def hessian_wrap(model_pert_in, model_pert_out):
"""
@Params
model_pert_in: input numpy array
model_pert_out: output numpy array
"""
model_pert_out *= 0.
DevitoOperators.td_born_hessian(model_pert_in=model_pert_in,
model_pert_out=model_pert_out,
src_coords=src_coord,
vel=vel,
geometry=geometry,
solver=solver,
params=params)
##################################################################################################
# This part of the code generates the forward data using the two models and computes the residual
##################################################################################################
dt = v.critical_dt
# Allocate numpy arrays to store data
data = np.zeros(shape=(params["Ns"], params["Nt"], params["Nr"]),
dtype=np.float32)
data1 = data * 0
# Call wave_propagator_forward with appropriate arguments
t_start = time.time()
DevitoOperators.wave_propagator_forward(data=data,
src_coords=src_coord,
vel=v,
geometry=geometry,
solver=solver,
params=params)
t_end = time.time()
print("\n Time to model shots for v took ", t_end - t_start, " sec.")
t_start = time.time()
DevitoOperators.wave_propagator_forward(data=data1,
src_coords=src_coord,
vel=v1,
geometry=geometry,
solver=solver,
params=params)
t_end = time.time()
print("\n Time to model shots for v1 took ", t_end - t_start, " sec.")
model_pert_out=model_pert_out,
src_coords=src_coord,
vel=vel,
geometry=geometry,
solver=solver,
params=params)
# Create rhs for inversion
dm_adjoint_image = np.zeros((params["Nt"], params["Nx"], params["Nz"]),
dtype=np.float32)
t_start = time.time()
DevitoOperators.td_born_hessian(model_pert_in=dm,
model_pert_out=dm_adjoint_image,
src_coords=src_coord,
vel=vel,
geometry=geometry,
solver=solver,
params=params)
t_end = time.time()
print("\nCreate adjoint image took ", t_end - t_start, " sec")
# Run the inversion
niter = 100
dm_invert, resid = conjugate_gradient(hessian_wrap,
rhs=dm_adjoint_image,
x0=None,
niter=niter,
printobj=False)
# Save results