Deleted:
This commit is contained in:
Ludger Küperkoch
parent
f6f1c9cf2f
commit
0494f2a11b
@ -1,133 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created Mar 2015
|
||||
Transcription of the rezipe of Diehl et al. (2009) for consistent phase
|
||||
picking. For a given inital (the most likely) pick, the corresponding earliest
|
||||
and latest possible pick is calculated based on noise measurements in front of
|
||||
the most likely pick and signal wavelength derived from zero crossings.
|
||||
|
||||
:author: Ludger Kueperkoch / MAGS2 EP3 working group
|
||||
"""
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from obspy.core import Stream
|
||||
import argparse
|
||||
|
||||
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
|
||||
'''
|
||||
Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
|
||||
as reasonable uncertainties. Latest possible pick is based on noise level,
|
||||
earliest possible pick is half a signal wavelength in front of most likely
|
||||
pick given by PragPicker or manually set by analyst. Most likely pick
|
||||
(initial pick Pick1) must be given.
|
||||
|
||||
:param: X, time series (seismogram)
|
||||
:type: `~obspy.core.stream.Stream`
|
||||
|
||||
:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
|
||||
:type: int
|
||||
|
||||
:param: TSNR, length of time windows around pick used to determine SNR [s]
|
||||
:type: tuple (T_noise, T_gap, T_signal)
|
||||
|
||||
:param: Pick1, initial (most likely) onset time, starting point for earllatepicker
|
||||
:type: float
|
||||
|
||||
:param: iplot, if given, results are plotted in figure(iplot)
|
||||
:type: int
|
||||
'''
|
||||
|
||||
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
|
||||
|
||||
LPick = None
|
||||
EPick = None
|
||||
PickError = None
|
||||
print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
|
||||
|
||||
x = X[0].data
|
||||
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate, X[0].stats.delta)
|
||||
#some parameters needed:
|
||||
tnoise = TSNR[0] #noise window length for calculating noise level
|
||||
tsignal = TSNR[2] #signal window length
|
||||
tsafety = TSNR[1] #safety gap between signal onset and noise window
|
||||
|
||||
#get latest possible pick
|
||||
#get noise window
|
||||
inoise = np.where((t <= max([Pick1 - tsafety, 0])) \
|
||||
& (t >= max([Pick1 - tnoise - tsafety, 0])))
|
||||
#get signal window
|
||||
isignal = np.where((t <= min([Pick1 + tsignal + tsafety, len(x)])) \
|
||||
& (t >= Pick1))
|
||||
#calculate noise level
|
||||
nlevel = max(abs(x[inoise])) * nfac
|
||||
#get time where signal exceeds nlevel
|
||||
ilup = np.where(x[isignal] > nlevel)
|
||||
ildown = np.where(x[isignal] < -nlevel)
|
||||
if len(ilup[0]) <= 1 and len(ildown[0]) <= 1:
|
||||
print 'earllatepicker: Signal lower than noise level, misspick?'
|
||||
return
|
||||
il = min([ilup[0][0], ildown[0][0]])
|
||||
LPick = t[isignal][il]
|
||||
|
||||
#get earliest possible pick
|
||||
#get next 2 zero crossings after most likely pick
|
||||
#initial onset is assumed to be the first zero crossing
|
||||
zc = []
|
||||
zc.append(Pick1)
|
||||
i = 0
|
||||
for j in range(isignal[0][1],isignal[0][len(t[isignal]) - 1]):
|
||||
i = i+ 1
|
||||
if x[j-1] <= 0 and x[j] >= 0:
|
||||
zc.append(t[isignal][i])
|
||||
elif x[j-1] > 0 and x[j] <= 0:
|
||||
zc.append(t[isignal][i])
|
||||
if len(zc) == 3:
|
||||
break
|
||||
#calculate maximum period T0 of signal out of zero crossings
|
||||
T0 = max(np.diff(zc)) #this is half wave length!
|
||||
#T0/4 is assumed as time difference between most likely and earliest possible pick!
|
||||
EPick = Pick1 - T0/2
|
||||
|
||||
#get symmetric pick error as mean from earliest and latest possible pick
|
||||
#by weighting latest possible pick two times earliest possible pick
|
||||
diffti_tl = LPick - Pick1
|
||||
diffti_te = Pick1 - EPick
|
||||
PickError = (diffti_te + 2 * diffti_tl) / 3
|
||||
|
||||
if iplot is not None:
|
||||
plt.figure(iplot)
|
||||
p1, = plt.plot(t, x, 'k')
|
||||
p2, = plt.plot(t[inoise], x[inoise])
|
||||
p3, = plt.plot(t[isignal], x[isignal], 'r')
|
||||
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
|
||||
p5, = plt.plot(zc, [0, 0, 0], '*g', markersize=14)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Noise Window', 'Signal Window', 'Noise Level', 'Zero Crossings'], \
|
||||
loc='best')
|
||||
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
|
||||
plt.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2)
|
||||
plt.plot([LPick, LPick], [max(x)/2, -max(x)/2], '--k')
|
||||
plt.plot([EPick, EPick], [max(x)/2, -max(x)/2], '--k')
|
||||
plt.plot([Pick1 + PickError, Pick1 + PickError], [max(x)/2, -max(x)/2], 'r--')
|
||||
plt.plot([Pick1 - PickError, Pick1 - PickError], [max(x)/2, -max(x)/2], 'r--')
|
||||
plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
ax = plt.gca()
|
||||
ax.set_xlim([t[inoise[0][0]] - 2, t[isignal[0][len(isignal) - 1]] + 3])
|
||||
plt.title('Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' % X[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(iplot)
|
||||
|
||||
return EPick, LPick, PickError
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--X', type=~obspy.core.stream.Stream, help='time series (seismogram) read with obspy module read')
|
||||
parser.add_argument('--nfac', type=int, help='(noise factor), nfac times noise level to calculate latest possible pick')
|
||||
parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
|
||||
[s] (Tnoise, Tgap, Tsignal)')
|
||||
parser.add_argument('--Pick1', 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.X, args.nfac, args.TSNR, args.Pick1, args.iplot)
|
||||
Loading…
Reference in New Issue
Block a user