def _on_file_save(self):
"""
Creates a new obspy.core.event.Magnitude object and writes the moment
magnitude to it.
"""
# Get the save filename.
filename = QtGui.QFileDialog.getSaveFileName(caption="Save as...")
filename = os.path.abspath(str(filename))
mag = Magnitude()
mag.mag = self.final_result["moment_magnitude"]
mag.magnitude_type = "Mw"
mag.station_count = self.final_result["station_count"]
mag.evaluation_mode = "manual"
# Link to the used origin.
mag.origin_id = self.current_state["event"].origins[0].resource_id
mag.method_id = "Magnitude picker Krischer"
# XXX: Potentially change once this program gets more stable.
mag.evaluation_status = "preliminary"
# Write the other results as Comments.
mag.comments.append( \
Comment("Seismic moment in Nm: %g" % \
self.final_result["seismic_moment"]))
mag.comments.append( \
Comment("Circular source radius in m: %.2f" % \
self.final_result["source_radius"]))
mag.comments.append( \
Comment("Stress drop in Pa: %.2f" % \
self.final_result["stress_drop"]))
mag.comments.append( \
Comment("Very rough Q estimation: %.1f" % \
self.final_result["quality_factor"]))
event = copy.deepcopy(self.current_state["event"])
event.magnitudes.append(mag)
cat = Catalog()
cat.events.append(event)
cat.write(filename, format="quakeml")
def _map_netmag2magnitude(self, db):
"""
Return an obspy Magnitude from an dict of CSS key/values
corresponding to one record.
Inputs
======
db : dict of key/values of CSS fields from the 'netmag' table
Returns
=======
obspy.core.event.Magnitude
Notes
=====
Any object that supports the dict 'get' method can be passed as
input, e.g. OrderedDict, custom classes, etc.
"""
m = Magnitude()
m.mag = db.get('magnitude')
m.magnitude_type = db.get('magtype')
m.mag_errors.uncertainty = db.get('uncertainty')
m.station_count = db.get('nsta')
posted_author = _str(db.get('auth'))
mode, status = self.get_event_status(posted_author)
m.evaluation_mode = mode
m.evaluation_status = status
m.creation_info = CreationInfo(
creation_time = _utc(db.get('lddate')),
agency_id = self.agency,
version = db.get('magid'),
author = posted_author,
)
m.resource_id = self._rid(m)
return m
def _on_file_save(self):
"""
Creates a new obspy.core.event.Magnitude object and writes the moment
magnitude to it.
"""
# Get the save filename.
filename = QtGui.QFileDialog.getSaveFileName(caption="Save as...")
filename = os.path.abspath(str(filename))
mag = Magnitude()
mag.mag = self.final_result["moment_magnitude"]
mag.magnitude_type = "Mw"
mag.station_count = self.final_result["station_count"]
mag.evaluation_mode = "manual"
# Link to the used origin.
mag.origin_id = self.current_state["event"].origins[0].resource_id
mag.method_id = "Magnitude picker Krischer"
# XXX: Potentially change once this program gets more stable.
mag.evaluation_status = "preliminary"
# Write the other results as Comments.
mag.comments.append( \
Comment("Seismic moment in Nm: %g" % \
self.final_result["seismic_moment"]))
mag.comments.append( \
Comment("Circular source radius in m: %.2f" % \
self.final_result["source_radius"]))
mag.comments.append( \
Comment("Stress drop in Pa: %.2f" % \
self.final_result["stress_drop"]))
mag.comments.append( \
Comment("Very rough Q estimation: %.1f" % \
self.final_result["quality_factor"]))
event = copy.deepcopy(self.current_state["event"])
event.magnitudes.append(mag)
cat = Catalog()
cat.events.append(event)
cat.write(filename, format="quakeml")
def test_creating_minimal_quakeml_with_mt(self):
"""
Tests the creation of a minimal QuakeML containing origin, magnitude
and moment tensor.
