def tti_operator(dse=False, dle='advanced', space_order=4):
nrec = 101
t0 = 0.0
tn = 250.
nbpml = 10
shape = (50, 50, 50)
spacing = (20., 20., 20.)
# Two layer model for true velocity
model = demo_model('layers-tti', ratio=3, nbpml=nbpml, space_order=space_order,
shape=shape, spacing=spacing)
# Source and receiver geometries
src_coordinates = np.empty((1, len(spacing)))
src_coordinates[0, :] = np.array(model.domain_size) * .5
src_coordinates[0, -1] = model.origin[-1] + 2 * spacing[-1]
rec_coordinates = np.empty((nrec, len(spacing)))
rec_coordinates[:, 0] = np.linspace(0., model.domain_size[0], num=nrec)
rec_coordinates[:, 1:] = src_coordinates[0, 1:]
geometry = AcquisitionGeometry(model, rec_coordinates, src_coordinates,
t0=t0, tn=tn, src_type='Gabor', f0=0.010)
return AnisotropicWaveSolver(model, geometry, space_order=space_order, dse=dse)
def tti_setup(shape=(50, 50, 50),
spacing=(20.0, 20.0, 20.0),
tn=250.0,
kernel='centered',
space_order=4,
nbl=10,
preset='layers-tti',
**kwargs):
# Two layer model for true velocity
model = demo_model(preset,
shape=shape,
spacing=spacing,
space_order=space_order,
nbl=nbl,
**kwargs)
# Source and receiver geometries
geometry = setup_geometry(model, tn)
return AnisotropicWaveSolver(model,
geometry,
space_order=space_order,
kernel=kernel,
**kwargs)
def tti_setup(shape=(50, 50, 50),
spacing=(20.0, 20.0, 20.0),
tn=250.0,
time_order=2,
space_order=4,
nbpml=10,
**kwargs):
nrec = 101
# Two layer model for true velocity
model = demo_model('layers-tti', shape=shape, spacing=spacing, nbpml=nbpml)
# Derive timestepping from model spacing
dt = model.critical_dt
t0 = 0.0
nt = int(1 + (tn - t0) / dt)
time = np.linspace(t0, tn, nt)
# Define source geometry (center of domain, just below surface)
src = RickerSource(name='src', grid=model.grid, f0=0.015, time=time)
src.coordinates.data[0, :] = np.array(model.domain_size) * .5
src.coordinates.data[0, -1] = model.origin[-1] + 2 * spacing[-1]
# Define receiver geometry (spread across x, lust below surface)
rec = Receiver(name='nrec', grid=model.grid, ntime=nt, npoint=nrec)
rec.coordinates.data[:, 0] = np.linspace(0.,
model.domain_size[0],
num=nrec)
rec.coordinates.data[:, 1:] = src.coordinates.data[0, 1:]
return AnisotropicWaveSolver(model,
source=src,
receiver=rec,
time_order=time_order,
space_order=space_order,
**kwargs)
def tti_operator(dse=False, space_order=4):
nrec = 101
t0 = 0.0
tn = 250.
nbpml = 10
shape = (50, 50, 50)
spacing = (20., 20., 20.)
# Two layer model for true velocity
model = demo_model('layers-tti', ratio=3, nbpml=nbpml, space_order=space_order,
shape=shape, spacing=spacing)
# Derive timestepping from model spacing
# Derive timestepping from model spacing
dt = model.critical_dt
time_range = TimeAxis(start=t0, stop=tn, step=dt)
# Define source geometry (center of domain, just below surface)
src = GaborSource(name='src', grid=model.grid, f0=0.01, time_range=time_range)
src.coordinates.data[0, :] = np.array(model.domain_size) * .5
src.coordinates.data[0, -1] = model.origin[-1] + 2 * spacing[-1]
# Define receiver geometry (spread across x, lust below surface)
rec = Receiver(name='nrec', grid=model.grid, time_range=time_range, npoint=nrec)
rec.coordinates.data[:, 0] = np.linspace(0., model.domain_size[0], num=nrec)
rec.coordinates.data[:, 1:] = src.coordinates.data[0, 1:]
return AnisotropicWaveSolver(model, source=src, receiver=rec,
space_order=space_order, dse=dse)
def tti_setup(shape=(50, 50, 50),
spacing=(20.0, 20.0, 20.0),
tn=250.0,
space_order=4,
nbl=10,
preset='layers-tti',
**kwargs):
nrec = 101
# Two layer model for true velocity
model = demo_model(preset, shape=shape, spacing=spacing, nbl=nbl)
# Source and receiver geometries
src_coordinates = np.empty((1, len(spacing)))
src_coordinates[0, :] = np.array(model.domain_size) * .5
if len(shape) > 1:
src_coordinates[0, -1] = model.origin[-1] + 2 * spacing[-1]
rec_coordinates = np.empty((nrec, len(spacing)))
rec_coordinates[:, 0] = np.linspace(0., model.domain_size[0], num=nrec)
if len(shape) > 1:
rec_coordinates[:, 1] = np.array(model.domain_size)[1] * .5
rec_coordinates[:, -1] = model.origin[-1] + 2 * spacing[-1]
geometry = AcquisitionGeometry(model,
rec_coordinates,
src_coordinates,
t0=0.0,
tn=tn,
src_type='Ricker',
f0=0.010)
return AnisotropicWaveSolver(model,
geometry,
space_order=space_order,
**kwargs)
def setup(dimensions=(50, 50, 50),
spacing=(20.0, 20.0, 20.0),
tn=250.0,
time_order=2,
space_order=4,
nbpml=10,
dse='advanced'):
origin = (0., 0., 0.)
