def forward_freq_modeling(model, src_coords, wavelet, rec_coords, freq, space_order=8, dt=None, factor=None, free_surface=False):
# Forward modeling with on-the-fly DFT of forward wavefields
clear_cache()
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
freq_dim = Dimension(name='freq_dim')
time = model.grid.time_dim
if factor is None:
factor = int(1 / (dt*4*np.max(freq)))
tsave = ConditionalDimension(name='tsave', parent=model.grid.time_dim, factor=factor)
if factor==1:
tsave = time
else:
tsave = ConditionalDimension(name='tsave', parent=model.grid.time_dim, factor=factor)
print("DFT subsampling factor: ", factor)
# Create wavefields
nfreq = freq.shape[0]
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=space_order)
f = Function(name='f', dimensions=(freq_dim,), shape=(nfreq,))
f.data[:] = freq[:]
ufr = Function(name='ufr', dimensions=(freq_dim,) + u.indices[1:], shape=(nfreq,) + model.shape_domain)
ufi = Function(name='ufi', dimensions=(freq_dim,) + u.indices[1:], shape=(nfreq,) + model.shape_domain)
ulaplace, rho = acoustic_laplacian(u, rho)
# Set up PDE and rearrange
stencil = damp * (2.0 * u - damp * u.backward + dt**2 * rho / m * ulaplace)
expression = [Eq(u.forward, stencil)]
expression += [Eq(ufr, ufr + factor*u*cos(2*np.pi*f*tsave*factor*dt))]
expression += [Eq(ufi, ufi - factor*u*sin(2*np.pi*f*tsave*factor*dt))]
# Source symbol with input wavelet
src = PointSource(name='src', grid=model.grid, ntime=nt, coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward, expr=src * dt**2 / m)
# Data is sampled at receiver locations
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
rec_term = rec.interpolate(expr=u)
# Create operator and run
expression += src_term + rec_term
# Free surface
if free_surface is True:
expression += freesurface(u, space_order//2, model.nbpml)
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression, subs=subs, dse='advanced', dle='advanced')
cf = op.cfunction
op()
return rec.data, ufr, ufi
def adjoint_modeling(model, src_coords, rec_coords, rec_data, space_order=8, free_surface=False, dt=None):
clear_cache()
# If wavelet is file, read it
if isinstance(rec_data, str):
rec_data = np.load(rec_data)
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
# Create the adjoint wavefield
if src_coords is not None:
v = TimeFunction(name="v", grid=model.grid, time_order=2, space_order=space_order)
else:
v = TimeFunction(name="v", grid=model.grid, time_order=2, space_order=space_order, save=nt)
# Set up PDE and rearrange
vlaplace, rho = acoustic_laplacian(v, rho)
# Input data is wavefield
full_q = 0
if isinstance(rec_data, TimeFunction):
wf_rec = TimeFunction(name='wf_rec', grid=model.grid, time_order=2, space_order=space_order, save=nt)
wf_rec._data = rec_data._data
full_q = wf_rec
stencil = damp * (2.0 * v - damp * v.forward + dt**2 * rho / m * (vlaplace + full_q))
expression = [Eq(v.backward, stencil)]
# Free surface
if free_surface is True:
expression += freesurface(v, space_order//2, model.nbpml, forward=False)
# Adjoint source is injected at receiver locations
if rec_coords is not None:
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
rec.data[:] = rec_data[:]
adj_src = rec.inject(field=v.backward, expr=rec * rho * dt**2 / m)
expression += adj_src
# Data is sampled at source locations
if src_coords is not None:
src = PointSource(name='src', grid=model.grid, ntime=nt, coordinates=src_coords)
adj_rec = src.interpolate(expr=v)
expression += adj_rec
# Create operator and run
subs = model.spacing_map
subs[v.grid.time_dim.spacing] = dt
op = Operator(expression, subs=subs, dse='advanced', dle='advanced')
cf = op.cfunction
summary = op.apply()
if src_coords is None:
return v, summary
else:
return src.data, summary
def forward_born(model, src_coords, wavelet, rec_coords, space_order=8, nb=40, isic=False, dt=None, free_surface=False):
clear_cache()
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, dm, damp = model.m, model.rho, model.dm, model.damp
# Create the forward and linearized wavefield
u = TimeFunction(name="u", grid=model.grid, time_order=2, space_order=space_order)
du = TimeFunction(name="du", grid=model.grid, time_order=2, space_order=space_order)
if len(model.shape) == 2:
x,y = u.space_dimensions
else:
x,y,z = u.space_dimensions
# Set up PDEs and rearrange
ulaplace, rho = acoustic_laplacian(u, rho)
dulaplace, _ = acoustic_laplacian(du, rho)
if isic:
# Sum ((u.dx * d, / rho).dx for x in dimensions)
# space_order//2 so that u.dx.dx has the same radius as u.laplace
du_aux = sum([first_derivative(first_derivative(u, dim=d, fd_order=space_order//2) * dm / rho, fd_order=space_order//2, dim=d)
for d in u.space_dimensions])
lin_source = dm /rho * u.dt2 * m - du_aux
else:
lin_source = dm / rho * u.dt2
stencil_u = damp * (2.0 * u - damp * u.backward + dt**2 * rho / m * ulaplace)
stencil_du = damp * (2.0 * du - damp * du.backward + dt**2 * rho / m * (dulaplace - lin_source))
expression_u = [Eq(u.forward, stencil_u)]
expression_du = [Eq(du.forward, stencil_du)]
# Define source symbol with wavelet
src = PointSource(name='src', grid=model.grid, ntime=nt, coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward, expr=src * rho * dt**2 / m)
# Define receiver symbol
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
rec_term = rec.interpolate(expr=du)
expression = expression_u + expression_du + src_term + rec_term
# Free surface
if free_surface is True:
expression += freesurface(u, space_order//2, model.nbpml)
expression += freesurface(du, space_order//2, model.nbpml)
# Create operator and run
set_log_level('ERROR')
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression, subs=subs, dse='advanced', dle='advanced')
op()
return rec.data
def adjoint_modeling(model,
src_coords,
rec_coords,
rec_data,
space_order=8,
nb=40,
dt=None):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, damp = model.m, model.damp
# Create the adjoint wavefield
v = TimeFunction(name="v",
grid=model.grid,
time_order=2,
space_order=space_order)
# Set up PDE and rearrange
eqn = m * v.dt2 - v.laplace - damp * v.dt
stencil = solve(eqn, v.backward)[0]
expression = [Eq(v.backward, stencil)]
# Adjoint source is injected at receiver locations
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec.data[:] = rec_data[:]
adj_src = rec.inject(field=v.backward,
offset=model.nbpml,
expr=rec * dt**2 / m)
# Data is sampled at source locations
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
adj_rec = src.interpolate(expr=v, offset=model.nbpml)
# Create operator and run
set_log_level('ERROR')
expression += adj_src + adj_rec
op = Operator(expression,
subs=model.spacing_map,
dse='advanced',
dle='advanced',
name="Backward%s" % randint(1e5))
op(dt=dt)
return src.data
def __init__(self, shots, shape, origin, spacing, m0, dm, noise=0.0, device='cpu', sequential=False):
super(wave_solver, self).__init__()
self.forward_born = ForwardBorn()
self.noise = noise
self.device = device
self.dm = dm
self.spacing = spacing
epsilon = np.sqrt(noise)*np.random.randn(shots.shape[0], shots.shape[1], shots.shape[2])
self.shots = shots + epsilon
self.model0 = Model(shape=shape, origin=origin, spacing=spacing, vp=1/np.sqrt(m0),
nbpml=40)
self.T = self.mute_top(dm, mute_end=10, length=5)
t0 = 0.
