def test_geostuff():
grid = np.zeros((2, 5))
location = [45.0, 45.0]
dist = geographical_distances(grid, location)
assert(type(dist) == np.ndarray)
assert(pytest.approx(dist[0]) == 6662472.7182103)
assert is_land(location, location)[0]
assert(floor(geograph_to_geocent(location[0])) == 44)
assert(pytest.approx(len_deg_lat(location[0])) == 111131.779)
assert(pytest.approx(len_deg_lon(location[0])) == 78582.91976)
def green_from_instaseis(self, station):
# set some parameters
lat_sta = station['lat']
lon_sta = station['lon']
lat_sta = geograph_to_geocent(float(lat_sta))
lon_sta = float(lon_sta)
rec = instaseis.Receiver(latitude=lat_sta, longitude=lon_sta)
point_f = float(self.config['wavefield_point_force'])
channel = self.config['wavefield_channel']
station_id = station['net'] + '.' + station['sta'] + '..MX' + channel
if self.config['verbose']:
print(station_id)
if channel == 'Z':
c_index = 0
elif channel == 'R':
c_index = 1
elif channel == 'T':
c_index = 2
else:
raise ValueError("Unknown channel: %s, choose R, T, Z"
% channel)
# initialize the file
f_out = os.path.join(self.wf_path, station_id + '.h5')
with h5py.File(f_out, "w") as f:
# DATASET NR 1: STATS
stats = f.create_dataset('stats', data=(0,))
stats.attrs['reference_station'] = station['sta']
stats.attrs['data_quantity'] = self.data_quantity
stats.attrs['ntraces'] = self.ntraces
stats.attrs['Fs'] = self.Fs
stats.attrs['nt'] = int(self.npts)
stats.attrs['npad'] = self.npad
if self.fdomain:
stats.attrs['fdomain'] = True
else:
stats.attrs['fdomain'] = False
# DATASET NR 2: Source grid
f.create_dataset('sourcegrid', data=self.sourcegrid)
# DATASET Nr 3: Seismograms itself
if self.fdomain:
traces = f.create_dataset('data', (self.ntraces,
self.npts + 1),
dtype=np.complex64)
else:
traces = f.create_dataset('data', (self.ntraces, self.npts),
dtype=np.float32)
for i in range(self.ntraces):
if i % 1000 == 0 and i > 0 and self.config['verbose']:
print('Converted %g of %g traces' % (i, self.ntraces))
lat_src = geograph_to_geocent(self.sourcegrid[1, i])
lon_src = self.sourcegrid[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src, f_r=point_f)
if self.config['synt_data'] == 'DIS':
values = self.db.get_seismograms(source=fsrc,
receiver=rec,
dt=1. / self.Fs)
elif self.config['synt_data'] == 'VEL':
values = self.db.get_seismograms(source=fsrc,
receiver=rec,
dt=1. / self.Fs,
kind='velocity')
elif self.config['synt_data'] == 'ACC':
values = self.db.get_seismograms(source=fsrc,
receiver=rec,
dt=1. / self.Fs,
kind='acceleration')
else:
raise ValueError('Unknown data quantity. \
Choose DIS, VEL or ACC in configuration.')
if channel in ['R', 'T']:
baz = gps2dist_azimuth(lat_src, lon_src,
lat_sta, lon_sta)[2]
values.rotate('NE->RT', baz)
if self.filter is not None:
trace = lfilter(*self.filter, x=values[c_index].data)
else:
trace = values[c_index].data
if self.fdomain:
trace_fd = np.fft.rfft(trace[0: self.npts],
n=self.npad)
traces[i, :] = trace_fd
else:
traces[i, :] = trace[0: self.npts]
return()
def compute_correlation(input_files,
all_conf,
nsrc,
all_ns,
taper,
insta=False):
"""
Compute noise cross-correlations from two .h5 'wavefield' files.
Noise source distribution and spectrum is given by starting_model.h5
It is assumed that noise sources are delta-correlated in space.
Metainformation: Include the reference station names for both stations
from wavefield files, if possible. Do not include geographic information
from .csv file as this might be error-prone. Just add the geographic
info later if needed.
"""
wf1, wf2 = input_files
ntime, n, n_corr, Fs = all_ns
ntraces = nsrc.src_loc[0].shape[0]
correlation = np.zeros(n_corr)
if insta:
# open database
dbpath = all_conf.config['wavefield_path']
# open
db = instaseis.open_db(dbpath)
# get receiver locations
station1 = wf1[0]
station2 = wf2[0]
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1)
taper1 = cosine_taper((wf1.stats["nt"]))
wf2 = WaveField(wf2)
taper2 = cosine_taper((wf2.stats["nt"]))
station1 = wf1.stats['reference_station']
station2 = wf2.stats['reference_station']
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
# Loop over source locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
S = nsrc.get_spect(i)
if S.sum() == 0.:
# If amplitude is 0, continue. (Spectrum has 0 phase anyway.)
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
Fs = all_conf.config['wavefield_sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=1. / Fs)[0].data * taper1
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=1. / Fs)[0].data * taper2
s1 = np.ascontiguousarray(s1)
s2 = np.ascontiguousarray(s2)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
if not wf1.fdomain:
# read Green's functions
s1 = np.ascontiguousarray(wf1.data[i, :] * taper1)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper2)
# Fourier transform for greater ease of convolution
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
spec1 = np.ascontiguousarray(wf1.data[i, :])
spec2 = np.ascontiguousarray(wf2.data[i, :])
# convolve G1G2
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
# convolve noise source
c = np.multiply(g1g2_tr, S[:len(g1g2_tr)])
# transform back
correlation += my_centered(np.fft.fftshift(np.fft.irfft(c, n)),
n_corr) * nsrc.surf_area[i]
# occasional info
if i % print_each_n == 0 and all_conf.config['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
# end of loop over all source locations #######################################
return (correlation, station1, station2)
def g1g2_kern(wf1str, wf2str, kernel, adjt, src, source_conf, insta=False):
measr_conf = yaml.safe_load(
open(os.path.join(source_conf['source_path'], 'measr_config.yml')))
bandpass = measr_conf['bandpass']
conf = yaml.safe_load(
open(os.path.join(source_conf['project_path'], 'config.yml')))
if bandpass is None:
filtcnt = 1
elif type(bandpass) == list:
if type(bandpass[0]) != list:
filtcnt = 1
else:
filtcnt = len(bandpass)
ntime, n, n_corr, Fs = get_ns(wf1str, source_conf, insta)
# use a one-sided taper: The seismogram probably has a non-zero end,
# being cut off whereever the solver stopped running.
taper = cosine_taper(ntime, p=0.01)
taper[0:ntime // 2] = 1.0
########################################################################
# Prepare filenames and adjoint sources
########################################################################
filenames = []
adjt_srcs = []
for ix_f in range(filtcnt):
filename = kernel + '.{}.npy'.format(ix_f)
filenames.append(filename)
f = Stream()
for a in adjt:
adjtfile = a + '*.{}.sac'.format(ix_f)
adjtfile = glob(adjtfile)
try:
f += read(adjtfile[0])[0]
f[-1].data = my_centered(f[-1].data, n_corr)
except IndexError:
warn('No adjoint source found: {}\n'.format(a))
if len(f) > 0:
adjt_srcs.append(f)
else:
return ()
########################################################################
# Compute the kernels
########################################################################
with NoiseSource(src) as nsrc:
# Uniform spatial weights.
nsrc.distr_basis = np.ones(nsrc.distr_basis.shape)
ntraces = nsrc.src_loc[0].shape[0]
if insta:
# open database
dbpath = conf['wavefield_path']
# open and determine Fs, nt
db = instaseis.open_db(dbpath)
# get receiver locations
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1str)
wf2 = WaveField(wf2str)
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
kern = np.zeros((filtcnt, ntraces, len(adjt)))
# Loop over locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
# For the kernel, this contains only the basis functions of the
# spectrum without weights; might still be location-dependent,
# for example when constraining sensivity to ocean
S = nsrc.get_spect(i)
if S.sum() == 0.:
# The spectrum has 0 phase so only checking
# absolute value here
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
dt = 1. / source_conf['sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=dt)[0].data * taper
s1 = np.ascontiguousarray(s1)
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=dt)[0].data * taper
s2 = np.ascontiguousarray(s2)
else:
s1 = np.ascontiguousarray(wf1.data[i, :] * taper)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
c = np.multiply(g1g2_tr, S)
#######################################################################
# Get Kernel at that location
#######################################################################
corr_temp = my_centered(np.fft.fftshift(np.fft.irfft(c, n)),
n_corr)
#######################################################################
# Apply the 'adjoint source'
#######################################################################
for ix_f in range(filtcnt):
f = adjt_srcs[ix_f]
if f is None:
continue
for j in range(len(f)):
delta = f[j].stats.delta
kern[ix_f, i, j] = np.dot(corr_temp, f[j].data) * delta
if i % print_each_n == 0 and conf['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
if not insta:
wf1.file.close()
wf2.file.close()
for ix_f in range(filtcnt):
filename = filenames[ix_f]
if kern[ix_f, :, :].sum() != 0:
np.save(filename, kern[ix_f, :, :])
return ()
def g1g2_corr(wf1, wf2, corr_file, src, source_conf, insta=False):
"""
Compute noise cross-correlations from two .h5 'wavefield' files.
Noise source distribution and spectrum is given by starting_model.h5
It is assumed that noise sources are delta-correlated in space.
Metainformation: Include the reference station names for both stations
from wavefield files, if possible. Do not include geographic information
from .csv file as this might be error-prone. Just add the geographic
info later if needed.