# True velocity
true_vp = np.ones(dimensions) + 1.0
true_vp[:, :, int(dimensions[0] / 3):int(2 * dimensions[0] / 3)] = 3.0
true_vp[:, :, int(2 * dimensions[0] / 3):int(dimensions[0])] = 4.0
model = Model(origin,
spacing,
dimensions,
true_vp,
nbpml=nbpml,
epsilon=.4 * np.ones(dimensions),
delta=-.1 * np.ones(dimensions),
theta=-np.pi / 7 * np.ones(dimensions),
phi=np.pi / 5 * np.ones(dimensions))
# Define seismic data.
f0 = .010
dt = model.critical_dt
t0 = 0.0
nt = int(1 + (tn - t0) / dt)
# Set up the source as Ricker wavelet for f0
# Source geometry
time_series = np.zeros((nt, 1))
time_series[:, 0] = source(np.linspace(t0, tn, nt), f0)
location = np.zeros((1, 3))
location[0, 0] = origin[0] + dimensions[0] * spacing[0] * 0.5
location[0, 1] = origin[1] + dimensions[1] * spacing[1] * 0.5
location[0, 2] = origin[1] + 2 * spacing[2]
src = PointSource(name='src', data=time_series, coordinates=location)
# Receiver geometry
receiver_coords = np.zeros((101, 3))
receiver_coords[:, 0] = np.linspace(0,
origin[0] + dimensions[0] * spacing[0],
num=101)
receiver_coords[:, 1] = origin[1] + dimensions[1] * spacing[1] * 0.5
receiver_coords[:, 2] = location[0, 1]
rec = Receiver(name='rec', ntime=nt, coordinates=receiver_coords)
return AnisotropicWaveSolver(model,
source=src,
time_order=time_order,
space_order=space_order,
receiver=rec,
dse=dse)
def test_tti_staggered(shape):
spacing = [10. for _ in shape]
# Model
model = demo_model('constant-tti', shape=shape, spacing=spacing)
# Define seismic data and parameters
f0 = .010
dt = model.critical_dt
t0 = 0.0
tn = 250.0
time_range = TimeAxis(start=t0, stop=tn, step=dt)
nt = time_range.num
last = (nt - 1) % 2
# Generate a wavefield as initial condition
source = RickerSource(name='src',
grid=model.grid,
f0=f0,
time_range=time_range)
source.coordinates.data[0, :] = np.array(model.domain_size) * .5
receiver = Receiver(name='rec',
grid=model.grid,
time_range=time_range,
npoint=1)
# Solvers
solver_tti = AnisotropicWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=8)
solver_tti2 = AnisotropicWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=8)
# Solve
configuration['dse'] = 'aggressive'
configuration['dle'] = 'advanced'
rec1, u1, v1, _ = solver_tti.forward(kernel='staggered')
configuration['dle'] = 'basic'
rec2, u2, v2, _ = solver_tti2.forward(kernel='staggered')
u_staggered1 = u1.data[last, :] + v1.data[last, :]
u_staggered2 = u2.data[last, :] + v2.data[last, :]
res = np.linalg.norm(u_staggered1.reshape(-1) - u_staggered2.reshape(-1))
log("DSE/DLE introduces error %2.4e in %d dimensions" % (res, len(shape)))
assert np.isclose(res, 0.0, atol=1e-8)
def overthrust_setup_tti(filename, tn=4000, space_order=2, nbpml=40, **kwargs):
model = from_hdf5(filename,
space_order=space_order,
nbpml=nbpml,
datakey='m0',
dtype=np.float32)
shape = model.vp.shape
spacing = model.shape
nrec = shape[0]
# Derive timestepping from model spacing
dt = model.critical_dt
t0 = 0.0
time_range = TimeAxis(start=t0, stop=tn, step=dt)