tn = 1500.0
dt = self.model0.critical_dt
nt = int(1 + (tn-t0) / dt)
self.time_range = np.linspace(t0,tn,nt)
self.f0 = 0.030
self.nsrc = 205
self.nsimsrc = 205
self.src = RickerSource(name='src', grid=self.model0.grid, f0=self.f0, time=self.time_range,
npoint=self.nsimsrc)
self.src.coordinates.data[:,0] = np.linspace(0, self.model0.domain_size[0], num=self.nsimsrc)
self.src.coordinates.data[:,-1] = 2.0*spacing[1]
nrec = 410
self.rec = Receiver(name='rec', grid=self.model0.grid, npoint=nrec, ntime=nt)
self.rec.coordinates.data[:, 0] = np.linspace(0, self.model0.domain_size[0], num=nrec)
self.rec.coordinates.data[:, 1] = 2.0*spacing[1]
self.seq_src_idx = 0
self.sequential = sequential
def adjoint_freq_born(model,
rec_coords,
rec_data,
freq,
ufr,
ufi,
space_order=8,
nb=40,
dt=None,
isic=False,
factor=None):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, damp = model.m, model.damp
nfreq = ufr.shape[0]
time = model.grid.time_dim
if factor is None:
factor = int(1 / (dt * 4 * np.max(freq)))
tsave = ConditionalDimension(name='tsave',
parent=model.grid.time_dim,
factor=factor)
if factor == 1:
tsave = time
else:
tsave = ConditionalDimension(name='tsave',
parent=model.grid.time_dim,
factor=factor)
dtf = factor * dt
ntf = factor / nt
print("DFT subsampling factor: ", factor)
# Create the forward and adjoint wavefield
v = TimeFunction(name='v',
grid=model.grid,
time_order=2,
space_order=space_order)
f = Function(name='f', dimensions=(ufr.indices[0], ), shape=(nfreq, ))
f.data[:] = freq[:]
gradient = Function(name="gradient", grid=model.grid)
# Set up PDE and rearrange
eqn = m * v.dt2 - v.laplace - damp * v.dt
stencil = solve(eqn, v.backward, simplify=False, rational=False)[0]
expression = [Eq(v.backward, stencil)]
# Data at receiver locations as adjoint source
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec.data[:] = rec_data[:]
adj_src = rec.inject(field=v.backward,
offset=model.nbpml,
expr=rec * dt**2 / m)
# Gradient update
if isic is True:
if len(model.shape) == 2:
gradient_update = [
Eq(
gradient, gradient + (2 * np.pi * f)**2 * ntf *
(ufr * cos(2 * np.pi * f * tsave * dtf) -
ufi * sin(2 * np.pi * f * tsave * dtf)) * v * model.m -
(ufr.dx * cos(2 * np.pi * f * tsave * dtf) -
ufi.dx * sin(2 * np.pi * f * tsave * dtf)) * v.dx * ntf -
(ufr.dy * cos(2 * np.pi * f * tsave * dtf) -
ufi.dy * sin(2 * np.pi * f * tsave * dtf)) * v.dy * ntf)
]
else:
gradient_update = [
Eq(
gradient, gradient + (2 * np.pi * f)**2 * ntf *
(ufr * cos(2 * np.pi * f * tsave * dtf) -
ufi * sin(2 * np.pi * f * tsave * dtf)) * v * model.m -
(ufr.dx * cos(2 * np.pi * f * tsave * dtf) -
ufi.dx * sin(2 * np.pi * f * tsave * dtf)) * v.dx * ntf -
(ufr.dy * cos(2 * np.pi * f * tsave * dtf) -
ufi.dy * sin(2 * np.pi * f * tsave * dtf)) * v.dy * ntf -
(ufr.dz * cos(2 * np.pi * f * tsave * dtf) -
ufi.dz * sin(2 * np.pi * f * tsave * dtf)) * v.dz * ntf)
]
else:
gradient_update = [
Eq(
gradient, gradient + (2 * np.pi * f)**2 / nt *
(ufr * cos(2 * np.pi * f * tsave * dtf) -
ufi * sin(2 * np.pi * f * tsave * dtf)) * v)
]
# Create operator and run
set_log_level('ERROR')
expression += adj_src + gradient_update
subs = model.spacing_map
subs[v.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse='advanced',
dle='advanced',
name="Gradient%s" % randint(1e5))
op()
clear_cache()
return gradient.data
def forward_modeling(model,
src_coords,
wavelet,
rec_coords,
save=False,
space_order=8,
nb=40,
free_surface=False,
op_return=False,
dt=None):
clear_cache()
# If wavelet is file, read it
if isinstance(wavelet, str):
wavelet = np.load(wavelet)
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
# Create the forward wavefield
if save is False and rec_coords is not None:
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order)
else:
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order,
save=nt)
# Set up PDE and rearrange
ulaplace, rho = acoustic_laplacian(u, rho)
H = symbols('H')
eqn = m / rho * u.dt2 - H + damp * u.dt
# Input source is wavefield
if isinstance(wavelet, TimeFunction):
wf_src = TimeFunction(name='wf_src',
grid=model.grid,
time_order=2,
space_order=space_order,
save=nt)
wf_src._data = wavelet._data
eqn -= wf_src
# Rearrange expression
stencil = solve(eqn, u.forward, simplify=False, rational=False)[0]
expression = [Eq(u.forward, stencil.subs({H: ulaplace}))]
# Free surface
if free_surface is True:
fs = DefaultDimension(name="fs", default_value=int(space_order / 2))
expression += [
Eq(u.forward.subs({u.indices[-1]: model.nbpml - fs - 1}),
-u.forward.subs({u.indices[-1]: model.nbpml + fs + 1}))
]
# Source symbol with input wavelet
if src_coords is not None:
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward,
offset=model.nbpml,
expr=src * rho * dt**2 / m)
expression += src_term
# Data is sampled at receiver locations
if rec_coords is not None:
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec_term = rec.interpolate(expr=u, offset=model.nbpml)
expression += rec_term
# Create operator and run
set_log_level('ERROR')
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse='advanced',
dle='advanced',
