on zero crossings.
|
||||
|
||||
:author: MAGS2 EP3 working group / Ludger Kueperkoch
|
||||
"""
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from obspy.core import Stream
|
||||
import argparse
|
||||
|
||||
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
|
||||
'''
|
||||
Function to derive first motion (polarity) of given phase onset Pick.
|
||||
Calculation is based on zero crossings determined within time window pickwin
|
||||
after given onset time.
|
||||
|
||||
:param: Xraw, unfiltered time series (seismogram)
|
||||
:type: `~obspy.core.stream.Stream`
|
||||
|
||||
:param: Xfilt, filtered time series (seismogram)
|
||||
:type: `~obspy.core.stream.Stream`
|
||||
|
||||
:param: pickwin, time window after onset Pick within zero crossings are calculated
|
||||
:type: float
|
||||
|
||||
:param: Pick, initial (most likely) onset time, starting point for fmpicker
|
||||
:type: float
|
||||
|
||||
:param: iplot, if given, results are plotted in figure(iplot)
|
||||
:type: int
|
||||
'''
|
||||
|
||||
assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
|
||||
assert isinstance(Xfilt, Stream), "%s is not a stream object" % str(Xfilt)
|
||||
|
||||
FM = None
|
||||
if Pick is not None:
|
||||
print 'fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...'
|
||||
|
||||
xraw = Xraw[0].data
|
||||
xfilt = Xfilt[0].data
|
||||
t = np.arange(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate, Xraw[0].stats.delta)
|
||||
#get pick window
|
||||
ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
|
||||
#remove mean
|
||||
xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
|
||||
xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
|
||||
|
||||
#get next zero crossing after most likely pick
|
||||
#initial onset is assumed to be the first zero crossing
|
||||
#first from unfiltered trace
|
||||
zc1 = []
|
||||
zc1.append(Pick)
|
||||
index1 = []
|
||||
i = 0
|
||||
for j in range(ipick[0][1],ipick[0][len(t[ipick]) - 1]):
|
||||
i = i+ 1
|
||||
if xraw[j-1] <= 0 and xraw[j] >= 0:
|
||||
zc1.append(t[ipick][i])
|
||||
index1.append(i)
|
||||
elif xraw[j-1] > 0 and xraw[j] <= 0:
|
||||
zc1.append(t[ipick][i])
|
||||
index1.append(i)
|
||||
if len(zc1) == 3:
|
||||
break
|
||||
|
||||
#if time difference betweeen 1st and 2cnd zero crossing
|
||||
#is too short, get time difference between 1st and 3rd
|
||||
#to derive maximum
|
||||
if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
|
||||
li1 = index1[1]
|
||||
else:
|
||||
li1 = index1[0]
|
||||
if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
|
||||
print 'earllatepicker: Onset on unfiltered trace too emergent for first motion determination!'
|
||||
P1 = None
|
||||
else:
|
||||
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
|
||||
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
|
||||
#calculate slope as polynomal fit of order 1
|
||||
xslope1 = np.arange(0, len(xraw[islope1]), 1)
|
||||
P1 = np.polyfit(xslope1, xraw[islope1], 1)
|
||||
datafit1 = np.polyval(P1, xslope1)
|
||||
|
||||
#now using filterd trace
|
||||
#next zero crossing after most likely pick
|
||||
zc2 = []
|
||||
zc2.append(Pick)
|
||||
index2 = []
|
||||
i = 0
|
||||
for j in range(ipick[0][1],ipick[0][len(t[ipick]) - 1]):
|
||||
i = i+ 1
|
||||
if xfilt[j-1] <= 0 and xfilt[j] >= 0:
|
||||
zc2.append(t[ipick][i])
|
||||
index2.append(i)
|
||||
elif xfilt[j-1] > 0 and xfilt[j] <= 0:
|
||||
zc2.append(t[ipick][i])
|
||||
index2.append(i)
|
||||
if len(zc2) == 3:
|
||||
break
|
||||
|
||||
#if time difference betweeen 1st and 2cnd zero crossing
|
||||
#is too short, get time difference between 1st and 3rd
|
||||
#to derive maximum
|
||||
if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
|
||||
li2 = index2[1]
|
||||
else:
|
||||
li2 = index2[0]
|
||||
if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
|
||||
print 'earllatepicker: Onset on filtered trace too emergent for first motion determination!'