# Define source geometry (center of domain, just below surface)
src = RickerSource(name='src',
grid=model.grid,
f0=0.015,
time_range=time_range)
src.coordinates.data[0, :] = np.array(model.domain_size) * .5
if len(shape) > 1:
src.coordinates.data[0, -1] = model.origin[-1] + 2 * spacing[-1]
# Define receiver geometry (spread across x, just below surface)
rec = Receiver(name='rec',
grid=model.grid,
time_range=time_range,
npoint=nrec)
rec.coordinates.data[:, 0] = np.linspace(0.,
model.domain_size[0],
num=nrec)
if len(shape) > 1:
rec.coordinates.data[:, 1:] = src.coordinates.data[0, 1:]
# Create solver object to provide relevant operators
return AnisotropicWaveSolver(model,
source=src,
receiver=rec,
space_order=space_order,
**kwargs)
def test_tti_staggered(shape):
spacing = [10. for _ in shape]
nrec = 1
# Model
model = demo_model('layers-tti', shape=shape, spacing=spacing)
# Source and receiver geometries
src_coordinates = np.empty((1, len(spacing)))
src_coordinates[0, :] = np.array(model.domain_size) * .5
src_coordinates[0, -1] = model.origin[-1] + 2 * spacing[-1]
rec_coordinates = np.empty((nrec, len(spacing)))
rec_coordinates[:, 0] = np.linspace(0., model.domain_size[0], num=nrec)
rec_coordinates[:, -1] = model.origin[-1] + 2 * spacing[-1]
geometry = AcquisitionGeometry(model,
rec_coordinates,
src_coordinates,
t0=0.0,
tn=250.,
src_type='Ricker',
f0=0.010)
# Solvers
solver_tti = AnisotropicWaveSolver(model,
geometry,
time_order=2,
space_order=8)
solver_tti2 = AnisotropicWaveSolver(model,
geometry,
time_order=2,
space_order=8)
# Solve
configuration['dse'] = 'aggressive'
configuration['dle'] = 'advanced'
rec1, u1, v1, _ = solver_tti.forward(kernel='staggered')
configuration['dle'] = 'basic'
rec2, u2, v2, _ = solver_tti2.forward(kernel='staggered')
res1 = np.linalg.norm(u1.data.reshape(-1) - u2.data.reshape(-1))
res2 = np.linalg.norm(v1.data.reshape(-1) - v2.data.reshape(-1))
log("DSE/DLE introduces error %2.4e, %2.4e in %d dimensions" %
(res1, res2, len(shape)))
assert np.isclose(res1, 0.0, atol=1e-8)
assert np.isclose(res2, 0.0, atol=1e-8)
def test_tti(dimensions, space_order):
nbpml = 10
ndim = len(dimensions)
origin = tuple([0.] * ndim)
spacing = tuple([10.] * ndim)
# Velocity model, two layers
true_vp = np.ones(dimensions) + .5
true_vp[..., int(dimensions[-1] / 3):dimensions[-1]] = 2.5
# Source location
location = np.zeros((1, ndim), dtype=np.float32)
location[0, :-1] = [
origin[i] + dimensions[i] * spacing[i] * .5 for i in range(ndim - 1)
]
location[0, -1] = origin[-1] + 2 * spacing[-1]
# Receivers locations
receiver_coords = np.zeros((dimensions[0], ndim), dtype=np.float32)
receiver_coords[:, 0] = np.linspace(0,
origin[0] +
(dimensions[0] - 1) * spacing[0],
num=dimensions[0])
receiver_coords[:, 1:] = location[0, 1:]
# True velocity
true_vp = np.ones(dimensions) + .5
model = Model(origin,
spacing,
dimensions,
true_vp,
nbpml=nbpml,
epsilon=0.0 * true_vp,
delta=0.0 * true_vp,
theta=0.0 * true_vp,
phi=0.0 * true_vp)