name="Forward%s" % randint(1e5))
if op_return is False:
op()
if rec_coords is None:
return u
else:
return rec.data, u
else:
return op
def forward_freq_modeling(model,
src_coords,
wavelet,
rec_coords,
freq,
space_order=8,
nb=40,
dt=None,
factor=None):
# Forward modeling with on-the-fly DFT of forward wavefields
clear_cache()
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, damp = model.m, model.damp
freq_dim = Dimension(name='freq_dim')
time = model.grid.time_dim
if factor is None:
factor = int(1 / (dt * 4 * np.max(freq)))
tsave = ConditionalDimension(name='tsave',
parent=model.grid.time_dim,
factor=factor)
if factor == 1:
tsave = time
else:
tsave = ConditionalDimension(name='tsave',
parent=model.grid.time_dim,
factor=factor)
print("DFT subsampling factor: ", factor)
# Create wavefields
nfreq = freq.shape[0]
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order)
f = Function(name='f', dimensions=(freq_dim, ), shape=(nfreq, ))
f.data[:] = freq[:]
ufr = Function(name='ufr',
dimensions=(freq_dim, ) + u.indices[1:],
shape=(nfreq, ) + model.shape_domain)
ufi = Function(name='ufi',
dimensions=(freq_dim, ) + u.indices[1:],
shape=(nfreq, ) + model.shape_domain)
# Set up PDE and rearrange
eqn = m * u.dt2 - u.laplace + damp * u.dt
stencil = solve(eqn, u.forward, simplify=False, rational=False)[0]
expression = [Eq(u.forward, stencil)]
expression += [
Eq(ufr, ufr + factor * u * cos(2 * np.pi * f * tsave * factor * dt))
]
expression += [
Eq(ufi, ufi - factor * u * sin(2 * np.pi * f * tsave * factor * dt))
]
# Source symbol with input wavelet
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward,
offset=model.nbpml,
expr=src * dt**2 / m)
# Data is sampled at receiver locations
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec_term = rec.interpolate(expr=u, offset=model.nbpml)
# Create operator and run
set_log_level('ERROR')
expression += src_term + rec_term
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse='advanced',
dle='advanced',
name="Forward%s" % randint(1e5))
op()
return rec.data, ufr, ufi
def forward_born(model,
src_coords,
wavelet,
rec_coords,
space_order=8,
nb=40,
isic=False,
dt=None):
clear_cache()
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, dm, damp = model.m, model.rho, model.dm, model.damp
# Create the forward and linearized wavefield
u = TimeFunction(name="u",
grid=model.grid,
time_order=2,
space_order=space_order)
du = TimeFunction(name="du",
grid=model.grid,
time_order=2,
space_order=space_order)
if len(model.shape) == 2:
x, y = u.space_dimensions
else:
x, y, z = u.space_dimensions
# Set up PDEs and rearrange
ulaplace, rho = acoustic_laplacian(u, rho)
dulaplace, _ = acoustic_laplacian(du, rho)
H = symbols('H')
S = symbols('S')
eqn = m / rho * u.dt2 - H + damp * u.dt
stencil1 = solve(eqn, u.forward, simplify=False, rational=False)[0]
eqn_lin = m / rho * du.dt2 - H + damp * du.dt + S
if isic:
# Sum ((u.dx * d, / rho).dx for x in dimensions)
# space_order//2 so that u.dx.dx has the same radius as u.laplace
du_aux = sum([
first_derivative(
first_derivative(u, dim=d, order=space_order // 2) * dm / rho,
order=space_order // 2,
dim=d) for d in u.space_dimensions
])
lin_source = dm / rho * u.dt2 * m - du_aux
else:
lin_source = dm / rho * u.dt2
stencil2 = solve(eqn_lin, du.forward, simplify=False, rational=False)[0]
expression_u = [Eq(u.forward, stencil1.subs({H: ulaplace}))]
expression_du = [
Eq(du.forward, stencil2.subs({
H: dulaplace,
S: lin_source
}))
]
# Define source symbol with wavelet
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward,
offset=model.nbpml,
expr=src * rho * dt**2 / m)
# Define receiver symbol
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec_term = rec.interpolate(expr=du, offset=model.nbpml)
# Create operator and run
set_log_level('ERROR')
expression = expression_u + src_term + expression_du + rec_term
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse='advanced',
dle='advanced',
name="Born%s" % randint(1e5))
op()
return rec.data
def adjoint_born(model,
rec_coords,
rec_data,
u=None,
op_forward=None,
is_residual=False,
space_order=8,
nb=40,
isic=False,
dt=None,
n_checkpoints=None,
maxmem=None):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
# Create adjoint wavefield and gradient
v = TimeFunction(name='v',
grid=model.grid,
time_order=2,
space_order=space_order)
gradient = Function(name='gradient', grid=model.grid)
# Set up PDE and rearrange
vlaplace, rho = acoustic_laplacian(v, rho)
H = symbols('H')
eqn = m / rho * v.dt2 - H - damp * v.dt
stencil = solve(eqn, v.backward, simplify=False, rational=False)[0]
expression = [Eq(v.backward, stencil.subs({H: vlaplace}))]
# Data at receiver locations as adjoint source
rec_g = Receiver(name='rec_g',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
if op_forward is None:
rec_g.data[:] = rec_data[:]
adj_src = rec_g.inject(field=v.backward,
offset=model.nbpml,
expr=rec_g * rho * dt**2 / m)
# Gradient update
if u is None:
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order)
if isic is not True:
gradient_update = [Eq(gradient, gradient - dt * u.dt2 / rho * v)]
else:
# sum u.dx * v.dx fo x in dimensions.
# space_order//2
diff_u_v = sum([
first_derivative(u, dim=d, order=space_order // 2) *
first_derivative(v, dim=d, order=space_order // 2)
for d in u.space_dimensions
])
gradient_update = [
Eq(gradient, gradient - dt * (u * v.dt2 * m + diff_u_v) / rho)
]
# Create operator and run
set_log_level('ERROR')
expression += adj_src + gradient_update
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse='advanced',
dle='advanced',
name="Gradient%s" % randint(1e5))
# Optimal checkpointing
if op_forward is not None:
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
cp = DevitoCheckpoint([u])
if maxmem is not None:
n_checkpoints = int(
np.floor(maxmem * 10**6 / (cp.size * u.data.itemsize)))
wrap_fw = CheckpointOperator(op_forward, u=u, m=model.m, rec=rec)
wrap_rev = CheckpointOperator(op, u=u, v=v, m=model.m, rec_g=rec_g)
# Run forward
wrp = Revolver(cp, wrap_fw, wrap_rev, n_checkpoints, nt - 2)
wrp.apply_forward()
# Residual and gradient
if is_residual is True: # input data is already the residual
rec_g.data[:] = rec_data[:]
else:
rec_g.data[:] = rec.data[:] - rec_data[:] # input is observed data
fval = .5 * np.dot(rec_g.data[:].flatten(),
rec_g.data[:].flatten()) * dt
wrp.apply_reverse()
else:
op()
clear_cache()
if op_forward is not None and is_residual is not True:
return fval, gradient.data
else:
return gradient.data
# Time axis
t0 = 0.
tn = 10000.
dt = model0.critical_dt
nt = int(1 + (tn - t0) / dt)
time_axis = np.linspace(t0, tn, nt)
# Source
f0 = 0.015
src = RickerSource(name='src', grid=model0.grid, f0=f0, time=time_axis)
src.coordinates.data[0, :] = np.array(4617.)
src.coordinates.data[0, -1] = 20.
# Receiver for observed data
rec_t = Receiver(name='rec_t', grid=model0.grid, npoint=1200, ntime=nt)
rec_t.coordinates.data[:, 0] = np.linspace(4717., 16617., num=1200)
rec_t.coordinates.data[:, 1] = 50.
# Compute LS-RTM gradient w/ on-the-fly DFTs
num_frequencies = [1, 2, 4, 8, 16, 32, 64, 128, 256]
timings = np.zeros(len(num_frequencies))
for j in range(len(num_frequencies)):
f = np.linspace(0.01, 0.01, num_frequencies[j]) # always use 10 Hz
t1 = time.time()
d0, ufr, ufi = forward_freq_modeling(model0,
src.coordinates.data,
src.data,
rec_t.coordinates.data,
freq=f,
dt=dt,
def adjoint_y(model,
y,
src_coords,
rcv_coords,
weight_fun_pars=None,
dt=None,
space_order=8,
save=False):
"Compute adjoint wavefield v = adjoint(F(m))*y and related quantities (||v||_w, v(xsrc))"
clear_cache()
# Setting time sampling
if dt is None:
dt = model.critical_dt
# Physical parameters
m, rho, damp = model.m, model.rho, model.damp
# Setting adjoint wavefield
nt = y.shape[0]
v = TimeFunction(name="v",
grid=model.grid,
time_order=2,
space_order=space_order,
save=None if not save else nt)
# Set up PDE expression and rearrange
vlaplace, rho = laplacian(v, rho)
stencil = damp * (2.0 * v - damp * v.forward + dt**2 * rho / m * vlaplace)
expression = [Eq(v.backward, stencil)]
# Setup adjoint source injected at receiver locations
rcv = Receiver(name="rcv",
grid=model.grid,
ntime=nt,
coordinates=rcv_coords)
rcv.data[:] = y[:]
adj_src = rcv.inject(field=v.backward, expr=rcv * rho * dt**2 / m)
expression += adj_src
# Setup adjoint wavefield sampling at source locations
src = PointSource(name="src",
grid=model.grid,
ntime=nt,
coordinates=src_coords)
adj_rcv = src.interpolate(expr=v)
expression += adj_rcv
# Setup ||v||_w computation
norm_vy2_t = Function(name="nvy2t", grid=model.grid)
expression += [Inc(norm_vy2_t, Pow(v, 2))]
i = Dimension(name="i", )
norm_vy2 = Function(name="nvy2",
shape=(1, ),
dimensions=(i, ),
grid=model.grid)
if weight_fun_pars is None:
expression += [Inc(norm_vy2[0], norm_vy2_t)]
else:
weight = weight_fun(weight_fun_pars, model, src_coords)
expression += [Inc(norm_vy2[0], norm_vy2_t / weight**2)]
# Create operator and run
subs = model.spacing_map
subs[v.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse="advanced",
dle="advanced",
name="adjoint_y")
op()
# Output
if save:
return norm_vy2.data[0], src.data, v
else:
return norm_vy2.data[0], src.data, None
def objTWRIdual_devito(model,
y,
src_coords,
rcv_coords,
wav,
dat,
Filter,
eps,
mode="eval",
objfact=np.float32(1),
comp_alpha=True,
grad_corr=False,
weight_fun_pars=None,
dt=None,
space_order=8):
"Evaluate TWRI objective functional/gradients for current (m, y)"
clear_cache()
# Setting time sampling
if dt is None:
dt = model.critical_dt
# Computing y in reduced mode (= residual) if not provided
u0 = None
y_was_None = y is None
if y_was_None:
u0rcv, u0 = forward(model,
src_coords,
rcv_coords,
wav,
dt=dt,
space_order=space_order,
save=(mode == "grad") and grad_corr)
y = applyfilt(dat - u0rcv, Filter)
PTy = applyfilt_transp(y, Filter)
# Normalization constants
nx = np.float32(model.m.size)
nt, nr = np.float32(y.shape)
etaf = npla.norm(wav.reshape(-1)) / np.sqrt(nt * nx)
etad = npla.norm(applyfilt(dat, Filter).reshape(-1)) / np.sqrt(nt * nr)
# Compute wavefield vy = adjoint(F(m))*Py
norm_vPTy2, vPTy_src, vPTy = adjoint_y(model,
PTy,
src_coords,
rcv_coords,
weight_fun_pars=weight_fun_pars,
dt=dt,
space_order=space_order,
save=(mode == "grad"))
# <PTy, d-F(m)*f> = <PTy, d>-<adjoint(F(m))*PTy, f>
PTy_dot_r = np.dot(PTy.reshape(-1), dat.reshape(-1)) - np.dot(
vPTy_src.reshape(-1), wav.reshape(-1))
# ||y||
norm_y = npla.norm(y.reshape(-1))
# Optimal alpha
c1 = etaf**np.float32(2) / (np.float32(4) * etad**np.float32(2) * nx * nt)
c2 = np.float32(1) / (etad * nr * nt)
c3 = eps / np.sqrt(nr * nt)
alpha = compute_optalpha(c1 * norm_vPTy2,
c2 * PTy_dot_r,
c3 * norm_y,
comp_alpha=comp_alpha)
# Lagrangian evaluation
fun = -alpha**np.float32(
2) * c1 * norm_vPTy2 + alpha * c2 * PTy_dot_r - np.abs(
alpha) * c3 * norm_y
# Gradient computation
if mode == "grad":
# Physical parameters
m, rho, damp = model.m, model.rho, model.damp
# Create the forward wavefield
u = TimeFunction(name="u",
grid=model.grid,
time_order=2,
space_order=space_order)
# Set up PDE and rearrange
ulaplace, rho = laplacian(u, rho)
if weight_fun_pars is None:
stencil = damp * (2.0 * u - damp * u.backward + dt**2 * rho / m *
(ulaplace + 2.0 * c1 / c2 * alpha * vPTy))