"""
# Rotate into physical domain
lat, lon, depth, org_time = 10.0, -20.0, 12000, UTCDateTime(2012, 1, 1)
mrr, mtt, mpp, mtr, mpr, mtp = 1E18, 2E18, 3E18, 3E18, 2E18, 1E18
scalar_moment = math.sqrt(
mrr ** 2 + mtt ** 2 + mpp ** 2 + mtr ** 2 + mpr ** 2 + mtp ** 2)
moment_magnitude = 0.667 * (math.log10(scalar_moment) - 9.1)
# Initialise event
ev = Event(event_type="earthquake")
ev_origin = Origin(time=org_time, latitude=lat, longitude=lon,
depth=depth, resource_id=ResourceIdentifier())
ev.origins.append(ev_origin)
# populate event moment tensor
ev_tensor = Tensor(m_rr=mrr, m_tt=mtt, m_pp=mpp, m_rt=mtr, m_rp=mpr,
m_tp=mtp)
ev_momenttensor = MomentTensor(tensor=ev_tensor)
ev_momenttensor.scalar_moment = scalar_moment
ev_momenttensor.derived_origin_id = ev_origin.resource_id
ev_focalmechanism = FocalMechanism(moment_tensor=ev_momenttensor)
ev.focal_mechanisms.append(ev_focalmechanism)
# populate event magnitude
ev_magnitude = Magnitude()
ev_magnitude.mag = moment_magnitude
ev_magnitude.magnitude_type = 'Mw'
ev_magnitude.evaluation_mode = 'automatic'
ev.magnitudes.append(ev_magnitude)
# write QuakeML file
cat = Catalog(events=[ev])
memfile = io.BytesIO()
cat.write(memfile, format="quakeml", validate=IS_RECENT_LXML)
memfile.seek(0, 0)
new_cat = _read_quakeml(memfile)
self.assertEqual(len(new_cat), 1)
event = new_cat[0]
self.assertEqual(len(event.origins), 1)
self.assertEqual(len(event.magnitudes), 1)
self.assertEqual(len(event.focal_mechanisms), 1)
org = event.origins[0]
mag = event.magnitudes[0]
fm = event.focal_mechanisms[0]
self.assertEqual(org.latitude, lat)
self.assertEqual(org.longitude, lon)
self.assertEqual(org.depth, depth)
self.assertEqual(org.time, org_time)
# Moment tensor.
mt = fm.moment_tensor.tensor
self.assertTrue((fm.moment_tensor.scalar_moment - scalar_moment) /
scalar_moment < scalar_moment * 1E-10)
self.assertEqual(mt.m_rr, mrr)
self.assertEqual(mt.m_pp, mpp)
self.assertEqual(mt.m_tt, mtt)
self.assertEqual(mt.m_rt, mtr)
self.assertEqual(mt.m_rp, mpr)
self.assertEqual(mt.m_tp, mtp)
# Mag
self.assertAlmostEqual(mag.mag, moment_magnitude)
self.assertEqual(mag.magnitude_type, "Mw")
self.assertEqual(mag.evaluation_mode, "automatic")
def build(self):
"""
Build an obspy moment tensor focal mech event
This makes the tensor output into an Event containing:
1) a FocalMechanism with a MomentTensor, NodalPlanes, and PrincipalAxes
2) a Magnitude of the Mw from the Tensor
Which is what we want for outputting QuakeML using
the (slightly modified) obspy code.
Input
-----
filehandle => open file OR str from filehandle.read()
Output
------
event => instance of Event() class as described above
"""
p = self.parser
event = Event(event_type='earthquake')
origin = Origin()
focal_mech = FocalMechanism()
nodal_planes = NodalPlanes()
moment_tensor = MomentTensor()
principal_ax = PrincipalAxes()
magnitude = Magnitude()
data_used = DataUsed()
creation_info = CreationInfo(agency_id='NN')
ev_mode = 'automatic'
ev_stat = 'preliminary'