# Define seismic data.
f0 = .010
dt = model.critical_dt
t0 = 0.0
tn = 350.0
nt = int(1 + (tn - t0) / dt)
last = (nt - 2) % 3
indlast = [(last + 1) % 3, last % 3, (last - 1) % 3]
# Set up the source as Ricker wavelet for f0
def ricker_source(t, f0):
r = (np.pi * f0 * (t - 1. / f0))
return (1 - 2. * r**2) * np.exp(-r**2)
# Source geometry
time_series = np.zeros((nt, 1))
time_series[:, 0] = ricker_source(np.linspace(t0, tn, nt), f0)
# Adjoint test
source = PointSource(name='src', data=time_series, coordinates=location)
receiver = Receiver(name='rec', ntime=nt, coordinates=receiver_coords)
acoustic = AcousticWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=space_order)
rec, u1, _ = acoustic.forward(save=False)
tn = 50.0
nt = int(1 + (tn - t0) / dt)
# Source geometry
time_series = np.zeros((nt, 1))
time_series[:, 0] = 0 * ricker_source(np.linspace(t0, tn, nt), f0)
source = PointSource(name='src', data=time_series, coordinates=location)
receiver = Receiver(name='rec', ntime=nt, coordinates=receiver_coords)
acoustic = AcousticWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=space_order)
solver_tti = AnisotropicWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=space_order)
# Create new wavefield object restart forward computation
u = TimeData(name='u',
shape=model.shape_domain,
save=False,
time_order=2,
space_order=space_order,
dtype=model.dtype)
u.data[0:3, :] = u1.data[indlast, :]
rec, _, _ = acoustic.forward(save=False, u=u)
utti = TimeData(name='u',
shape=model.shape_domain,
save=False,
time_order=2,
space_order=space_order,
dtype=model.dtype)
vtti = TimeData(name='v',
shape=model.shape_domain,
save=False,
time_order=2,
space_order=space_order,
dtype=model.dtype)
utti.data[0:3, :] = u1.data[indlast, :]
vtti.data[0:3, :] = u1.data[indlast, :]
rec_tti, u_tti, v_tti, _ = solver_tti.forward(u=utti, v=vtti)
res = linalg.norm(
u.data.reshape(-1) - .5 * u_tti.data.reshape(-1) -
.5 * v_tti.data.reshape(-1))
res /= linalg.norm(u.data.reshape(-1))
log("Difference between acoustic and TTI with all coefficients to 0 %f" %
res)
assert np.isclose(res, 0.0, atol=1e-1)
def test_tti(shape, space_order, kernel):
"""
This first test compare the solution of the acoustic wave-equation and the
TTI wave-eqatuon with all anisotropy parametrs to 0. The two solutions should
be the same.
"""
if kernel == 'shifted':
space_order *= 2
to = 2
so = space_order
nbl = 10
origin = [0. for _ in shape]
spacing = [10. for _ in shape]
vp = 1.5 * np.ones(shape)
nrec = shape[0]
# Constant model for true velocity
model = Model(origin=origin,
shape=shape,
vp=vp,
spacing=spacing,
nbl=nbl,
space_order=space_order,
epsilon=np.zeros(shape),
delta=np.zeros(shape),
theta=np.zeros(shape),
phi=np.zeros(shape))
# Source and receiver geometries
src_coordinates = np.empty((1, len(spacing)))
src_coordinates[0, :] = np.array(model.domain_size) * .5
src_coordinates[0, -1] = model.origin[-1] + 2 * spacing[-1]
rec_coordinates = np.empty((nrec, len(spacing)))
rec_coordinates[:, 0] = np.linspace(0., model.domain_size[0], num=nrec)
rec_coordinates[:, 1] = np.array(model.domain_size)[1] * .5
rec_coordinates[:, -1] = model.origin[-1] + 2 * spacing[-1]
geometry = AcquisitionGeometry(model,
rec_coordinates,
src_coordinates,
t0=0.0,
tn=350.,
src_type='Ricker',
f0=0.010)
acoustic = AcousticWaveSolver(model,
geometry,
time_order=2,
space_order=so)
rec, u1, _ = acoustic.forward(save=False)
# Solvers
solver_tti = AnisotropicWaveSolver(model,
geometry,
time_order=2,
space_order=space_order)
# zero src
src = geometry.src
src.data.fill(0.)
# last time index
nt = geometry.nt
last = (nt - 2) % 3
indlast = [(last + 1) % 3, last % 3, (last - 1) % 3]
# Create new wavefield object restart forward computation
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=so)
u.data[0:3, :] = u1.data[indlast, :]
acoustic.forward(save=False, u=u, time_M=10, src=src)
utti = TimeFunction(name='u',
grid=model.grid,
time_order=to,
space_order=so)
vtti = TimeFunction(name='v',
grid=model.grid,
time_order=to,
space_order=so)
utti.data[0:to + 1, :] = u1.data[indlast[:to + 1], :]
vtti.data[0:to + 1, :] = u1.data[indlast[:to + 1], :]
solver_tti.forward(u=utti, v=vtti, kernel=kernel, time_M=10, src=src)
normal_u = u.data[:]
normal_utti = .5 * utti.data[:]
normal_vtti = .5 * vtti.data[:]
res = linalg.norm((normal_u - normal_utti - normal_vtti).reshape(-1))**2
res /= np.linalg.norm(normal_u.reshape(-1))**2
log("Difference between acoustic and TTI with all coefficients to 0 %2.4e"
% res)
assert np.isclose(res, 0.0, atol=1e-4)
def test_tti(shape, space_order, kernel):
"""
This first test compare the solution of the acoustic wave-equation and the
TTI wave-eqatuon with all anisotropy parametrs to 0. The two solutions should
be the same.