else:
weight = weight_fun(weight_fun_pars, model, src_coords)
stencil = damp * (
2.0 * u - damp * u.backward + dt**2 * rho / m *
(ulaplace + 2.0 * c1 / c2 * alpha * vPTy / weight**2))
expression = [Eq(u.forward, stencil)]
# Setup source with wavelet
nt = wav.shape[0]
src = PointSource(name="src",
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wav[:]
src_term = src.inject(field=u.forward,
expr=src * rho * dt**2 / m) #######
expression += src_term
# Setup data sampling at receiver locations
rcv = Receiver(name="rcv",
grid=model.grid,
ntime=nt,
coordinates=rcv_coords)
rcv_term = rcv.interpolate(expr=u)
expression += rcv_term
# Setup gradient wrt m
gradm = Function(name="gradm", grid=model.grid)
expression += [Inc(gradm, alpha * c2 * vPTy * u.dt2)]
# Create operator and run
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse="advanced",
dle="advanced",
name="Grad")
op()
# Compute gradient wrt y
if not y_was_None or grad_corr:
norm_y = npla.norm(y)
if norm_y == 0:
grady_data = alpha * c2 * applyfilt(dat - rcv.data, Filter)
else:
grady_data = alpha * c2 * applyfilt(
dat - rcv.data, Filter) - np.abs(alpha) * c3 * y / norm_y
# Correcting for reduced gradient
if not y_was_None or (y_was_None and not grad_corr):
gradm_data = gradm.data
else:
# Compute wavefield vy_ = adjoint(F(m))*grady
_, _, vy_ = adjoint_y(model,
applyfilt_transp(grady_data, Filter),
src_coords,
rcv_coords,
dt=dt,
space_order=space_order,
save=True)
# Setup reduced gradient wrt m
gradm_corr = Function(name="gradmcorr", grid=model.grid)
expression = [Inc(gradm_corr, vy_ * u0.dt2)]
# Create operator and run
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse="advanced",
dle="advanced",
name="GradRed")
op()
# Reduced gradient post-processing
gradm_data = gradm.data + gradm_corr.data
# Return output
if mode == "eval":
return fun / objfact
elif mode == "grad" and y_was_None:
return fun / objfact, gradm_data / objfact
elif mode == "grad" and not y_was_None:
return fun / objfact, gradm_data / objfact, grady_data / objfact
def forward(model,
src_coords,
rcv_coords,
wav,
dt=None,
space_order=8,
save=False):
"Compute forward wavefield u = A(m)^{-1}*f and related quantities (u(xrcv))"
clear_cache()
# Setting time sampling
if dt is None:
dt = model.critical_dt
# Physical parameters
m, rho, damp = model.m, model.rho, model.damp
# Setting adjoint wavefield
nt = wav.shape[0]
u = TimeFunction(name="u",
grid=model.grid,
time_order=2,
space_order=space_order,
save=None if not save else nt)
# Set up PDE expression and rearrange
ulaplace, rho = laplacian(u, rho)
stencil = damp * (2.0 * u - damp * u.backward + dt**2 * rho / m * ulaplace)
expression = [Eq(u.forward, stencil)]
# Setup adjoint source injected at receiver locations
src = PointSource(name="src",
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wav[:]
src_term = src.inject(field=u.forward, expr=src * rho * dt**2 / m)
expression += src_term
# Setup adjoint wavefield sampling at source locations
rcv = Receiver(name="rcv",
grid=model.grid,
ntime=nt,
coordinates=rcv_coords)
adj_rcv = rcv.interpolate(expr=u)
expression += adj_rcv
# Create operator and run
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse="advanced",
dle="advanced",
name="forward")
op()
# Output
if save:
return rcv.data, u
else:
return rcv.data, None
def adjoint_born(model,
rec_coords,
rec_data,
u=None,
op_forward=None,
is_residual=False,
space_order=8,
nb=40,
isic=False,
dt=None):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, damp = model.m, model.damp
# Create adjoint wavefield and gradient
v = TimeFunction(name='v',
grid=model.grid,
time_order=2,
space_order=space_order)
gradient = Function(name='gradient', grid=model.grid)
# Set up PDE and rearrange
eqn = m * v.dt2 - v.laplace - damp * v.dt
stencil = solve(eqn, v.backward)[0]
expression = [Eq(v.backward, stencil)]
# Data at receiver locations as adjoint source
rec_g = Receiver(name='rec_g',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
if op_forward is None:
rec_g.data[:] = rec_data[:]
adj_src = rec_g.inject(field=v.backward,
offset=model.nbpml,
expr=rec_g * dt**2 / m)
# Gradient update
if u is None:
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order)
if isic is not True:
gradient_update = [Eq(gradient, gradient - u * v.dt2)
] # zero-lag cross-correlation imaging condition
else:
# linearized inverse scattering imaging condition (Op't Root et al. 2010; Whitmore and Crawley 2012)
if len(model.shape) == 2:
gradient_update = [
Eq(gradient,
gradient - (u * v.dt2 * m + u.dx * v.dx + u.dy * v.dy))
]
else:
gradient_update = [
Eq(
gradient, gradient -
(u * v.dt2 * m + u.dx * v.dx + u.dy * v.dy + u.dz * v.dz))
]
# Create operator and run
set_log_level('ERROR')
expression += adj_src + gradient_update
op = Operator(expression,
subs=model.spacing_map,
dse='advanced',
dle='advanced',
name="Gradient%s" % randint(1e5))
# Optimal checkpointing
if op_forward is not None:
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
cp = DevitoCheckpoint([u])
n_checkpoints = None
wrap_fw = CheckpointOperator(op_forward,
u=u,
m=model.m.data,
rec=rec,
dt=dt)
wrap_rev = CheckpointOperator(op,
u=u,
v=v,
m=model.m.data,
rec_g=rec_g,
dt=dt)
# Run forward
wrp = Revolver(cp, wrap_fw, wrap_rev, n_checkpoints, nt - 2)
wrp.apply_forward()
# Residual and gradient
if is_residual is True: # input data is already the residual
rec_g.data[:] = rec_data[:]
else:
rec_g.data[:] = rec.data[:] - rec_data[:] # input is observed data
fval = .5 * np.linalg.norm(rec_g.data[:])**2
wrp.apply_reverse()
else:
op(dt=dt)
clear_cache()
if op_forward is not None and is_residual is not True:
return fval, gradient.data
else:
return gradient.data
def adjoint_freq_born(model,
rec_coords,
rec_data,
freq,
ufr,
ufi,
space_order=8,
nb=40,
dt=None):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, damp = model.m, model.damp
nfreq = ufr.shape[0]
time = model.grid.time_dim
# Create the forward and adjoint wavefield
v = TimeFunction(name='v',
grid=model.grid,
time_order=2,
space_order=space_order)
f = Function(name='f', dimensions=(ufr.indices[0], ), shape=(nfreq, ))
f.data[:] = freq[:]