evid = None
orid = None
# Parse the entire file line by line.
for n,l in enumerate(p.line):
if 'REVIEWED BY NSL STAFF' in l:
ev_mode = 'manual'
ev_stat = 'reviewed'
if 'Event ID' in l:
evid = p._id(n)
if 'Origin ID' in l:
orid = p._id(n)
if 'Ichinose' in l:
moment_tensor.category = 'regional'
if re.match(r'^\d{4}\/\d{2}\/\d{2}', l):
ev = p._event_info(n)
if 'Depth' in l:
derived_depth = p._depth(n)
if 'Mw' in l:
magnitude.mag = p._mw(n)
magnitude.magnitude_type = 'Mw'
if 'Mo' in l and 'dyne' in l:
moment_tensor.scalar_moment = p._mo(n)
if 'Percent Double Couple' in l:
moment_tensor.double_couple = p._percent(n)
if 'Percent CLVD' in l:
moment_tensor.clvd = p._percent(n)
if 'Epsilon' in l:
moment_tensor.variance = p._epsilon(n)
if 'Percent Variance Reduction' in l:
moment_tensor.variance_reduction = p._percent(n)
if 'Major Double Couple' in l and 'strike' in p.line[n+1]:
np = p._double_couple(n)
nodal_planes.nodal_plane_1 = NodalPlane(*np[0])
nodal_planes.nodal_plane_2 = NodalPlane(*np[1])
nodal_planes.preferred_plane = 1
if 'Spherical Coordinates' in l:
mt = p._mt_sphere(n)
moment_tensor.tensor = Tensor(
m_rr = mt['Mrr'],
m_tt = mt['Mtt'],
m_pp = mt['Mff'],
m_rt = mt['Mrt'],
m_rp = mt['Mrf'],
m_tp = mt['Mtf'],
)
if 'Eigenvalues and eigenvectors of the Major Double Couple' in l:
ax = p._vectors(n)
principal_ax.t_axis = Axis(ax['T']['trend'], ax['T']['plunge'], ax['T']['ev'])
principal_ax.p_axis = Axis(ax['P']['trend'], ax['P']['plunge'], ax['P']['ev'])
principal_ax.n_axis = Axis(ax['N']['trend'], ax['N']['plunge'], ax['N']['ev'])
if 'Number of Stations' in l:
data_used.station_count = p._number_of_stations(n)
if 'Maximum' in l and 'Gap' in l:
focal_mech.azimuthal_gap = p._gap(n)
if re.match(r'^Date', l):
creation_info.creation_time = p._creation_time(n)
# Creation Time
creation_info.version = orid
# Fill in magnitude values
magnitude.evaluation_mode = ev_mode
magnitude.evaluation_status = ev_stat
magnitude.creation_info = creation_info.copy()
magnitude.resource_id = self._rid(magnitude)
# Stub origin
origin.time = ev.get('time')
origin.latitude = ev.get('lat')
origin.longitude = ev.get('lon')
origin.depth = derived_depth * 1000.
origin.depth_type = "from moment tensor inversion"
origin.creation_info = creation_info.copy()
# Unique from true origin ID
_oid = self._rid(origin)
origin.resource_id = ResourceIdentifier(str(_oid) + '/mt')
del _oid
# Make an id for the MT that references this origin
ogid = str(origin.resource_id)
doid = ResourceIdentifier(ogid, referred_object=origin)
# Make an id for the moment tensor mag which references this mag
mrid = str(magnitude.resource_id)
mmid = ResourceIdentifier(mrid, referred_object=magnitude)
# MT todo: could check/use URL for RID if parsing the php file
moment_tensor.evaluation_mode = ev_mode
moment_tensor.evaluation_status = ev_stat
moment_tensor.data_used = data_used
moment_tensor.moment_magnitude_id = mmid
moment_tensor.derived_origin_id = doid
moment_tensor.creation_info = creation_info.copy()
moment_tensor.resource_id = self._rid(moment_tensor)
# Fill in focal_mech values
focal_mech.nodal_planes = nodal_planes
focal_mech.moment_tensor = moment_tensor
focal_mech.principal_axes = principal_ax
focal_mech.creation_info = creation_info.copy()
focal_mech.resource_id = self._rid(focal_mech)
# add mech and new magnitude to event
event.focal_mechanisms = [focal_mech]
event.magnitudes = [magnitude]
event.origins = [origin]
event.creation_info = creation_info.copy()
# If an MT was done, that's the preferred mag/mech
event.preferred_magnitude_id = str(magnitude.resource_id)
event.preferred_focal_mechanism_id = str(focal_mech.resource_id)
if evid:
event.creation_info.version = evid
event.resource_id = self._rid(event)
self.event = event
def calculate_moment_magnitudes(cat, output_file):
"""
:param cat: obspy.core.event.Catalog object.
"""
Mws = []
Mls = []
Mws_std = []
for event in cat:
if not event.origins:
print "No origin for event %s" % event.resource_id
continue
if not event.magnitudes:
print "No magnitude for event %s" % event.resource_id
continue
origin_time = event.origins[0].time
local_magnitude = event.magnitudes[0].mag
#if local_magnitude < 1.0:
#continue
moments = []
source_radii = []
corner_frequencies = []
for pick in event.picks:
# Only p phase picks.
if pick.phase_hint.lower() == "p":
radiation_pattern = 0.52
velocity = V_P
k = 0.32
elif pick.phase_hint.lower() == "s":
radiation_pattern = 0.63
velocity = V_S
k = 0.21
else:
continue
distance = (pick.time - origin_time) * velocity
if distance <= 0.0:
continue
stream = get_corresponding_stream(pick.waveform_id, pick.time,
PADDING)
if stream is None or len(stream) != 3:
continue
omegas = []
corner_freqs = []
for trace in stream:
# Get the index of the pick.
pick_index = int(round((pick.time - trace.stats.starttime) / \
trace.stats.delta))