"""
if kernel == 'shifted':
space_order *= 2
to = 2
so = space_order
nbpml = 10
origin = [0. for _ in shape]
spacing = [10. for _ in shape]
vp = 1.5 * np.ones(shape)
nrec = shape[0]
# Constant model for true velocity
model = Model(origin=origin, shape=shape, vp=vp,
spacing=spacing, nbpml=nbpml, space_order=space_order,
epsilon=np.zeros(shape), delta=np.zeros(shape),
theta=np.zeros(shape), phi=np.zeros(shape))
# Source and receiver geometries
src_coordinates = np.empty((1, len(spacing)))
src_coordinates[0, :] = np.array(model.domain_size) * .5
src_coordinates[0, -1] = model.origin[-1] + 2 * spacing[-1]
rec_coordinates = np.empty((nrec, len(spacing)))
rec_coordinates[:, 0] = np.linspace(0., model.domain_size[0], num=nrec)
rec_coordinates[:, 1] = np.array(model.domain_size)[1] * .5
rec_coordinates[:, -1] = model.origin[-1] + 2 * spacing[-1]
geometry = AcquisitionGeometry(model, rec_coordinates, src_coordinates,
t0=0.0, tn=350., src_type='Ricker', f0=0.010)
acoustic = AcousticWaveSolver(model, geometry, time_order=2, space_order=so)
rec, u1, _ = acoustic.forward(save=False)
# Solvers
solver_tti = AnisotropicWaveSolver(model, geometry, time_order=2,
space_order=space_order)
# zero src
src = geometry.src
src.data.fill(0.)
# last time index
nt = geometry.nt
last = (nt - 2) % 3
indlast = [(last + 1) % 3, last % 3, (last-1) % 3]
# Create new wavefield object restart forward computation
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=so)
u.data[0:3, :] = u1.data[indlast, :]
acoustic.forward(save=False, u=u, time_M=10, src=src)
utti = TimeFunction(name='u', grid=model.grid, time_order=to, space_order=so)
vtti = TimeFunction(name='v', grid=model.grid, time_order=to, space_order=so)
utti.data[0:to+1, :] = u1.data[indlast[:to+1], :]
vtti.data[0:to+1, :] = u1.data[indlast[:to+1], :]
solver_tti.forward(u=utti, v=vtti, kernel=kernel, time_M=10, src=src)
normal_u = u.data[:]
normal_utti = .5 * utti.data[:]
normal_vtti = .5 * vtti.data[:]
res = linalg.norm((normal_u - normal_utti - normal_vtti).reshape(-1))**2
res /= np.linalg.norm(normal_u.reshape(-1))**2
log("Difference between acoustic and TTI with all coefficients to 0 %2.4e" % res)
assert np.isclose(res, 0.0, atol=1e-4)
def tti_operator(self, dse=False, space_order=4):
return AnisotropicWaveSolver(self.model, self.geometry,
space_order=space_order, dse=dse)
def test_tti(shape, space_order, kernel):
"""
This first test compare the solution of the acoustic wave-equation and the
TTI wave-eqatuon with all anisotropy parametrs to 0. The two solutions should
be the same.
"""
if kernel == 'shifted':
space_order *= 2
to = 2
so = space_order // 2 if kernel == 'shifted' else space_order
nbpml = 10
origin = [0. for _ in shape]
spacing = [10. for _ in shape]
vp = 1.5 * np.ones(shape)
# Constant model for true velocity
model = Model(origin=origin,
shape=shape,
vp=vp,
spacing=spacing,
nbpml=nbpml,
space_order=space_order,
epsilon=np.zeros(shape),
delta=np.zeros(shape),
theta=np.zeros(shape),
phi=np.zeros(shape))
# Define seismic data and parameters
f0 = .010
dt = model.critical_dt
t0 = 0.0
tn = 350.0
time_range = TimeAxis(start=t0, stop=tn, step=dt)
nt = time_range.num
last = (nt - 2) % 3
indlast = [(last + 1) % 3, last % 3, (last - 1) % 3]
# Generate a wavefield as initial condition
source = RickerSource(name='src',
grid=model.grid,
f0=f0,
time_range=time_range)
source.coordinates.data[0, :] = np.array(model.domain_size) * .5
receiver = Receiver(name='rec',
grid=model.grid,
time_range=time_range,
npoint=1)
acoustic = AcousticWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=so)
rec, u1, _ = acoustic.forward(save=False)
source.data.fill(0.)
