def rpp_t(self, dt):
if self.domain == 'time':
if dt == self.dz:
return self.rpp
else:
raise Exception('sampling mismatch')
vpt = depth_to_time(self.vp, self.vp, self.dz, dt)
times = np.arange(vpt.shape[0]) * dt
vst = depth_to_time(self.vs, self.vp, self.dz, times)
rhot = depth_to_time(self.rho, self.vp, self.dz, times)
rpp = reflectivity_array(vpt, vst, rhot, self.theta)
return rpp
def test_recip(self):
data = np.zeros((100, 100))
vmodel = np.zeros((100, 100))
data[0:50, :] += 100.0
data[50:, :] += 200.0
vmodel[0:50, :] += 1500.0
vmodel[50:, :] += 1800.0
dt = 0.001
dz = 1.
out1 = depth_to_time(data, vmodel, dz, dt)
v2 = depth_to_time(vmodel, vmodel, dz, dt)
out2 = time_to_depth(out1, v2, dt, dz)
def test_recip(self):
data = np.zeros((100, 100))
vmodel = np.zeros((100, 100))
data[0:50, :] += 100.0
data[50:, :] += 200.0
vmodel[0:50, :] += 1500.0
vmodel[50:, :] += 1800.0
dt = 0.001
dz = 1.
out1 = depth_to_time(data, vmodel, dz, dt)
v2 = depth_to_time(vmodel, vmodel, dz, dt)
out2 = time_to_depth(out1, v2, dt, dz)
def rpp_t(self, dt):
if self.domain == 'time':
if dt == self.dz:
return self.rpp
else:
raise Exception('sampling mismatch')
vpt = depth_to_time(self.vp, self.vp, self.dz, dt)
times = np.arange(vpt.shape[0]) * dt
vst = depth_to_time(self.vs, self.vp, self.dz, times)
rhot = depth_to_time(self.rho, self.vp, self.dz, times)
rpp = reflectivity_array(vpt, vst, rhot, self.theta)
return rpp
def test_depthtotime(self):
data = np.zeros((100, 100))
vmodel = np.zeros((100, 100))
data[0:50, :] += 100.0
data[50:, :] += 200.0
vmodel[0:50, :] += 1500.0
vmodel[50:, :] += 3000.0
dt = 0.001
dz = 1.0
face_change = np.floor(((49 * dz) / 1500.0) / dt)
output = depth_to_time(data, vmodel, dz, dt, twt=False)
self.assertTrue((output[face_change + 1, 50] - output[face_change, 50]) == 100)
def test_depthtotime(self):
data = np.zeros((100, 100))
vmodel = np.zeros((100, 100))
data[0:50, :] += 100.0
data[50:, :] += 200.0
vmodel[0:50, :] += 1500.0
vmodel[50:, :] += 3000.0
dt = 0.001
dz = 1.0
face_change = int(np.floor(((49 * dz) / 1500.0) / dt))
output = depth_to_time(data, vmodel, dz, dt, twt=False)
self.assertTrue((output[face_change + 1, 50] -
output[face_change, 50]) == 100)
def depth2time(self, dt, samples=None):
if self.units == 'time':
raise ValueError
vp_data = self.vp_data(samples=samples)
data = self.get_data(samples=samples)
indices = np.array([np.arange(vp_data.shape[0])
for i in range(vp_data.shape[1])])\
.transpose()
dz = self.depth / data.shape[0]
time_index = depth_to_time(indices, vp_data, dz, dt).astype(int)
self.image = np.asarray([data[time_index[:, i], i, :]
for i in range(data.shape[1])])\
.transpose(1, 0, 2)
def depth2time(self, dt, samples=None):
if self.units == 'time':
raise ValueError
vp_data = self.vp_data(samples=samples)
data = self.get_data(samples=samples)
indices = np.array([np.arange(vp_data.shape[0])
for i in range(vp_data.shape[1])])\
.transpose()
dz = self.depth / data.shape[0]
time_index = depth_to_time(indices, vp_data,
dz, dt).astype(int)
self.image = np.asarray([data[time_index[:, i], i, :]
for i in range(data.shape[1])])\
.transpose(1, 0, 2)
def to_time(self, v=None, dt=1e-12):
"""
Convert model to time domain.
Args:
v (ndarray): Interval velocities. Must be same shape as model. If
None, then the model's own permittivities are used.
dt (float): The new time step or sampling interval, in seconds.
Returns:
Model. The time-converted model, as a jeepr.Model instance.
"""
if self.domain != 'depth':
raise ModelError('You can only time-convert a depth model.')