# Choose date window 0.5 seconds before and 1 second after pick.
data_window = trace.data[pick_index - \
int(TIME_BEFORE_PICK * trace.stats.sampling_rate): \
pick_index + int(TIME_AFTER_PICK * trace.stats.sampling_rate)]
# Calculate the spectrum.
spec, freq = mtspec.mtspec(data_window, trace.stats.delta, 2)
try:
fit = fit_spectrum(spec, freq, pick.time - origin_time,
spec.max(), 10.0)
except:
continue
if fit is None:
continue
Omega_0, f_c, err, _ = fit
Omega_0 = np.sqrt(Omega_0)
omegas.append(Omega_0)
corner_freqs.append(f_c)
M_0 = 4.0 * np.pi * DENSITY * velocity ** 3 * distance * \
np.sqrt(omegas[0] ** 2 + omegas[1] ** 2 + omegas[2] ** 2) / \
radiation_pattern
r = 3 * k * V_S / sum(corner_freqs)
moments.append(M_0)
source_radii.append(r)
corner_frequencies.extend(corner_freqs)
if not len(moments):
print "No moments could be calculated for event %s" % \
event.resource_id.resource_id
continue
# Calculate the seismic moment via basic statistics.
moments = np.array(moments)
moment = moments.mean()
moment_std = moments.std()
corner_frequencies = np.array(corner_frequencies)
corner_frequency = corner_frequencies.mean()
corner_frequency_std = corner_frequencies.std()
# Calculate the source radius.
source_radii = np.array(source_radii)
source_radius = source_radii.mean()
source_radius_std = source_radii.std()
# Calculate the stress drop of the event based on the average moment and
# source radii.
stress_drop = (7 * moment) / (16 * source_radius ** 3)
stress_drop_std = np.sqrt((stress_drop ** 2) * \
(((moment_std ** 2) / (moment ** 2)) + \
(9 * source_radius * source_radius_std ** 2)))
if source_radius > 0 and source_radius_std < source_radius:
print "Source radius:", source_radius, " Std:", source_radius_std
print "Stress drop:", stress_drop / 1E5, " Std:", stress_drop_std / 1E5
Mw = 2.0 / 3.0 * (np.log10(moment) - 9.1)
Mw_std = 2.0 / 3.0 * moment_std / (moment * np.log(10))
Mws_std.append(Mw_std)
Mws.append(Mw)
Mls.append(local_magnitude)
calc_diff = abs(Mw - local_magnitude)
Mw = ("%.3f" % Mw).rjust(7)
Ml = ("%.3f" % local_magnitude).rjust(7)
diff = ("%.3e" % calc_diff).rjust(7)
ret_string = colorama.Fore.GREEN + \
"For event %s: Ml=%s | Mw=%s | " % (event.resource_id.resource_id,
Ml, Mw)
if calc_diff >= 1.0:
ret_string += colorama.Fore.RED
ret_string += "Diff=%s" % diff
ret_string += colorama.Fore.GREEN
ret_string += " | Determined at %i stations" % len(moments)
ret_string += colorama.Style.RESET_ALL
print ret_string
mag = Magnitude()
mag.mag = Mw
mag.mag_errors.uncertainty = Mw_std
mag.magnitude_type = "Mw"
mag.origin_id = event.origins[0].resource_id
mag.method_id = "smi:com.github/krischer/moment_magnitude_calculator/automatic/1"
mag.station_count = len(moments)
mag.evaluation_mode = "automatic"
mag.evaluation_status = "preliminary"
mag.comments.append(Comment( \
"Seismic Moment=%e Nm; standard deviation=%e" % (moment,
moment_std)))
mag.comments.append(Comment("Custom fit to Boatwright spectrum"))
if source_radius > 0 and source_radius_std < source_radius:
mag.comments.append(Comment( \
"Source radius=%.2fm; standard deviation=%.2f" % (source_radius,
source_radius_std)))
event.magnitudes.append(mag)
print "Writing output file..."
cat.write(output_file, format="quakeml")
def calculate_moment_magnitudes(cat, output_file):
"""
:param cat: obspy.core.event.Catalog object.