# Solvers
acoustic = AcousticWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=so)
solver_tti = AnisotropicWaveSolver(model,
source=source,
receiver=receiver,
time_order=2,
space_order=space_order)
# Create new wavefield object restart forward computation
u = TimeFunction(name='u', grid=model.grid, time_order=2, space_order=so)
u.data[0:3, :] = u1.data[indlast, :]
acoustic.forward(save=False, u=u, time_M=10, src=source)
utti = TimeFunction(name='u',
grid=model.grid,
time_order=to,
space_order=so)
vtti = TimeFunction(name='v',
grid=model.grid,
time_order=to,
space_order=so)
utti.data[0:to + 1, :] = u1.data[indlast[:to + 1], :]
vtti.data[0:to + 1, :] = u1.data[indlast[:to + 1], :]
solver_tti.forward(u=utti, v=vtti, kernel=kernel, time_M=10, src=source)
normal_u = u.data[:]
normal_utti = .5 * utti.data[:]
normal_vtti = .5 * vtti.data[:]
res = linalg.norm((normal_u - normal_utti - normal_vtti).reshape(-1))**2
res /= np.linalg.norm(normal_u.reshape(-1))**2
log("Difference between acoustic and TTI with all coefficients to 0 %2.4e"
% res)
assert np.isclose(res, 0.0, atol=1e-4)
def forward_modeling_single_shot(record, table, par_files):
"Serial modeling function"
worker = get_worker() # The worker on which this task is running
strng = '{} : {} =>'.format(worker.address, str(record).zfill(5))
filename = 'logfile_{}.txt'.format(str(record).zfill(5))
g = open(filename, 'w')
g.write("This will show up in the worker logs")
# Read velocity model
f = segyio.open(par_files[-1], iline=segyio.tracefield.TraceField.FieldRecord,
xline=segyio.tracefield.TraceField.CDP)
xl, il, t = f.xlines, f.ilines, f.samples
dz = t[1] - t[0]
dx = f.header[1][segyio.TraceField.SourceX]-f.header[0][segyio.TraceField.SourceX]
if len(il) != 1:
dims = (len(xl), len(il), len(f.samples))
else:
dims = (len(xl), len(f.samples))
vp = f.trace.raw[:].reshape(dims)
vp *= 1./1000 # convert to km/sec
epsilon = np.empty(dims)
delta = np.empty(dims)
theta = np.empty(dims)
params = [epsilon, delta, theta]
# Read Thomsem parameters
for segyfile, par in zip(par_files, params):
f = segyio.open(segyfile, iline=segyio.tracefield.TraceField.FieldRecord,
xline=segyio.tracefield.TraceField.CDP)
par[:] = f.trace.raw[:].reshape(dims)
theta *= (np.pi/180.) # use radians
g.write('{} Parameter model dims: {}\n'.format(strng, vp.shape))
origin = (0., 0.)
shape = vp.shape
spacing = (dz, dz)
# Get a single shot as a numpy array
filename = table[record]['filename']
position = table[record]['Trace_Position']
traces_in_shot = table[record]['Num_Traces']
src_coord = np.array(table[record]['Source']).reshape((1, len(dims)))
rec_coord = np.array(table[record]['Receivers'])
start = time.time()
f = segyio.open(filename, ignore_geometry=True)
num_samples = len(f.samples)
samp_int = f.bin[segyio.BinField.Interval]/1000
retrieved_shot = np.zeros((traces_in_shot, num_samples))
shot_traces = f.trace[position:position+traces_in_shot]
for i, trace in enumerate(shot_traces):
retrieved_shot[i] = trace
g.write('{} Shot loaded in: {} seconds\n'.format(strng, time.time()-start))
# Only keep receivers within the model'
xmin = origin[0]
idx_xrec = np.where(rec_coord[:, 0] < xmin)[0]
is_empty = idx_xrec.size == 0
if not is_empty:
g.write('{} in {}\n'.format(strng, rec_coord.shape))
idx_tr = np.where(rec_coord[:, 0] >= xmin)[0]
rec_coord = np.delete(rec_coord, idx_xrec, axis=0)
# For 3D shot records, scan also y-receivers
if len(origin) == 3:
ymin = origin[1]
idx_yrec = np.where(rec_coord[:, 1] < ymin)[0]
is_empty = idx_yrec.size == 0
if not is_empty:
rec_coord = np.delete(rec_coord, idx_yrec, axis=0)
if rec_coord.size == 0:
g.write('all receivers outside of model\n')
return np.zeros(vp.shape)
space_order = 8
g.write('{} before: {} {} {}\n'.format(strng, params[0].shape, rec_coord.shape, src_coord.shape))
model = limit_model_to_receiver_area(rec_coord, src_coord, origin, spacing,
shape, vp, params, space_order=space_order, nbl=80)
g.write('{} shape_vp: {}\n'.format(strng, model.vp.shape))
model.smooth(('epsilon', 'delta', 'theta'))
# Geometry for current shot
geometry = AcquisitionGeometry(model, rec_coord, src_coord, 0, (num_samples-1)*samp_int, f0=0.018, src_type='Ricker')
g.write("{} Number of samples modelled data & dt: {} & {}\n".format(strng, geometry.nt, model.critical_dt))
g.write("{} Samples & dt: {} & {}\n".format(strng, num_samples, samp_int))