gradient = Function(name="gradient", grid=model.grid)
# Set up PDE and rearrange
eqn = m * v.dt2 - v.laplace - damp * v.dt
stencil = solve(eqn, v.backward)[0]
expression = [Eq(v.backward, stencil)]
# Data at receiver locations as adjoint source
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec.data[:] = rec_data[:]
adj_src = rec.inject(field=v.backward,
offset=model.nbpml,
expr=rec * dt**2 / m)
# Gradient update
gradient_update = [
Eq(
gradient, gradient + (2 * np.pi * f)**2 / nt *
(ufr * cos(2 * np.pi * f * time * dt) -
ufi * sin(2 * np.pi * f * time * dt)) * v)
]
# Create operator and run
set_log_level('ERROR')
expression += adj_src + gradient_update
op = Operator(expression,
subs=model.spacing_map,
dse='advanced',
dle='advanced',
name="Gradient%s" % randint(1e5))
op(dt=dt)
clear_cache()
return gradient.data
def forward_modeling(model,
src_coords,
wavelet,
rec_coords,
save=False,
space_order=8,
nb=40,
op_return=False,
dt=None):
clear_cache()
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, damp = model.m, model.damp
# Create the forward wavefield
if save is False:
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order)
else:
u = TimeFunction(name='u',
grid=model.grid,
time_order=2,
space_order=space_order,
save=nt)
# Set up PDE and rearrange
eqn = m * u.dt2 - u.laplace + damp * u.dt
stencil = solve(eqn, u.forward)[0]
expression = [Eq(u.forward, stencil)]
# Source symbol with input wavelet
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward,
offset=model.nbpml,
expr=src * dt**2 / m)
# Data is sampled at receiver locations
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec_term = rec.interpolate(expr=u, offset=model.nbpml)
# Create operator and run
set_log_level('ERROR')
expression += src_term + rec_term
op = Operator(expression,
subs=model.spacing_map,
dse='advanced',
dle='advanced',
name="Forward%s" % randint(1e5))
if op_return is False:
op(dt=dt)
return rec.data, u
else:
return op
def forward_born(model,
src_coords,
wavelet,
rec_coords,
space_order=8,
nb=40,
isic=False,
dt=None):
clear_cache()
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, dm, damp = model.m, model.dm, model.damp
# Create the forward and linearized wavefield
u = TimeFunction(name="u",
grid=model.grid,
time_order=2,
space_order=space_order)
du = TimeFunction(name="du",
grid=model.grid,
time_order=2,
space_order=space_order)
if len(model.shape) == 2:
x, y = u.space_dimensions
else:
x, y, z = u.space_dimensions
# Set up PDEs and rearrange
eqn = m * u.dt2 - u.laplace + damp * u.dt
stencil1 = solve(eqn, u.forward)[0]
if isic is not True:
eqn_lin = m * du.dt2 - du.laplace + damp * du.dt + dm * u.dt2 # born modeling
else:
du_aux = sum([
first_derivative(
first_derivative(u, dim=d, order=space_order // 2) * dm,
order=space_order // 2,
dim=d) for d in u.space_dimensions
])
eqn_lin = m * du.dt2 - du.laplace + damp * du.dt + (dm * u.dt2 * m -
du_aux)
if isic is not True:
stencil2 = solve(eqn_lin, du.forward)[0]
else:
stencil2 = solve(eqn_lin, du.forward, simplify=False,
rational=False)[0]
expression_u = [Eq(u.forward, stencil1)]
expression_du = [Eq(du.forward, stencil2)]
# Define source symbol with wavelet
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward,
offset=model.nbpml,
expr=src * dt**2 / m)
# Define receiver symbol
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec_term = rec.interpolate(expr=du, offset=model.nbpml)
# Create operator and run
set_log_level('ERROR')
expression = expression_u + src_term + expression_du + rec_term
op = Operator(expression,
subs=model.spacing_map,
dse='advanced',
dle='advanced',
name="Born%s" % randint(1e5))
op(dt=dt)
return rec.data
def adjoint_freq_born(model, rec_coords, rec_data, freq, ufr, ufi, space_order=8, dt=None, isic=False, factor=None,
free_surface=False):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
nfreq = ufr.shape[0]
time = model.grid.time_dim
if factor is None:
factor = int(1 / (dt*4*np.max(freq)))
tsave = ConditionalDimension(name='tsave', parent=model.grid.time_dim, factor=factor)
if factor==1:
tsave = time
else:
tsave = ConditionalDimension(name='tsave', parent=model.grid.time_dim, factor=factor)
dtf = factor * dt
ntf = factor / nt
print("DFT subsampling factor: ", factor)
# Create the forward and adjoint wavefield
v = TimeFunction(name='v', grid=model.grid, time_order=2, space_order=space_order)
f = Function(name='f', dimensions=(ufr.indices[0],), shape=(nfreq,))
f.data[:] = freq[:]
gradient = Function(name="gradient", grid=model.grid)
vlaplace, rho = acoustic_laplacian(v, rho)
# Set up PDE and rearrange
stencil = damp * (2.0 * v - damp * v.forward + dt**2 * rho / m * vlaplace)
expression = [Eq(v.backward, stencil)]
# Data at receiver locations as adjoint source
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
rec.data[:] = rec_data[:]
adj_src = rec.inject(field=v.backward, expr=rec * dt**2 / m)
# Gradient update
if isic is True:
if len(model.shape) == 2:
gradient_update = [Eq(gradient, gradient + (2*np.pi*f)**2*ntf*(ufr*cos(2*np.pi*f*tsave*dtf) - ufi*sin(2*np.pi*f*tsave*dtf))*v*model.m -
(ufr.dx*cos(2*np.pi*f*tsave*dtf) - ufi.dx*sin(2*np.pi*f*tsave*dtf))*v.dx*ntf -
(ufr.dy*cos(2*np.pi*f*tsave*dtf) - ufi.dy*sin(2*np.pi*f*tsave*dtf))*v.dy*ntf)]
else:
gradient_update = [Eq(gradient, gradient + (2*np.pi*f)**2*ntf*(ufr*cos(2*np.pi*f*tsave*dtf) - ufi*sin(2*np.pi*f*tsave*dtf))*v*model.m -
(ufr.dx*cos(2*np.pi*f*tsave*dtf) - ufi.dx*sin(2*np.pi*f*tsave*dtf))*v.dx*ntf -
(ufr.dy*cos(2*np.pi*f*tsave*dtf) - ufi.dy*sin(2*np.pi*f*tsave*dtf))*v.dy*ntf -
(ufr.dz*cos(2*np.pi*f*tsave*dtf) - ufi.dz*sin(2*np.pi*f*tsave*dtf))*v.dz*ntf)]
else:
gradient_update = [Eq(gradient, gradient + (2*np.pi*f)**2/nt*(ufr*cos(2*np.pi*f*tsave*dtf) - ufi*sin(2*np.pi*f*tsave*dtf))*v)]
# Create operator and run
# Free surface
if free_surface is True:
expression += freesurface(v, space_order//2, model.nbpml, forward=False)
expression += adj_src + gradient_update
subs = model.spacing_map
subs[v.grid.time_dim.spacing] = dt
op = Operator(expression, subs=subs, dse='advanced', dle='advanced')
cf = op.cfunction
op()
clear_cache()
return gradient.data
# Time axis
t0 = 0.