"""
Mws = []
Mls = []
Mws_std = []
for event in cat:
if not event.origins:
print "No origin for event %s" % event.resource_id
continue
if not event.magnitudes:
print "No magnitude for event %s" % event.resource_id
continue
origin_time = event.origins[0].time
local_magnitude = event.magnitudes[0].mag
#if local_magnitude < 1.0:
#continue
moments = []
source_radii = []
corner_frequencies = []
for pick in event.picks:
# Only p phase picks.
if pick.phase_hint.lower() == "p":
radiation_pattern = 0.52
velocity = V_P
k = 0.32
elif pick.phase_hint.lower() == "s":
radiation_pattern = 0.63
velocity = V_S
k = 0.21
else:
continue
distance = (pick.time - origin_time) * velocity
if distance <= 0.0:
continue
stream = get_corresponding_stream(pick.waveform_id, pick.time,
PADDING)
if stream is None or len(stream) != 3:
continue
omegas = []
corner_freqs = []
for trace in stream:
# Get the index of the pick.
pick_index = int(round((pick.time - trace.stats.starttime) / \
trace.stats.delta))
# Choose date window 0.5 seconds before and 1 second after pick.
data_window = trace.data[pick_index - \
int(TIME_BEFORE_PICK * trace.stats.sampling_rate): \
pick_index + int(TIME_AFTER_PICK * trace.stats.sampling_rate)]
# Calculate the spectrum.
spec, freq = mtspec.mtspec(data_window, trace.stats.delta, 2)
try:
fit = fit_spectrum(spec, freq, pick.time - origin_time,
spec.max(), 10.0)
except:
continue
if fit is None:
continue
Omega_0, f_c, err, _ = fit
Omega_0 = np.sqrt(Omega_0)
omegas.append(Omega_0)
corner_freqs.append(f_c)
M_0 = 4.0 * np.pi * DENSITY * velocity ** 3 * distance * \
np.sqrt(omegas[0] ** 2 + omegas[1] ** 2 + omegas[2] ** 2) / \
radiation_pattern
r = 3 * k * V_S / sum(corner_freqs)
moments.append(M_0)
source_radii.append(r)
corner_frequencies.extend(corner_freqs)
if not len(moments):
print "No moments could be calculated for event %s" % \
event.resource_id.resource_id
continue
# Calculate the seismic moment via basic statistics.
moments = np.array(moments)
moment = moments.mean()
moment_std = moments.std()
corner_frequencies = np.array(corner_frequencies)
corner_frequency = corner_frequencies.mean()
corner_frequency_std = corner_frequencies.std()
# Calculate the source radius.
source_radii = np.array(source_radii)
source_radius = source_radii.mean()
source_radius_std = source_radii.std()
# Calculate the stress drop of the event based on the average moment and
# source radii.
stress_drop = (7 * moment) / (16 * source_radius ** 3)
stress_drop_std = np.sqrt((stress_drop ** 2) * \
(((moment_std ** 2) / (moment ** 2)) + \
(9 * source_radius * source_radius_std ** 2)))
if source_radius > 0 and source_radius_std < source_radius:
print "Source radius:", source_radius, " Std:", source_radius_std
print "Stress drop:", stress_drop / 1E5, " Std:", stress_drop_std / 1E5
Mw = 2.0 / 3.0 * (np.log10(moment) - 9.1)
Mw_std = 2.0 / 3.0 * moment_std / (moment * np.log(10))
Mws_std.append(Mw_std)
Mws.append(Mw)
Mls.append(local_magnitude)
calc_diff = abs(Mw - local_magnitude)
Mw = ("%.3f" % Mw).rjust(7)
Ml = ("%.3f" % local_magnitude).rjust(7)
diff = ("%.3e" % calc_diff).rjust(7)
ret_string = colorama.Fore.GREEN + \
"For event %s: Ml=%s | Mw=%s | " % (event.resource_id.resource_id,
Ml, Mw)
if calc_diff >= 1.0:
ret_string += colorama.Fore.RED
ret_string += "Diff=%s" % diff
ret_string += colorama.Fore.GREEN
ret_string += " | Determined at %i stations" % len(moments)
ret_string += colorama.Style.RESET_ALL
print ret_string
mag = Magnitude()
mag.mag = Mw
mag.mag_errors.uncertainty = Mw_std
mag.magnitude_type = "Mw"
mag.origin_id = event.origins[0].resource_id
mag.method_id = "Custom fit to Boatwright spectrum"
mag.station_count = len(moments)
mag.evaluation_mode = "automatic"
mag.evaluation_status = "preliminary"
mag.comments.append(Comment( \
"Seismic Moment=%e Nm; standard deviation=%e" % (moment,
moment_std)))
if source_radius > 0 and source_radius_std < source_radius:
mag.comments.append(Comment( \
"Source radius=%.2fm; standard deviation=%.2f" % (source_radius,
source_radius_std)))
event.magnitudes.append(mag)
print "Writing output file..."
cat.write(output_file, format="quakeml")
def _read_single_event(event_file, locate_dir, units, local_mag_ph):
"""
Parse an event file from QuakeMigrate into an obspy Event object.