# Set up solver.
solver_tti = AnisotropicWaveSolver(model, geometry, space_order=space_order)
# Create image symbol and instantiate the previously defined imaging operator
image = Function(name='image', grid=model.grid)
itemsize = np.dtype(np.float32).itemsize
full_fld_mem = model.vp.size*itemsize*geometry.nt*2.
checkpointing = True
if checkpointing:
op_imaging = ImagingOperator(geometry, image, space_order, save=False)
n_checkpoints = 150
ckp_fld_mem = model.vp.size*itemsize*n_checkpoints*2.
g.write('Mem full fld: {} == {} use ckp instead\n'.format(full_fld_mem, humanbytes(full_fld_mem)))
g.write('Number of checkpoints/timesteps: {}/{}\n'.format(n_checkpoints, geometry.nt))
g.write('Memory saving: {}\n'.format(humanbytes(full_fld_mem-ckp_fld_mem)))
u = TimeFunction(name='u', grid=model.grid, staggered=None,
time_order=2, space_order=space_order)
v = TimeFunction(name='v', grid=model.grid, staggered=None,
time_order=2, space_order=space_order)
vv = TimeFunction(name='vv', grid=model.grid, staggered=None,
time_order=2, space_order=space_order)
uu = TimeFunction(name='uu', grid=model.grid, staggered=None,
time_order=2, space_order=space_order)
cp = DevitoCheckpoint([u, v])
op_fwd = solver_tti.op_fwd(save=False)
op_fwd.cfunction
op_imaging.cfunction
wrap_fw = CheckpointOperator(op_fwd, src=geometry.src,
u=u, v=v, vp=model.vp, epsilon=model.epsilon,
delta=model.delta, theta=model.theta, dt=model.critical_dt)
time_range = TimeAxis(start=0, stop=(num_samples-1)*samp_int, step=samp_int)
dobs = Receiver(name='dobs', grid=model.grid, time_range=time_range, coordinates=geometry.rec_positions)
if not is_empty:
dobs.data[:] = retrieved_shot[idx_tr, :].T
else:
dobs.data[:] = retrieved_shot[:].T
dobs_resam = dobs.resample(num=geometry.nt)
g.write('Shape of residual: {}\n'.format(dobs_resam.data.shape))
wrap_rev = CheckpointOperator(op_imaging, u=u, v=v, vv=vv, uu=uu, vp=model.vp,
epsilon=model.epsilon, delta=model.delta, theta=model.theta,
dt=model.critical_dt, residual=dobs_resam.data)
# Run forward
wrp = Revolver(cp, wrap_fw, wrap_rev, n_checkpoints, dobs_resam.shape[0]-2)
g.write('Revolver storage: {}\n'.format(humanbytes(cp.size*n_checkpoints*itemsize)))
wrp.apply_forward()
g.write('{} run finished\n'.format(strng))
summary = wrp.apply_reverse()
form = 'image_{}.bin'.format(str(record).zfill(5))
h = open(form, 'wb')
g.write('{}\n'.format(str(image.data.shape)))
np.transpose(image.data).astype('float32').tofile(h)
else:
# For illustrative purposes, assuming that there is enough memory
g.write('enough memory to save full fld: {} == {}\n'.format(full_fld_mem, humanbytes(full_fld_mem)))
op_imaging = ImagingOperator(geometry, image, space_order)
vv = TimeFunction(name='vv', grid=model.grid, staggered=None, time_order=2, space_order=space_order)
uu = TimeFunction(name='uu', grid=model.grid, staggered=None, time_order=2, space_order=space_order)
time_range = TimeAxis(start=0, stop=(num_samples-1)*samp_int, step=samp_int)
dobs = Receiver(name='dobs', grid=model.grid, time_range=time_range, coordinates=geometry.rec_positions)
if not is_empty:
dobs.data[:] = retrieved_shot[idx_tr, :].T
else:
dobs.data[:] = retrieved_shot[:].T
dobs_resam = dobs.resample(num=geometry.nt)
u, v = solver_tti.forward(vp=model.vp, epsilon=model.epsilon, delta=model.delta,