tn = 1000.
dt = model.critical_dt
nt = int(1 + (tn - t0) / dt)
time = np.linspace(t0, tn, nt)
# Source
f0 = 0.010
src = RickerSource(name='src', grid=model.grid, f0=f0, time=time)
src.coordinates.data[0, :] = np.array(model.domain_size) * 0.5
src.coordinates.data[0, -1] = 20.
# Receiver for observed data
rec_t = Receiver(name='rec_t', grid=model.grid, npoint=101, ntime=nt)
rec_t.coordinates.data[:, 0] = np.linspace(0, model.domain_size[0], num=101)
rec_t.coordinates.data[:, 1] = 20.
# Observed data
dobs, utrue = forward_modeling(model, src.coordinates.data, src.data,
rec_t.coordinates.data)
##################################################################################################################
# Receiver for predicted data
rec = Receiver(name='rec', grid=model0.grid, npoint=101, ntime=nt)
rec.coordinates.data[:, 0] = np.linspace(0, model0.domain_size[0], num=101)
rec.coordinates.data[:, 1] = 20.
# Save wavefields
def forward_modeling(model, src_coords, wavelet, rec_coords, save=False, space_order=8, nb=40, free_surface=False, op_return=False, u_return=False, dt=None, tsub_factor=1):
clear_cache()
# If wavelet is file, read it
if isinstance(wavelet, str):
wavelet = np.load(wavelet)
# Parameters
nt = wavelet.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
# Create the forward wavefield
if save is False and rec_coords is not None:
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=space_order)
eqsave = []
elif save is True and tsub_factor > 1:
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=space_order)
time_subsampled = ConditionalDimension(name='t_sub', parent=u.grid.time_dim, factor=tsub_factor)
nsave = (nt-1)//tsub_factor + 2
usave = TimeFunction(name='us', grid=model.grid, time_order=2, space_order=space_order, time_dim=time_subsampled, save=nsave)
eqsave = [Eq(usave.forward, u.forward)]
else:
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=space_order, save=nt)
eqsave = []
# Set up PDE
ulaplace, rho = acoustic_laplacian(u, rho)
stencil = damp * ( 2.0 * u - damp * u.backward + dt**2 * rho / m * ulaplace)
# Input source is wavefield
if isinstance(wavelet, TimeFunction):
wf_src = TimeFunction(name='wf_src', grid=model.grid, time_order=2, space_order=space_order, save=nt)
wf_src._data = wavelet._data
stencil -= wf_src
# Rearrange expression
expression = [Eq(u.forward, stencil)]
# Data is sampled at receiver locations
if rec_coords is not None:
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
rec_term = rec.interpolate(expr=u)
expression += rec_term
# Create operator and run
if save:
expression += eqsave
# Free surface
kwargs = dict()
if free_surface is True:
expression += freesurface(u, space_order//2, model.nbpml)
# Source symbol with input wavelet
if src_coords is not None:
src = PointSource(name='src', grid=model.grid, ntime=nt, coordinates=src_coords)
src.data[:] = wavelet[:]
src_term = src.inject(field=u.forward, expr=src * rho * dt**2 / m)
expression += src_term
# Create operator and run
set_log_level('ERROR')
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression, subs=subs, dse='advanced', dle='advanced')
# Return data and wavefields
if op_return is False:
op()
if save is True and tsub_factor > 1:
if rec_coords is None:
return usave
else:
return rec.data, usave
else:
if rec_coords is None:
return u
else:
return rec.data, u
# For optimal checkpointing, return operator only
else:
return op
# Time axis
t0 = 0.
tn = 1400.
dt = model.critical_dt
nt = int(1 + (tn - t0) / dt)
time_axis = np.linspace(t0, tn, nt)
# Source
f0 = 0.010
src = RickerSource(name='src', grid=model.grid, f0=f0, time=time_axis)
src.coordinates.data[0, :] = np.array(model.domain_size) * 0.5
src.coordinates.data[0, -1] = 20.
# Receiver for observed data
rec_t = Receiver(name='rec_t', grid=model.grid, npoint=401, ntime=nt)
rec_t.coordinates.data[:, 0] = np.linspace(100, 900, num=401)
rec_t.coordinates.data[:, 1] = 20.