"""
with open(os.path.join(source_conf['project_path'], 'config.yml')) as fh:
conf = yaml.safe_load(fh)
with NoiseSource(src) as nsrc:
ntime, n, n_corr, Fs = get_ns(wf1, source_conf, insta)
# use a one-sided taper: The seismogram probably has a non-zero end,
# being cut off wherever the solver stopped running.
taper = cosine_taper(ntime, p=0.01)
taper[0:ntime // 2] = 1.0
ntraces = nsrc.src_loc[0].shape[0]
correlation = np.zeros(n_corr)
if insta:
# open database
dbpath = conf['wavefield_path']
# open
db = instaseis.open_db(dbpath)
# get receiver locations
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1)
wf2 = WaveField(wf2)
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
# Loop over source locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
S = nsrc.get_spect(i)
if S.sum() == 0.:
# If amplitude is 0, continue. (Spectrum has 0 phase anyway.)
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
Fs = conf['wavefield_sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=1. / Fs)[0].data * taper
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=1. / Fs)[0].data * taper
s1 = np.ascontiguousarray(s1)
s2 = np.ascontiguousarray(s2)
else:
if not wf1.fdomain:
# read Green's functions
s1 = np.ascontiguousarray(wf1.data[i, :] * taper)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper)
# Fourier transform for greater ease of convolution
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
pass
# convolve G1G2
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
# convolve noise source
c = np.multiply(g1g2_tr, S)
# transform back
correlation += my_centered(np.fft.ifftshift(np.fft.irfft(c, n)),
n_corr) * nsrc.surf_area[i]
# occasional info
if i % print_each_n == 0 and conf['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
# end of loop over all source locations #######################################
if not insta:
wf1.file.close()
wf2.file.close()
# save output
trace = Trace()
trace.stats.sampling_rate = Fs
trace.data = correlation
# try to add some meta data
try:
sta1 = wf1.stats['reference_station']
sta2 = wf2.stats['reference_station']
trace.stats.station = sta1.split('.')[1]
trace.stats.network = sta1.split('.')[0]
trace.stats.location = sta1.split('.')[2]
trace.stats.channel = sta1.split('.')[3]
trace.stats.sac = {}
trace.stats.sac['kuser0'] = sta2.split('.')[1]
trace.stats.sac['kuser1'] = sta2.split('.')[0]
trace.stats.sac['kuser2'] = sta2.split('.')[2]
trace.stats.sac['kevnm'] = sta2.split('.')[3]
except (KeyError, IndexError):
pass
trace.write(filename=corr_file, format='SAC')
def compute_kernel(input_files, all_conf, nsrc, all_ns, taper, insta=False):
ntime, n, n_corr, Fs = all_ns
wf1, wf2, adjt = input_files
########################################################################
# Prepare filenames and adjoint sources
########################################################################
adjt_srcs = []
for ix_f in range(all_conf.filtcnt):
f = Stream()
for a in adjt:
adjtfile = a + '*.{}.sac'.format(ix_f)
adjtfile = glob(adjtfile)
try:
f += read(adjtfile[0])[0]
f[-1].data = my_centered(f[-1].data, n_corr)
except IndexError:
if all_conf.config['verbose']:
print('No adjoint source found: {}\n'.format(a))
else:
pass
if len(f) > 0:
adjt_srcs.append(f)
else:
return None
# Uniform spatial weights. (current model is in the adjoint source)
nsrc.distr_basis = np.ones(nsrc.distr_basis.shape)
ntraces = nsrc.src_loc[0].shape[0]
if insta:
# open database
dbpath = all_conf.config['wavefield_path']
# open and determine Fs, nt
db = instaseis.open_db(dbpath)
# get receiver locations
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1)
wf2 = WaveField(wf2)
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
kern = np.zeros(
(nsrc.spect_basis.shape[0], all_conf.filtcnt, ntraces, len(adjt)))
# Loop over locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
# For the kernel, this contains only the basis functions of the
# spectrum without weights; might still be location-dependent,
# for example when constraining sensivity to ocean
S = nsrc.get_spect(i)
if S.sum() == 0.:
# The spectrum has 0 phase so only checking
# absolute value here
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
dt = 1. / all_conf.source_config['sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=dt)[0].data * taper
s1 = np.ascontiguousarray(s1)
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=dt)[0].data * taper
s2 = np.ascontiguousarray(s2)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
if not wf1.fdomain:
s1 = np.ascontiguousarray(wf1.data[i, :] * taper)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
spec1 = wf1.data[i, :]
spec2 = wf2.data[i, :]
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
# spectrum
for ix_spec in range(nsrc.spect_basis.shape[0]):
c = np.multiply(g1g2_tr, nsrc.spect_basis[ix_spec, :])
###################################################################
# Get Kernel at that location
###################################################################
corr_temp = my_centered(np.fft.fftshift(np.fft.irfft(c, n)),
n_corr)
###################################################################
# Apply the 'adjoint source'
###################################################################
for ix_f in range(all_conf.filtcnt):
f = adjt_srcs[ix_f]
if f is None:
continue
for j in range(len(f)):
delta = f[j].stats.delta
kern[ix_spec, ix_f, i,
j] = np.dot(corr_temp, f[j].data) * delta
if i % print_each_n == 0 and all_conf.config['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
if not insta:
wf1.file.close()
wf2.file.close()
return kern