theta=model.theta, dt=model.critical_dt, save=True)[1:-1]
op_imaging(u=u, v=v, vv=vv, uu=uu, epsilon=model.epsilon, delta=model.delta,
theta=model0.theta, vp=model.vp, dt=model.critical_dt, residual=dobs_resam)
full_image = extend_image(origin, vp, model, image)
return full_image
def test_tti(shape, space_order):
nbpml = 10
ndim = len(shape)
origin = [0. for _ in shape]
spacing = [10. for _ in shape]
# Source location
location = np.zeros((1, ndim), dtype=np.float32)
location[0, :-1] = [origin[i] + shape[i] * spacing[i] * .5
for i in range(ndim-1)]
location[0, -1] = origin[-1] + 2 * spacing[-1]
# Receivers locations
receiver_coords = np.zeros((shape[0], ndim), dtype=np.float32)
receiver_coords[:, 0] = np.linspace(0, origin[0] +
(shape[0]-1) * spacing[0],
num=shape[0])
receiver_coords[:, 1:] = location[0, 1:]
# Two layer model for true velocity
model = demo_model('layers-isotropic', ratio=3, shape=shape,
spacing=spacing, nbpml=nbpml,
epsilon=np.zeros(shape),
delta=np.zeros(shape),
theta=np.zeros(shape),
phi=np.zeros(shape))
# Define seismic data and parameters
f0 = .010
dt = model.critical_dt
t0 = 0.0
tn = 350.0
nt = int(1+(tn-t0)/dt)
last = (nt - 2) % 3
indlast = [(last + 1) % 3, last % 3, (last-1) % 3]
# Set up the source as Ricker wavelet for f0
def ricker_source(t, f0):
r = (np.pi * f0 * (t - 1./f0))
return (1-2.*r**2)*np.exp(-r**2)
# Source geometry
time_series = np.zeros((nt, 1))
time_series[:, 0] = ricker_source(np.linspace(t0, tn, nt), f0)
# Adjoint test
source = PointSource(name='src', grid=model.grid, data=time_series,
coordinates=location)
receiver = Receiver(name='rec', grid=model.grid, ntime=nt,
coordinates=receiver_coords)
acoustic = AcousticWaveSolver(model, source=source, receiver=receiver,
time_order=2, space_order=space_order)
rec, u1, _ = acoustic.forward(save=False)
tn = 100.0
nt = int(1 + (tn - t0) / dt)
# Source geometry
time_series = np.zeros((nt, 1))
time_series[:, 0] = 0*ricker_source(np.linspace(t0, tn, nt), f0)
source = PointSource(name='src', grid=model.grid, data=time_series,
coordinates=location)
receiver = Receiver(name='rec', grid=model.grid, ntime=nt,
coordinates=receiver_coords)
acoustic = AcousticWaveSolver(model, source=source, receiver=receiver,
time_order=2, space_order=space_order)
solver_tti = AnisotropicWaveSolver(model, source=source, receiver=receiver,
time_order=2, space_order=space_order)
# Create new wavefield object restart forward computation
u = TimeFunction(name='u', grid=model.grid,
time_order=2, space_order=space_order, dtype=model.dtype)
u.data[0:3, :] = u1.data[indlast, :]
rec, _, _ = acoustic.forward(save=False, u=u)
utti = TimeFunction(name='u', grid=model.grid,
time_order=2, space_order=space_order, dtype=model.dtype)
vtti = TimeFunction(name='v', grid=model.grid,
time_order=2, space_order=space_order, dtype=model.dtype)
utti.data[0:3, :] = u1.data[indlast, :]
vtti.data[0:3, :] = u1.data[indlast, :]
rec_tti, u_tti, v_tti, _ = solver_tti.forward(u=utti, v=vtti)
res = linalg.norm(u.data.reshape(-1) -
.5 * u_tti.data.reshape(-1) - .5 * v_tti.data.reshape(-1))
res /= linalg.norm(u.data.reshape(-1))
log("Difference between acoustic and TTI with all coefficients to 0 %f" % res)
assert np.isclose(res, 0.0, atol=1e-4)