Parameters
----------
event_file : `pathlib.Path` object
Path to .event file to read.
locate_dir : `pathlib.Path` object
Path to locate directory (contains "events", "picks" etc. directories).
units : {"km", "m"}
Grid projection coordinates for QM LUT (determines units of depths and
uncertainties in the .event files).
local_mag_ph : {"S", "P"}
Amplitude measurement used to calculate local magnitudes.
Returns
-------
event : `obspy.Event` object
Event object populated with all available information output by
:class:`~quakemigrate.signal.scan.locate()`, including event locations
and uncertainties, picks, and amplitudes and magnitudes if available.
"""
# Parse information from event file
event_info = pd.read_csv(event_file).iloc[0]
event_uid = str(event_info["EventID"])
# Set distance conversion factor (from units of QM LUT projection units).
if units == "km":
factor = 1e3
elif units == "m":
factor = 1
else:
raise AttributeError(f"units must be 'km' or 'm'; not {units}")
# Create event object to store origin and pick information
event = Event()
event.extra = AttribDict()
event.resource_id = str(event_info["EventID"])
event.creation_info = CreationInfo(author="QuakeMigrate",
version=quakemigrate.__version__)
# Add COA info to extra
event.extra.coa = {"value": event_info["COA"], "namespace": ns}
event.extra.coa_norm = {"value": event_info["COA_NORM"], "namespace": ns}
event.extra.trig_coa = {"value": event_info["TRIG_COA"], "namespace": ns}
event.extra.dec_coa = {"value": event_info["DEC_COA"], "namespace": ns}
event.extra.dec_coa_norm = {
"value": event_info["DEC_COA_NORM"],
"namespace": ns
}
# Determine location of cut waveform data - add to event object as a
# custom extra attribute.
mseed = locate_dir / "raw_cut_waveforms" / event_uid
event.extra.cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
if (locate_dir / "real_cut_waveforms").exists():
mseed = locate_dir / "real_cut_waveforms" / event_uid
event.extra.real_cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
if (locate_dir / "wa_cut_waveforms").exists():
mseed = locate_dir / "wa_cut_waveforms" / event_uid
event.extra.wa_cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
# Create origin with spline location and set to preferred event origin.
origin = Origin()
origin.method_id = "spline"
origin.longitude = event_info["X"]
origin.latitude = event_info["Y"]
origin.depth = event_info["Z"] * factor
origin.time = UTCDateTime(event_info["DT"])
event.origins = [origin]
event.preferred_origin_id = origin.resource_id
# Create origin with gaussian location and associate with event
origin = Origin()
origin.method_id = "gaussian"
origin.longitude = event_info["GAU_X"]
origin.latitude = event_info["GAU_Y"]
origin.depth = event_info["GAU_Z"] * factor
origin.time = UTCDateTime(event_info["DT"])
event.origins.append(origin)
ouc = OriginUncertainty()
ce = ConfidenceEllipsoid()
ce.semi_major_axis_length = event_info["COV_ErrY"] * factor
ce.semi_intermediate_axis_length = event_info["COV_ErrX"] * factor
ce.semi_minor_axis_length = event_info["COV_ErrZ"] * factor
ce.major_axis_plunge = 0
ce.major_axis_azimuth = 0
ce.major_axis_rotation = 0
ouc.confidence_ellipsoid = ce
ouc.preferred_description = "confidence ellipsoid"
# Set uncertainties for both as the gaussian uncertainties
for origin in event.origins:
origin.longitude_errors.uncertainty = kilometer2degrees(
event_info["GAU_ErrX"] * factor / 1e3)
origin.latitude_errors.uncertainty = kilometer2degrees(
event_info["GAU_ErrY"] * factor / 1e3)