params = copy.deepcopy(self.__dict__)
if v is None:
v = self.velocity
arr = depth_to_time(self, v, dz=self.dz, dt=dt)
basis = utils.srange(0, dt, arr.shape[0])
params['domain'] = 'time'
params['dz'] = 0
params['dt'] = dt
return Model(arr, params), basis
def run_script(json_payload):
# parse json
fs_model = FluidSub1D.from_json(json_payload["earth_model"])
seismic = Seismic.from_json(json_payload["seismic"])
# extract rock properties
vp, vs, rho = (fs_model.vp, fs_model.vs, fs_model.rho)
vp_sub, vs_sub, rho_sub = fs_model.smith_sub()
# convert to time
dt = seismic.dt
dz = fs_model.dz
z = fs_model.z
# use indices so we only run the algorithm once
index = np.arange(vp.size, dtype=int)
t_index = depth_to_time(index, vp, dz, dt).astype(int)
sub_t_index = depth_to_time(index, vp_sub, dz, dt).astype(int)
vp_t, vs_t, rho_t = (vp[t_index], vs[t_index], rho[t_index])
vp_sub_t, vs_sub_t, rho_sub_t = (vp_sub[sub_t_index], vs_sub[sub_t_index],
rho_sub[sub_t_index])
# calculate reflectivities
rpp = np.nan_to_num(
np.array([
zoep(vp_t[:-1], vs_t[:-1], rho_t[:-1], vp_t[1:], vs_t[1:],
rho_t[1:], float(theta)) for theta in seismic.theta[::-1]
]).T)
rpp_sub = np.nan_to_num(
np.array([
zoep(vp_sub_t[:-1], vs_sub_t[:-1],
rho_sub_t[:-1], vp_sub_t[1:], vs_sub_t[1:], rho_sub_t[1:],
float(theta)) for theta in seismic.theta[::-1]
]).T)
# trim to be the same size
n = min(rpp.shape[0], rpp_sub.shape[0])
rpp = rpp[:n, :]
rpp_sub = rpp_sub[:n, :]
t = np.arange(n) * dt
# create synthetic seismic
traces = np.squeeze(do_convolve(seismic.src, rpp[:, np.newaxis, :]))
sub_traces = np.squeeze(do_convolve(seismic.src, rpp_sub[:,
np.newaxis, :]))
output = {
"vp":
vp.tolist(),
"vs":
vs.tolist(),
"rho":
rho.tolist(),
"vp_sub":
vp_sub.tolist(),
"vs_sub":
vs_sub.tolist(),
"rho_sub":
rho_sub.tolist(),
"synth":
np.nan_to_num(traces).T.tolist(),
"synth_sub":
np.nan_to_num(sub_traces).T.tolist(),
"theta":
seismic.theta,
"rpp":
rpp[:, 0].tolist(),
"rpp_sub":
rpp_sub[:, 0].tolist(),
"t_lim": [float(np.amin(t)), float(np.amax(t))],
"z_lim": [float(np.amin(z)), float(np.amax(z))],
"vp_lim": [float(np.amin((vp, vp_sub))),
float(np.amax((vp, vp_sub)))],
"vs_lim": [float(np.amin((vs, vs_sub))),
float(np.amax((vs, vs_sub)))],
"rho_lim":
[float(np.amin((rho, rho_sub))),
float(np.amax((rho, rho_sub)))],
"rpp_lim":
[float(np.amin((rpp, rpp_sub))),
float(np.amax((rpp, rpp_sub)))],
"synth_lim": [
float(np.amin((traces, sub_traces))),
float(np.amax((traces, sub_traces)))
],
"dt":
dt,
"dz":
dz
}
return output
def run_script(json_payload):
# parse json
fs_model = FluidSub1D.from_json(json_payload["earth_model"])
seismic = Seismic.from_json(json_payload["seismic"])
# extract rock properties
vp, vs, rho = (fs_model.vp, fs_model.vs, fs_model.rho)
vp_sub, vs_sub, rho_sub = fs_model.smith_sub()
# convert to time
dt = seismic.dt
dz = fs_model.dz
z = fs_model.z
# use indices so we only run the algorithm once
index = np.arange(vp.size, dtype=int)
t_index = depth_to_time(index, vp, dz, dt).astype(int)
sub_t_index = depth_to_time(index, vp_sub, dz, dt).astype(int)
vp_t, vs_t, rho_t = (vp[t_index], vs[t_index], rho[t_index])
vp_sub_t, vs_sub_t, rho_sub_t = (vp_sub[sub_t_index],
vs_sub[sub_t_index],
rho_sub[sub_t_index])
# calculate reflectivities
rpp = np.nan_to_num(np.array([zoep(vp_t[:-1], vs_t[:-1], rho_t[:-1],
vp_t[1:], vs_t[1:], rho_t[1:],
float(theta))
for theta in seismic.theta[::-1]]).T)
rpp_sub = np.nan_to_num(np.array([zoep(vp_sub_t[:-1], vs_sub_t[:-1],
rho_sub_t[:-1],
vp_sub_t[1:], vs_sub_t[1:],
rho_sub_t[1:], float(theta))
for theta in seismic.theta[::-1]]).T)
# trim to be the same size
n = min(rpp.shape[0], rpp_sub.shape[0])
rpp = rpp[:n, :]
rpp_sub = rpp_sub[:n, :]
t = np.arange(n) * dt
# create synthetic seismic
traces = np.squeeze(do_convolve(seismic.src, rpp[:, np.newaxis, :]))
sub_traces = np.squeeze(do_convolve(seismic.src,
rpp_sub[:, np.newaxis, :]))
output = {"vp": vp.tolist(), "vs": vs.tolist(),
"rho": rho.tolist(), "vp_sub": vp_sub.tolist(),
"vs_sub": vs_sub.tolist(), "rho_sub": rho_sub.tolist(),
"synth": np.nan_to_num(traces).T.tolist(),
"synth_sub": np.nan_to_num(sub_traces).T.tolist(),
"theta": seismic.theta,
"rpp": rpp[:, 0].tolist(),
"rpp_sub": rpp_sub[:, 0].tolist(),
"t_lim": [float(np.amin(t)), float(np.amax(t))],
"z_lim": [float(np.amin(z)), float(np.amax(z))],
"vp_lim": [float(np.amin((vp, vp_sub))),
float(np.amax((vp, vp_sub)))],
"vs_lim": [float(np.amin((vs, vs_sub))),
float(np.amax((vs, vs_sub)))],
"rho_lim": [float(np.amin((rho, rho_sub))),
float(np.amax((rho, rho_sub)))],
"rpp_lim": [float(np.amin((rpp, rpp_sub))),
float(np.amax((rpp, rpp_sub)))],
"synth_lim": [float(np.amin((traces, sub_traces))),
float(np.amax((traces, sub_traces)))],
"dt": dt,
"dz": dz}
return output