# Linearized data
print("Forward J")
t1 = time.time()
dD_hat = forward_born(model_const,
src.coordinates.data,
src.data,
rec_t.coordinates.data,
isic=False,
dt=dt)
dm_hat = model0.dm.data
t2 = time.time()
print(t2 - t1)
def adjoint_modeling(model,
src_coords,
rec_coords,
rec_data,
space_order=8,
nb=40,
free_surface=False,
dt=None):
clear_cache()
# If wavelet is file, read it
if isinstance(rec_data, str):
rec_data = np.load(rec_data)
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
# Create the adjoint wavefield
if src_coords is not None:
v = TimeFunction(name="v",
grid=model.grid,
time_order=2,
space_order=space_order)
else:
v = TimeFunction(name="v",
grid=model.grid,
time_order=2,
space_order=space_order,
save=nt)
# Set up PDE and rearrange
vlaplace, rho = acoustic_laplacian(v, rho)
H = symbols('H')
eqn = m / rho * v.dt2 - H - damp * v.dt
# Input data is wavefield
if isinstance(rec_data, TimeFunction):
wf_rec = TimeFunction(name='wf_rec',
grid=model.grid,
time_order=2,
space_order=space_order,
save=nt)
wf_rec._data = rec_data._data
eqn -= wf_rec
stencil = solve(eqn, v.backward, simplify=False, rational=False)[0]
expression = [Eq(v.backward, stencil.subs({H: vlaplace}))]
# Free surface
if free_surface is True:
fs = DefaultDimension(name="fs", default_value=int(space_order / 2))
expression += [
Eq(v.forward.subs({v.indices[-1]: model.nbpml - fs - 1}),
-v.forward.subs({v.indices[-1]: model.nbpml + fs + 1}))
]
# Adjoint source is injected at receiver locations
if rec_coords is not None:
rec = Receiver(name='rec',
grid=model.grid,
ntime=nt,
coordinates=rec_coords)
rec.data[:] = rec_data[:]
adj_src = rec.inject(field=v.backward,
offset=model.nbpml,
expr=rec * rho * dt**2 / m)
expression += adj_src
# Data is sampled at source locations
if src_coords is not None:
src = PointSource(name='src',
grid=model.grid,
ntime=nt,
coordinates=src_coords)
adj_rec = src.interpolate(expr=v, offset=model.nbpml)
expression += adj_rec
# Create operator and run
set_log_level('ERROR')
subs = model.spacing_map
subs[v.grid.time_dim.spacing] = dt
op = Operator(expression,
subs=subs,
dse='advanced',
dle='advanced',
name="Backward%s" % randint(1e5))
op()
if src_coords is None:
return v
else:
return src.data
rec_coordinates = np.empty((nrec, ndims))
rec_coordinates[:, 0] = gx
rec_coordinates[:, 1] = gz
geometry = AcquisitionGeometry(model,
rec_coordinates,
src_coordinates,
t0=0.0,
tn=tn,
src_type='Ricker',
f0=f0)
# Resample input data to computational grid
dorig = resample(dorig, t0, tn, nt, nt_comp)
dobs = Receiver(name='rec_t',
grid=model.grid,
ntime=nt_comp,
coordinates=rec_coordinates)
dobs.data[:] = dorig
# Predicted data
if iteration > 1:
dpred, summary0 = forward_born(model,
geometry.src_positions,
geometry.src.data,
geometry.rec_positions,
space_order=space_order)
# Residual and function value
dpred = dpred - dobs
else:
dpred = Receiver(name='rec',
grid=model.grid,
def adjoint_born(model, rec_coords, rec_data, u=None, op_forward=None, is_residual=False,
space_order=8, isic=False, dt=None, n_checkpoints=None, maxmem=None,
free_surface=False, tsub_factor=1, checkpointing=False):
clear_cache()
# Parameters
nt = rec_data.shape[0]
if dt is None:
dt = model.critical_dt
m, rho, damp = model.m, model.rho, model.damp
# Create adjoint wavefield and gradient
v = TimeFunction(name='v', grid=model.grid, time_order=2, space_order=space_order)
gradient = Function(name='gradient', grid=model.grid)
# Set up PDE and rearrange
vlaplace, rho = acoustic_laplacian(v, rho)
stencil = damp * (2.0 * v - damp * v.forward + dt**2 * rho / m * vlaplace)
expression = [Eq(v.backward, stencil)]
# Data at receiver locations as adjoint source
rec_g = Receiver(name='rec_g', grid=model.grid, ntime=nt, coordinates=rec_coords)
if op_forward is None:
rec_g.data[:] = rec_data[:]
adj_src = rec_g.inject(field=v.backward, expr=rec_g * rho * dt**2 / m)
# Gradient update
if u is None:
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=space_order)
if isic is not True:
gradient_update = [Inc(gradient, - dt * u.dt2 / rho * v)]
else:
# sum u.dx * v.dx fo x in dimensions.
# space_order//2
diff_u_v = sum([first_derivative(u, dim=d, fd_order=space_order//2)*
first_derivative(v, dim=d, fd_order=space_order//2)
for d in u.space_dimensions])
gradient_update = [Inc(gradient, - tsub_factor * dt * (u * v.dt2 * m + diff_u_v) / rho)]
# Create operator and run
# Free surface
if free_surface is True:
expression += freesurface(v, space_order//2, model.nbpml, forward=False)
expression += adj_src + gradient_update
subs = model.spacing_map
subs[u.grid.time_dim.spacing] = dt
op = Operator(expression, subs=subs, dse='advanced', dle='advanced')
# Optimal checkpointing
summary1 = None
summary2 = None
if op_forward is not None and checkpointing is True:
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
cp = DevitoCheckpoint([u])
if maxmem is not None:
n_checkpoints = int(np.floor(maxmem * 10**6 / (cp.size * u.data.itemsize)))
wrap_fw = CheckpointOperator(op_forward, u=u, m=model.m, rec=rec)
wrap_rev = CheckpointOperator(op, u=u, v=v, m=model.m, rec_g=rec_g)
# Run forward
wrp = Revolver(cp, wrap_fw, wrap_rev, n_checkpoints, nt-2)
wrp.apply_forward()
# Residual and gradient
if is_residual is True: # input data is already the residual
rec_g.data[:] = rec_data[:]
else:
rec_g.data[:] = rec.data[:] - rec_data[:] # input is observed data
fval = .5*np.dot(rec_g.data[:].flatten(), rec_g.data[:].flatten()) * dt
wrp.apply_reverse()
elif op_forward is not None and checkpointing is False:
# Compile first
cf1 = op_forward.cfunction
cf2 = op.cfunction
# Run forward and adjoint
summary1 = op_forward.apply()
if is_residual is True:
rec_g.data[:] = rec_data[:]
else:
rec = Receiver(name='rec', grid=model.grid, ntime=nt, coordinates=rec_coords)
rec_g.data[:] = rec.data[:] - rec_data[:]
fval = .5*np.dot(rec_g.data[:].flatten(), rec_g.data[:].flatten()) * dt
summary2 = op.apply()
else:
cf = op.cfunction
summary1 = op.apply()
clear_cache()
if op_forward is not None and is_residual is not True:
if summary2 is not None:
return fval, gradient.data, summary1, summary2
elif summary1 is not None:
return fval, gradient.data, summary1
else:
return fval, gradient.data
else:
if summary2 is not None:
return gradient.data, summary1, summary2
elif summary1 is not None:
return gradient.data, summary1
else:
return gradient.data