origin.depth_errors.uncertainty = event_info["GAU_ErrZ"] * factor
origin.origin_uncertainty = ouc
# Add OriginQuality info to each origin?
for origin in event.origins:
origin.origin_type = "hypocenter"
origin.evaluation_mode = "automatic"
# --- Handle picks file ---
pick_file = locate_dir / "picks" / event_uid
if pick_file.with_suffix(".picks").is_file():
picks = pd.read_csv(pick_file.with_suffix(".picks"))
else:
return None
for _, pickline in picks.iterrows():
station = str(pickline["Station"])
phase = str(pickline["Phase"])
wid = WaveformStreamID(network_code="", station_code=station)
for method in ["modelled", "autopick"]:
pick = Pick()
pick.extra = AttribDict()
pick.waveform_id = wid
pick.method_id = method
pick.phase_hint = phase
if method == "autopick" and str(pickline["PickTime"]) != "-1":
pick.time = UTCDateTime(pickline["PickTime"])
pick.time_errors.uncertainty = float(pickline["PickError"])
pick.extra.snr = {
"value": float(pickline["SNR"]),
"namespace": ns
}
elif method == "modelled":
pick.time = UTCDateTime(pickline["ModelledTime"])
else:
continue
event.picks.append(pick)
# --- Handle amplitudes file ---
amps_file = locate_dir / "amplitudes" / event_uid
if amps_file.with_suffix(".amps").is_file():
amps = pd.read_csv(amps_file.with_suffix(".amps"))
i = 0
for _, ampsline in amps.iterrows():
wid = WaveformStreamID(seed_string=ampsline["id"])
noise_amp = ampsline["Noise_amp"] / 1000 # mm to m
for phase in ["P_amp", "S_amp"]:
amp = Amplitude()
if pd.isna(ampsline[phase]):
continue
amp.generic_amplitude = ampsline[phase] / 1000 # mm to m
amp.generic_amplitude_errors.uncertainty = noise_amp
amp.unit = "m"
amp.type = "AML"
amp.method_id = phase
amp.period = 1 / ampsline[f"{phase[0]}_freq"]
amp.time_window = TimeWindow(
reference=UTCDateTime(ampsline[f"{phase[0]}_time"]))
# amp.pick_id = ?
amp.waveform_id = wid
# amp.filter_id = ?
amp.magnitude_hint = "ML"
amp.evaluation_mode = "automatic"
amp.extra = AttribDict()
try:
amp.extra.filter_gain = {
"value": ampsline[f"{phase[0]}_filter_gain"],
"namespace": ns
}
amp.extra.avg_amp = {
"value": ampsline[f"{phase[0]}_avg_amp"] / 1000, # m
"namespace": ns
}
except KeyError:
pass
if phase[0] == local_mag_ph and not pd.isna(ampsline["ML"]):
i += 1
stat_mag = StationMagnitude()
stat_mag.extra = AttribDict()
# stat_mag.origin_id = ? local_mag_loc
stat_mag.mag = ampsline["ML"]
stat_mag.mag_errors.uncertainty = ampsline["ML_Err"]
stat_mag.station_magnitude_type = "ML"
stat_mag.amplitude_id = amp.resource_id
stat_mag.extra.picked = {
"value": ampsline["is_picked"],
"namespace": ns
}
stat_mag.extra.epi_dist = {
"value": ampsline["epi_dist"],
"namespace": ns
}
stat_mag.extra.z_dist = {
"value": ampsline["z_dist"],
"namespace": ns
}
event.station_magnitudes.append(stat_mag)
event.amplitudes.append(amp)
mag = Magnitude()
mag.extra = AttribDict()
mag.mag = event_info["ML"]
mag.mag_errors.uncertainty = event_info["ML_Err"]
mag.magnitude_type = "ML"
# mag.origin_id = ?
mag.station_count = i
mag.evaluation_mode = "automatic"
mag.extra.r2 = {"value": event_info["ML_r2"], "namespace": ns}
event.magnitudes = [mag]
event.preferred_magnitude_id = mag.resource_id
return event
def write_qml(config, sourcepar):
if not config.options.qml_file:
return
qml_file = config.options.qml_file
cat = read_events(qml_file)
evid = config.hypo.evid
try:
ev = [e for e in cat if evid in str(e.resource_id)][0]
except Exception:
logging.warning('Unable to find evid "{}" in QuakeML file. '
'QuakeML output will not be written.'.format(evid))
origin = ev.preferred_origin()
if origin is None:
origin = ev.origins[0]
origin_id = origin.resource_id
origin_id_strip = origin_id.id.split('/')[-1]
origin_id_strip = origin_id_strip.replace(config.smi_strip_from_origin_id,
'')
# Common parameters
ssp_version = get_versions()['version']
method_id = config.smi_base + '/sourcespec/' + ssp_version
cr_info = CreationInfo()
cr_info.agency_id = config.agency_id
if config.author is None:
author = '{}@{}'.format(getuser(), gethostname())
else:
author = config.author
cr_info.author = author
cr_info.creation_time = UTCDateTime()
means = sourcepar.means_weight
errors = sourcepar.errors_weight
