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[add] for global seismology CF pick windows will now be calculated

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relative to estimated tt from TauPy, metadata and source location (in XML
file) needed
[TO DO]: automatic export of XML (esp. source loc) before autopicking
This commit is contained in:
Marcel Paffrath
2017-08-07 11:21:20 +02:00
parent 60ebaa6528
commit 17a93cf62f
7 changed files with 169 additions and 87 deletions
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72
pylot/core/pick/autopick.py
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@@ -18,10 +18,13 @@ from pylot.core.pick.charfuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
from pylot.core.pick.picker import AICPicker, PragPicker
from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker, \
getSNR, fmpicker, checkPonsets, wadaticheck
from pylot.core.util.utils import getPatternLine, gen_Pool
from pylot.core.util.utils import getPatternLine, gen_Pool, identifyPhase, loopIdentifyPhase, \
full_range
from obspy.taup import TauPyModel
def autopickevent(data, param, iplot=0, fig_dict=None, ncores=0):
def autopickevent(data, param, iplot=0, fig_dict=None, ncores=0, metadata=None, origin=None):
stations = []
all_onsets = {}
input_tuples = []
@@ -42,9 +45,11 @@ def autopickevent(data, param, iplot=0, fig_dict=None, ncores=0):
topick = data.select(station=station)
if not iplot:
input_tuples.append((topick, param, apverbose))
input_tuples.append((topick, param, apverbose, metadata, origin))
if iplot > 0:
all_onsets[station] = autopickstation(topick, param, verbose=apverbose, iplot=iplot, fig_dict=fig_dict)
all_onsets[station] = autopickstation(topick, param, verbose=apverbose,
iplot=iplot, fig_dict=fig_dict,
metadata=metadata, origin=origin)
if iplot > 0:
print('iPlot Flag active: NO MULTIPROCESSING possible.')
@@ -69,12 +74,13 @@ def autopickevent(data, param, iplot=0, fig_dict=None, ncores=0):
def call_autopickstation(input_tuple):
wfstream, pickparam, verbose = input_tuple
wfstream, pickparam, verbose, metadata, origin = input_tuple
# multiprocessing not possible with interactive plotting
return autopickstation(wfstream, pickparam, verbose, iplot=0)
return autopickstation(wfstream, pickparam, verbose, iplot=0, metadata=metadata, origin=origin)
def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
def autopickstation(wfstream, pickparam, verbose=False,
iplot=0, fig_dict=None, metadata=None, origin=None):
"""
:param wfstream: `~obspy.core.stream.Stream` containing waveform
:type wfstream: obspy.core.stream.Stream
@@ -117,6 +123,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
algoS = pickparam.get('algoS')
sstart = pickparam.get('sstart')
sstop = pickparam.get('sstop')
use_taup = pickparam.get('use_taup')
taup_model = pickparam.get('taup_model')
bph1 = pickparam.get('bph1')
bph2 = pickparam.get('bph2')
tsnrh = pickparam.get('tsnrh')
@@ -182,6 +190,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
if len(ndat) == 0: # check for other components
ndat = wfstream.select(component="1")
wfstart, wfend = full_range(wfstream)
if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
msg = '##################################################\nautopickstation:' \
' Working on P onset of station {station}\nFiltering vertical ' \
@@ -197,7 +207,47 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
z_copy[0].data = tr_filt.data
##############################################################
# check length of waveform and compare with cut times
Lc = pstop - pstart
# for global seismology: use tau-p method for estimating travel times (needs source and station coords.)
# if not given: sets Lc to infinity to use full stream
if use_taup:
Lc = np.inf
print('autopickstation: use_taup flag active.')
if not metadata[1]:
print('Warning: Could not use TauPy to estimate onsets as there are no metadata given.')
else:
if origin:
source_origin = origin[0]
station_id = wfstream[0].get_id()
parser = metadata[1]
station_coords = parser.get_coordinates(station_id)
model = TauPyModel(taup_model)
arrivals = model.get_travel_times_geo(
source_origin.depth,
source_origin.latitude,
source_origin.longitude,
station_coords['latitude'],
station_coords['longitude']
)
phases = {'P': [],
'S': []}
for arr in arrivals:
phases[identifyPhase(loopIdentifyPhase(arr.phase.name))].append(arr)
# get first P and S onsets from arrivals list
arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key = lambda t: t[1])
arrS, estFirstS = min([(arr, arr.time) for arr in phases['S']], key = lambda t: t[1])
print('autopick: estimated first arrivals for P: {}, S:{} using TauPy'.format(estFirstP, estFirstS))
# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
pstart += (source_origin.time + estFirstP) - wfstart
pstop += (source_origin.time + estFirstP) - wfstart
Lc = pstop - pstart
else:
print('No source origins given!')
else:
Lc = pstop - pstart
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
Ldiff = Lwf - Lc
if Ldiff < 0:
@@ -238,6 +288,12 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
else:
fig = None
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP, fig=fig)
# add pstart and pstop to aic plot
if fig.axes:
for ax in fig.axes:
ax.vlines(pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
ax.vlines(pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
ax.legend()
##############################################################
if aicpick.getpick() is not None:
# check signal length to detect spuriously picked noise peaks