def search_inc(self, bazi):
inc_range = np.arange(0.1, 90, 0.1)
s_range = self.trim(20, 20, isreturn=True)
power = np.zeros(inc_range.shape[0])
for i in range(len(inc_range)):
l_comp, _, _ = rotate_zne_lqt(s_range[2].data, s_range[1].data,
s_range[0].data, bazi, inc_range[i])
power[i] = np.mean(l_comp**2)
real_inc_idx = np.argmin(power)
real_inc = inc_range[real_inc_idx]
self.inc_correction = real_inc - self.inc
self.inc = real_inc
def test_rotate_zne_lqt_vs_pitsa(self):
"""
Test LQT component rotation against PITSA. Test back-rotation.
"""
# load test files
with gzip.open(os.path.join(self.path, 'rjob_20051006.gz')) as f:
data_z = np.loadtxt(f)
with gzip.open(os.path.join(self.path, 'rjob_20051006_n.gz')) as f:
data_n = np.loadtxt(f)
with gzip.open(os.path.join(self.path, 'rjob_20051006_e.gz')) as f:
data_e = np.loadtxt(f)
# test different backazimuth/incidence combinations
for ba, inci in ((60, 130), (210, 60)):
# rotate traces
data_l, data_q, data_t = \
rotate_zne_lqt(data_z, data_n, data_e, ba, inci)
# rotate traces back to ZNE
data_back_z, data_back_n, data_back_e = \
rotate_lqt_zne(data_l, data_q, data_t, ba, inci)
# load pitsa files
with gzip.open(os.path.join(self.path,
'rjob_20051006_q_%sba_%sinc.gz' %
(ba, inci))) as f:
data_pitsa_q = np.loadtxt(f)
with gzip.open(os.path.join(self.path,
'rjob_20051006_t_%sba_%sinc.gz' %
(ba, inci))) as f:
data_pitsa_t = np.loadtxt(f)
with gzip.open(os.path.join(self.path,
'rjob_20051006_l_%sba_%sinc.gz' %
(ba, inci))) as f:
data_pitsa_l = np.loadtxt(f)
# Assert the output. Has to be to rather low accuracy due to
# rounding error prone rotation and single precision value.
self.assertTrue(
np.allclose(data_l, data_pitsa_l, rtol=1E-3, atol=1E-5))
self.assertTrue(
np.allclose(data_q, data_pitsa_q, rtol=1E-3, atol=1E-5))
self.assertTrue(
np.allclose(data_t, data_pitsa_t, rtol=1E-3, atol=1E-5))
self.assertTrue(
np.allclose(data_z, data_back_z, rtol=1E-3, atol=1E-5))
self.assertTrue(
np.allclose(data_n, data_back_n, rtol=1E-3, atol=1E-5))
self.assertTrue(
np.allclose(data_e, data_back_e, rtol=1E-3, atol=1E-5))
def search_inc(self, bazi):
inc_range = np.arange(0.1, 90, 0.1)
# bazi_range = np.repeat(bazi, len(inc_range))
# M_all = seispy.geo.rot3D(bazi=bazi_range, inc=inc_range)
# ZEN = np.array([self.rf[2].data, self.rf[0].data, self.rf[1].data])
s_range = self.trim(10, 10, phase='S', isreturn=True)
# LQT_all = np.zeros([ZEN.shape[0], ZEN.shape[1], M_all.shape[2]])
power = np.zeros(inc_range.shape[0])
for i in range(len(inc_range)):
# LQT_all[:, :, i] = M_all[:, :, i].dot(ZEN)
l_comp, _, _ = rotate_zne_lqt(s_range[2].data, s_range[1].data, s_range[0].data, bazi, inc_range[i])
power[i] = np.mean(l_comp ** 2)
# real_inc_idx = seispy.geo.extrema(power, opt='min')
real_inc_idx = np.argmin(power)
# print(real_inc_idx)
real_inc = inc_range[real_inc_idx]
return real_inc
def test_rotate2zne_against_lqt(self):
np.random.seed(789)
z = np.random.random(10)
n = np.random.random(10)
e = np.random.random(10)
bas = [0.0, 14.325, 38.234, 78.1, 90.0, 136.3435, 265.4, 180.0,
351.35, 360.0]
incs = [0.0, 10.325, 32.23, 88.1, 90.0, 132.3435, 245.4, 180.0,
341.35, 360.0]
for ba, inc in itertools.product(bas, incs):
l, q, t = rotate_zne_lqt(z=z, n=n, e=e, ba=ba, inc=inc)
dip_l = (inc % 180.0) - 90.0
if 180 <= inc < 360:
dip_l *= -1.0
dip_q = ((inc + 90) % 180) - 90
if 0 < inc < 90 or 270 <= inc < 360:
dip_q *= -1.0
az_l = ba + 180.0
az_q = ba
# Azimuths flip depending on the incidence angle.
if inc > 180:
az_l += 180
if 90 < inc <= 270:
az_q += 180
z_new, n_new, e_new = rotate2zne(l, az_l, dip_l,
q, az_q, dip_q,
t, ba + 270, 0)
np.testing.assert_allclose(z_new, z)
np.testing.assert_allclose(n_new, n)
np.testing.assert_allclose(e_new, e)
def get_P_rf(self, window_start=-10.0, window_end=100.0,
wl = 0.1, rotation_method = 'RTZ',
type = 'earth_model',plot = False,
decon_type='water_level'):
self.window_start = window_start
self.window_end = window_end
#initialize receiver function time series
len_s = self.window_end - self.window_start
len_i = int(len_s/self.ses3d_seismogram.dt)
if(type == 'earth_model'):
model = TauPyModel(model='pyrolite_5km')
tt = model.get_travel_times(source_depth_in_km = self.eq_depth,
distance_in_degree = self.delta_deg,
phase_list=["P","P660s"])
#just in case there's more than one phase arrival, loop through tt list
for i in range(0,len(tt)):
if tt[i].name == 'P':
p_wave_arrival = tt[i].time
p_i = int(p_wave_arrival / self.ses3d_seismogram.dt)
elif tt[i].name == 'P660s':
self.slowness = tt[i].ray_param * (np.pi/180.0)
elif(type == 'toy_model'):
p_i = np.argmax(self.ses3d_seismogram.trace_z)
p_wave_arrival = p_i * self.ses3d_seismogram.dt
#window seismograms to [P-window_start : P+window_end]
wi_start = int((p_wave_arrival+window_start)/self.ses3d_seismogram.dt)
wi_end = int((p_wave_arrival+window_end)/self.ses3d_seismogram.dt)
trace_x_windowed = self.ses3d_seismogram.trace_x[wi_start:wi_end]
trace_y_windowed = self.ses3d_seismogram.trace_y[wi_start:wi_end]
trace_z_windowed = self.ses3d_seismogram.trace_z[wi_start:wi_end]
#find incidence angle from P wave amplitudes. first rotate to rtz
r_here, t_here = rotate.rotate_ne_rt(trace_x_windowed, trace_y_windowed, self.back_az)
z_here = trace_z_windowed
r_amp = np.amax(r_here)
z_amp = np.amax(z_here)
incidence_angle = np.arctan(r_amp/z_amp) * (180/np.pi)
#rotate
if rotation_method == 'RTZ':
self.r, self.t = rotate.rotate_ne_rt(trace_x_windowed,
trace_y_windowed,
self.back_az)
self.z = trace_z_windowed
elif rotation_method == 'LQT' and type == 'earth_model':
self.z, self.r, self.t = rotate.rotate_zne_lqt(trace_z_windowed,
trace_x_windowed,
trace_y_windowed,
self.back_az,
incidence_angle)
elif rotation_method == 'LQT' and type == 'toy_model':
raise ValueError('rotation method LQT not implemented for type : toy_model')
#deconvolve Z (apprx. P wave pulse) from R
self.time = np.linspace(window_start, window_end, len(self.r))
if decon_type == 'water_level':
self.prf = water_level(self.r,self.z,wl)
elif decon_type == 'damped_lstsq':
self.prf = damped_lstsq(self.r,self.z,damping=0.001)
#plot the two waveforms being deconvolved
if plot == True:
plt.plot(self.time, self.r)
plt.plot(self.time, self.z)
if decon_type=='water_level':
#center of the receiver function on the P arrival
spike = np.exp((-1.0*(self.time)**2)/0.1)
spike_omega = np.fft.fft(spike)
prf_omega = np.fft.fft(self.prf)
prf_shifted = spike_omega*prf_omega
self.prf = np.real(np.fft.ifft(prf_shifted))
#normalize
self.prf /= self.prf.max()
if rotation_method=='LQT':
self.prf *= -1.0
def test_rotate2zne_against_lqt_different_combinations(self):
np.random.seed(101112)
z = np.random.random(10)
n = np.random.random(10)
e = np.random.random(10)
# Exclude coordinate axis.
bas = [14.325, 38.234, 78.1, 136.3435, 265.4, 351.35]
incs = [10.325, 32.23, 88.1, 132.3435, 245.4, 341.35]
success_count = 0
failure_count = 0
for ba, inc in itertools.product(bas, incs):
l, q, t = rotate_zne_lqt(z=z, n=n, e=e, ba=ba, inc=inc)
dip_l = (inc % 180.0) - 90.0
if 180 <= inc < 360:
dip_l *= -1.0
dip_q = ((inc + 90) % 180) - 90
if 0 < inc < 90 or 270 <= inc < 360:
dip_q *= -1.0
az_l = ba + 180.0
az_q = ba
# Azimuths flip depending on the incidence angle.
if inc > 180:
az_l += 180
if 90 < inc <= 270:
az_q += 180
_z = [z, 0, -90, "Z"]
_n = [n, 0, 0, "N"]
_e = [e, 90, 0, "E"]
_l = [l, az_l, dip_l, "L"]
_q = [q, az_q, dip_q, "Q"]
_t = [t, ba + 270, 0, "T"]
# Any three of them (except three horizontal ones) should be
# able to reconstruct ZNE.
for a, b, c in itertools.permutations([_l, _q, _t, _z, _n, _e], 3):
# Three horizontal components are linearly dependent, as are
# Z, Q, and L.
if a[2] == b[2] == c[2] == 0 or \
set([_i[3] for _i in (a, b, c)]) == \
set(["Z", "Q", "L"]):
with self.assertRaises(ValueError) as err:
rotate2zne(a[0], a[1], a[2],
b[0], b[1], b[2],
c[0], c[1], c[2])
self.assertTrue(err.exception.args[0].startswith(
"The given directions are not linearly independent, "
"at least within numerical precision. Determinant of "
"the base change matrix:"))
failure_count += 1
continue
z_new, n_new, e_new = rotate2zne(a[0], a[1], a[2],
b[0], b[1], b[2],
c[0], c[1], c[2])
np.testing.assert_allclose(z_new, z)
np.testing.assert_allclose(n_new, n)
np.testing.assert_allclose(e_new, e)
success_count += 1
# Make sure it actually tested all combinations.
self.assertEqual(success_count, 3888)
# Also the linearly dependent variants.
self.assertEqual(failure_count, 432)
def get_P_rf(self,
window_start=-10.0,
window_end=100.0,
wl=0.1,
rotation_method='RTZ',
type='earth_model',
plot=False,
decon_type='water_level'):
self.window_start = window_start
self.window_end = window_end
#initialize receiver function time series
len_s = self.window_end - self.window_start
len_i = int(len_s / self.ses3d_seismogram.dt)
if (type == 'earth_model'):
model = TauPyModel(model='pyrolite_5km')
tt = model.get_travel_times(source_depth_in_km=self.eq_depth,
distance_in_degree=self.delta_deg,
phase_list=["P", "P660s"])
#just in case there's more than one phase arrival, loop through tt list
for i in range(0, len(tt)):
if tt[i].name == 'P':
p_wave_arrival = tt[i].time
p_i = int(p_wave_arrival / self.ses3d_seismogram.dt)
elif tt[i].name == 'P660s':
self.slowness = tt[i].ray_param * (np.pi / 180.0)
elif (type == 'toy_model'):
p_i = np.argmax(self.ses3d_seismogram.trace_z)
p_wave_arrival = p_i * self.ses3d_seismogram.dt
#window seismograms to [P-window_start : P+window_end]
wi_start = int(
(p_wave_arrival + window_start) / self.ses3d_seismogram.dt)
wi_end = int((p_wave_arrival + window_end) / self.ses3d_seismogram.dt)
trace_x_windowed = self.ses3d_seismogram.trace_x[wi_start:wi_end]
trace_y_windowed = self.ses3d_seismogram.trace_y[wi_start:wi_end]
trace_z_windowed = self.ses3d_seismogram.trace_z[wi_start:wi_end]
#find incidence angle from P wave amplitudes. first rotate to rtz
r_here, t_here = rotate.rotate_ne_rt(trace_x_windowed,
trace_y_windowed, self.back_az)
z_here = trace_z_windowed
r_amp = np.amax(r_here)
z_amp = np.amax(z_here)
incidence_angle = np.arctan(r_amp / z_amp) * (180 / np.pi)
#rotate
if rotation_method == 'RTZ':
self.r, self.t = rotate.rotate_ne_rt(trace_x_windowed,
trace_y_windowed,
self.back_az)
self.z = trace_z_windowed
elif rotation_method == 'LQT' and type == 'earth_model':
self.z, self.r, self.t = rotate.rotate_zne_lqt(
trace_z_windowed, trace_x_windowed, trace_y_windowed,
self.back_az, incidence_angle)
elif rotation_method == 'LQT' and type == 'toy_model':
raise ValueError(
'rotation method LQT not implemented for type : toy_model')
#deconvolve Z (apprx. P wave pulse) from R
self.time = np.linspace(window_start, window_end, len(self.r))
if decon_type == 'water_level':
self.prf = water_level(self.r, self.z, wl)
elif decon_type == 'damped_lstsq':
self.prf = damped_lstsq(self.r, self.z, damping=0.001)
#plot the two waveforms being deconvolved
if plot == True:
plt.plot(self.time, self.r)
plt.plot(self.time, self.z)
if decon_type == 'water_level':
#center of the receiver function on the P arrival
spike = np.exp((-1.0 * (self.time)**2) / 0.1)
spike_omega = np.fft.fft(spike)
prf_omega = np.fft.fft(self.prf)
prf_shifted = spike_omega * prf_omega
self.prf = np.real(np.fft.ifft(prf_shifted))
#normalize
self.prf /= self.prf.max()
if rotation_method == 'LQT':
self.prf *= -1.0
