def depth2time(self, trace, point, dz, dt):
"""
Converts a data array in the depth domain to the time
domain.
Args:
data (array, 1d): A 1D numpy array.
point (float): distance along transect corresponding to
the trace location. Not actually used, as this model
assumes a constant velocity. Kept for consistency with other
velocity methods.
dz (float): The sample interval of the input data.
dt (float): The sample interval of the converted
data.
"""
distance = [(p - point)**2.0 for p in self.coords]
idx = np.argmin(distance)
profile = self.data[idx]
seismic = np.array(trace)
# HACK TO MAKE FAKE VELOCITIES
velocity = np.ones(profile.data.size) * 2000.0
samp = profile.stats["sampling_rate"]
vel_z = np.arange(velocity.size) * 1.0 / samp
z = np.arange(seismic.size) * dz
velocity = np.interp(z, vel_z, velocity, velocity[0], velocity[-1])
t = np.arange(self.range[0], self.range[1], dt)
data = depth_to_time(seismic, velocity, z, t, mode="cubic")
return data
def depth2time(self, trace, point, dz, dt):
"""
Converts a data array in the depth domain to the time
domain.
Args:
data (array, 1d): A 1D numpy array.
point (float): distance along transect corresponding to
the trace location. Not actually used, as this model
assumes a constant velocity. Kept for consistency with other
velocity methods.
dz (float): The sample interval of the input data.
dt (float): The sample interval of the converted
data.
"""
distance = [(p - point)**2.0 for p in self.coords]
idx = np.argmin(distance)
profile = self.data[idx]
seismic = np.array(trace)
# HACK TO MAKE FAKE VELOCITIES
velocity = np.ones(profile.data.size) * 2000.0
samp = profile.stats["sampling_rate"]
vel_z = np.arange(velocity.size) * 1.0 / samp
z = np.arange(seismic.size)*dz
velocity = np.interp(z, vel_z, velocity, velocity[0], velocity[-1])
t = np.arange(self.range[0], self.range[1], dt)
data = depth_to_time(seismic, velocity, z, t, mode="cubic")
return data
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 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)
"""plt.figure()
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 )
"""plt.figure()
def depth2time(self, data, point, dz, dt):
"""
Converts a data array in the depth domain to the time
domain.
Args:
data (array, 1d): A 1D numpy array.
point (float): distance along transect corresponding to
the trace location. Not actually used, as this model
assumes a constant velocity. Kept for consistency with other
velocity methods.
dz (float): The sample interval of the input data.
dt (float): The sample interval of the converted
data.
"""
velocity = self.velocity
return depth_to_time(data, np.ones(data.size) * velocity, dz, dt)
def depth2time(self, data, point, dz, dt):
"""
Converts a data array in the depth domain to the time
domain.
Args:
data (array, 1d): A 1D numpy array.
point (float): distance along transect corresponding to
the trace location. Not actually used, as this model
assumes a constant velocity. Kept for consistency with other
velocity methods.
dz (float): The sample interval of the input data.
dt (float): The sample interval of the converted
data.
"""
velocity = self.velocity
return depth_to_time(data, np.ones(data.size)*velocity, dz, dt)
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)
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 = np.floor( ( ( 49* dz ) / 1500.0 ) /
dt )
output = depth_to_time( data, vmodel, dz, dt )
self.assertTrue( ( output[face_change+1,50] -
output[ face_change, 50 ] ) ==100 )