stationpar = sourcepar.station_parameters
# Magnitude
mag = Magnitude()
_id = config.smi_magnitude_template.replace('$SMI_BASE', config.smi_base)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
mag.resource_id = ResourceIdentifier(id=_id)
mag.method_id = ResourceIdentifier(id=method_id)
mag.origin_id = origin_id
mag.magnitude_type = 'Mw'
mag.mag = means['Mw']
mag_err = QuantityError()
mag_err.uncertainty = errors['Mw']
mag_err.confidence_level = 68.2
mag.mag_errors = mag_err
mag.station_count = len([_s for _s in stationpar.keys()])
mag.evaluation_mode = 'automatic'
mag.creation_info = cr_info
# Seismic moment -- It has to be stored in a MomentTensor object
# which, in turn, is part of a FocalMechanism object
mt = MomentTensor()
_id = config.smi_moment_tensor_template.replace('$SMI_BASE',
config.smi_base)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
mt.resource_id = ResourceIdentifier(id=_id)
mt.derived_origin_id = origin_id
mt.moment_magnitude_id = mag.resource_id
mt.scalar_moment = means['Mo']
mt_err = QuantityError()
mt_err.lower_uncertainty = errors['Mo'][0]
mt_err.upper_uncertainty = errors['Mo'][1]
mt_err.confidence_level = 68.2
mt.scalar_moment_errors = mt_err
mt.method_id = method_id
mt.creation_info = cr_info
# And here is the FocalMechanism object
fm = FocalMechanism()
_id = config.smi_focal_mechanism_template.replace('$SMI_BASE',
config.smi_base)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
fm.resource_id = ResourceIdentifier(id=_id)
fm.triggering_origin_id = origin_id
fm.method_id = ResourceIdentifier(id=method_id)
fm.moment_tensor = mt
fm.creation_info = cr_info
ev.focal_mechanisms.append(fm)
# Station magnitudes
for statId in sorted(stationpar.keys()):
par = stationpar[statId]
st_mag = StationMagnitude()
seed_id = statId.split()[0]
_id = config.smi_station_magnitude_template.replace(
'$SMI_MAGNITUDE_TEMPLATE', config.smi_magnitude_template)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
_id = _id.replace('$SMI_BASE', config.smi_base)
_id = _id.replace('$WAVEFORM_ID', seed_id)
st_mag.resource_id = ResourceIdentifier(id=_id)
st_mag.origin_id = origin_id
st_mag.mag = par['Mw']
st_mag.station_magnitude_type = 'Mw'
st_mag.method_id = mag.method_id
st_mag.creation_info = cr_info
st_mag.waveform_id = WaveformStreamID(seed_string=seed_id)
st_mag.extra = SSPExtra()
st_mag.extra.moment = SSPTag(par['Mo'])
st_mag.extra.corner_frequency = SSPTag(par['fc'])
st_mag.extra.t_star = SSPTag(par['t_star'])
ev.station_magnitudes.append(st_mag)
st_mag_contrib = StationMagnitudeContribution()
st_mag_contrib.station_magnitude_id = st_mag.resource_id
mag.station_magnitude_contributions.append(st_mag_contrib)
ev.magnitudes.append(mag)
# Write other average parameters as custom tags
ev.extra = SSPExtra()
ev.extra.corner_frequency = SSPContainerTag()
ev.extra.corner_frequency.value.value = SSPTag(means['fc'])
ev.extra.corner_frequency.value.lower_uncertainty =\
SSPTag(errors['fc'][0])
ev.extra.corner_frequency.value.upper_uncertainty =\
SSPTag(errors['fc'][1])
ev.extra.corner_frequency.value.confidence_level = SSPTag(68.2)
ev.extra.t_star = SSPContainerTag()
ev.extra.t_star.value.value = SSPTag(means['t_star'])
ev.extra.t_star.value.uncertainty = SSPTag(errors['t_star'])
ev.extra.t_star.value.confidence_level = SSPTag(68.2)
ev.extra.source_radius = SSPContainerTag()
ev.extra.source_radius.value.value = SSPTag(means['ra'])
ev.extra.source_radius.value.lower_uncertainty =\
SSPTag(errors['ra'][0])
ev.extra.source_radius.value.upper_uncertainty =\
SSPTag(errors['ra'][1])
ev.extra.source_radius.value.confidence_level = SSPTag(68.2)
ev.extra.stress_drop = SSPContainerTag()
ev.extra.stress_drop.value.value = SSPTag(means['bsd'])
ev.extra.stress_drop.value.lower_uncertainty =\
SSPTag(errors['bsd'][0])
ev.extra.stress_drop.value.upper_uncertainty =\
SSPTag(errors['bsd'][1])
ev.extra.stress_drop.value.confidence_level = SSPTag(68.2)
if config.set_preferred_magnitude:
ev.preferred_magnitude_id = mag.resource_id.id
qml_file_out = os.path.join(config.options.outdir, evid + '.xml')
ev.write(qml_file_out, format='QUAKEML')
logging.info('QuakeML file written to: ' + qml_file_out)