|
||||
P2 = None
|
||||
else:
|
||||
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
|
||||
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
|
||||
#calculate slope as polynomal fit of order 1
|
||||
xslope2 = np.arange(0, len(xfilt[islope2]), 1)
|
||||
P2 = np.polyfit(xslope2, xfilt[islope2], 1)
|
||||
datafit2 = np.polyval(P2, xslope2)
|
||||
|
||||
#compare results
|
||||
if P1 is not None and P2 is not None:
|
||||
if P1[0] < 0 and P2[0] < 0:
|
||||
FM = 'D'
|
||||
elif P1[0] >= 0 and P2[0] < 0:
|
||||
FM = '-'
|
||||
elif P1[0] < 0 and P2[0]>= 0:
|
||||
FM = '-'
|
||||
elif P1[0] > 0 and P2[0] > 0:
|
||||
FM = 'U'
|
||||
elif P1[0] <= 0 and P2[0] > 0:
|
||||
FM = '+'
|
||||
elif P1[0] > 0 and P2[0] <= 0:
|
||||
FM = '+'
|
||||
|
||||
if iplot is not None:
|
||||
plt.figure(iplot)
|
||||
plt.subplot(2,1,1)
|
||||
plt.plot(t, xraw, 'k')
|
||||
p1, = plt.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2)
|
||||
if P1 is not None:
|
||||
p2, = plt.plot(t[islope1], xraw[islope1])
|
||||
p3, = plt.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14)
|
||||
p4, = plt.plot(t[islope1], datafit1, '--g', linewidth=2)
|
||||
plt.legend([p1, p2, p3, p4], ['Pick', 'Slope Window', 'Zero Crossings', 'Slope'], \
|
||||
loc='best')
|
||||
plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
|
||||
ax = plt.gca()
|
||||
ax.set_xlim([t[islope1[0][0]] - 0.1, t[islope1[0][len(islope1) - 1]] + 0.3])
|
||||
plt.yticks([])
|
||||
plt.title('First-Motion Determination, %s, Unfiltered Data' % Xraw[0].stats.station)
|
||||
|
||||
plt.subplot(2,1,2)
|
||||
plt.title('First-Motion Determination, Filtered Data')
|
||||
plt.plot(t, xfilt, 'k')
|
||||
p1, = plt.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b', linewidth=2)
|
||||
if P2 is not None:
|
||||
p2, = plt.plot(t[islope2], xfilt[islope2])
|
||||
p3, = plt.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
|
||||
p4, = plt.plot(t[islope2], datafit2, '--g', linewidth=2)
|
||||
plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
|
||||
ax = plt.gca()
|
||||
ax.set_xlim([t[islope2[0][0]] - 0.1, t[islope2[0][len(islope2) - 1]] + 0.3])
|
||||
plt.xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(iplot)
|
||||
|
||||
return FM
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--Xraw', type=~obspy.core.stream.Stream, help='unfiltered time series (seismogram) read with obspy module read')
|
||||
parser.add_argument('--Xfilt', type=~obspy.core.stream.Stream, help='filtered time series (seismogram) read with obspy module read')
|
||||
parser.add_argument('--pickwin', type=float, help='length of pick window [s] for first motion determination')
|
||||
parser.add_argument('--Pick', type=float, help='Onset time of most likely pick')
|
||||
parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
|
||||
args = parser.parse_args()
|
||||
earllatepicker(args.Xraw, args.Xfilt, args.pickwin, args.Pick, args.iplot)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user