def test_resample():
shape = (50, 50, 50)
spacing = (10., 10., 10.)
nbl = 10
f0 = 0.01
t0 = 0.0
tn = 500
# Create two-layer model from preset
model = demo_model(preset='layers-isotropic',
vp_top=1.,
vp_bottom=2.,
spacing=spacing,
shape=shape,
nbl=nbl)
time_range = TimeAxis(start=t0, stop=tn, step=model.critical_dt)
src_a = RickerSource(name='src_a',
grid=model.grid,
f0=f0,
time_range=time_range)
time_range_f = TimeAxis(start=t0,
step=time_range.step / (10 * np.sqrt(2)),
stop=time_range.stop)
src_b = RickerSource(name='src_b',
grid=model.grid,
f0=f0,
time_range=time_range_f)
# Test resampling specifying dt.
src_c = src_b.resample(dt=src_a._time_range.step)
end = min(src_a.data.shape[0], src_c.data.shape[0])
assert np.allclose(src_a.data[:end], src_c.data[:end])
assert np.allclose(src_a.data[:end], src_c.data[:end])
# Text resampling based on num
src_d = RickerSource(name='src_d',
grid=model.grid,
f0=f0,
time_range=TimeAxis(start=time_range_f.start,
stop=time_range_f.stop,
num=src_a._time_range.num))
src_e = src_b.resample(num=src_d._time_range.num)
assert np.isclose(src_d._time_range.step, src_e._time_range.step)
assert np.isclose(src_d._time_range.stop, src_e._time_range.stop)
assert src_d._time_range.num == src_e._time_range.num
assert np.allclose(src_d.data, src_e.data)
assert np.allclose(src_d.data, src_e.data)
def run_acoustic_forward(dse=None):
shape = (50, 50, 50)
spacing = (10., 10., 10.)
nbpml = 10
nrec = 101
t0 = 0.0
tn = 250.0
# Create two-layer model from preset
model = demo_model(preset='layers-isotropic', vp_top=3., vp_bottom=4.5,
spacing=spacing, shape=shape, nbpml=nbpml)
# 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 = RickerSource(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] = 20.
# Define receiver geometry (same as source, but spread across x)
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:]
solver = AcousticWaveSolver(model, source=src, receiver=rec, dse=dse, dle='basic')
rec, u, _ = solver.forward(save=False)
return u, rec
def update_devito(self, velIndex=1):
tn, vp = velLoader(velIndex=velIndex, datapath=self.data_path)
self.model.vp = vp
dt = self.model.critical_dt
t0 = 0.0
nt = int(1 + (tn - t0) / dt)
self.virt_timestep = int(nt // self.correction_num)
rec_samp = np.linspace(0., self.model.domain_size[0], num=self.num_rec)
rec_samp = rec_samp[1] - rec_samp[0]
xsrc = 100
time_range = TimeAxis(start=t0, stop=tn, step=dt)
self.src.coordinates.data[0, :] = np.array([xsrc * self.spacing[0], 2 * \
self.spacing[1]]).astype(np.float32)
self.src_zero.data.fill(0.)
self.src_zero.coordinates.data[0, :] = np.array([xsrc * self.spacing[0], 2 * \
self.spacing[1]]).astype(np.float32)
self.rec.coordinates.data[:, 0] = np.linspace(0., self.model.domain_size[0], \
num=self.num_rec)
self.rec.coordinates.data[:, 1:] = self.src.coordinates.data[0, 1:]
self.rec_zero.coordinates.data[:, 0] = np.linspace(0., self.model.domain_size[0], \
num=self.num_rec)
self.rec_zero.coordinates.data[:,
1:] = self.src_zero.coordinates.data[0,
1:]
self.u_HF.data.fill(0.)
self.u_LF.data.fill(0.)
def acoustic_setup(shape=(50, 50, 50), spacing=(15.0, 15.0, 15.0),
tn=500., kernel='OT2', space_order=4, nbpml=10,
constant=False, **kwargs):
nrec = shape[0]
preset = 'constant-isotropic' if constant else 'layers-isotropic'
model = demo_model(preset, space_order=space_order, shape=shape, nbpml=nbpml,
dtype=kwargs.pop('dtype', np.float32), spacing=spacing)
# Derive timestepping from model spacing
dt = model.critical_dt * (1.73 if kernel == 'OT4' else 1.0)
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=0.01, 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
solver = AcousticWaveSolver(model, source=src, receiver=rec, kernel=kernel,
space_order=space_order, **kwargs)
return solver
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 test_acoustic(mkey, shape, kernel, space_order, nbpml):
t0 = 0.0 # Start time
tn = 500. # Final time
nrec = 130 # Number of receivers
# Create model from preset
model = demo_model(spacing=[15. for _ in shape],
dtype=np.float64,
space_order=space_order,
shape=shape,
nbpml=nbpml,
**(presets[mkey]))
# Derive timestepping from model spacing
dt = model.critical_dt * (1.73 if kernel == 'OT4' else 1.0)
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=0.01,
time_range=time_range)
src.coordinates.data[0, :] = np.array(model.domain_size) * .5
src.coordinates.data[0, -1] = 30.
# Define receiver geometry (same as source, but spread across x)
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)
rec.coordinates.data[:, 1:] = src.coordinates.data[0, 1:]
# Create solver object to provide relevant operators
solver = AcousticWaveSolver(model,
source=src,
receiver=rec,
kernel=kernel,
space_order=space_order)
# Create adjoint receiver symbol
srca = Receiver(name='srca',
grid=model.grid,
time_range=solver.source.time_range,
coordinates=solver.source.coordinates.data)
# Run forward and adjoint operators
rec, _, _ = solver.forward(save=False)
solver.adjoint(rec=rec, srca=srca)
# Adjoint test: Verify <Ax,y> matches <x, A^Ty> closely
term1 = np.dot(srca.data.reshape(-1), solver.source.data)
term2 = linalg.norm(rec.data)**2
info('<Ax,y>: %f, <x, A^Ty>: %f, difference: %12.12f, ratio: %f' %
(term1, term2, (term1 - term2) / term1, term1 / term2))
assert np.isclose((term1 - term2) / term1, 0., rtol=1.e-10)
def src(self):
t0, tn, dt = self.time_params
time_range = TimeAxis(start=t0, stop=tn, step=dt) # Discretized time axis
# Define source geometry (center of domain, just below surface)
src = RickerSource(name='src', grid=self.model.grid, f0=0.01,
time_range=time_range, dtype=self.dtype)
src.coordinates.data[0, :] = np.array(self.model.domain_size) * .5
src.coordinates.data[0, -1] = 30.
return src
def rec(self):
nrec = 130 # Number of receivers
t0, tn, dt = self.time_params
time_range = TimeAxis(start=t0, stop=tn, step=dt)
rec = Receiver(name='rec', grid=self.model.grid,
time_range=time_range,
npoint=nrec, dtype=self.dtype)
rec.coordinates.data[:, 0] = np.linspace(0., self.model.domain_size[0], num=nrec)
rec.coordinates.data[:, 1:] = self.src.coordinates.data[0, 1:]
return rec
def iso_acoustic(self, opt):
shape = (101, 101)
extent = (1000, 1000)
origin = (0., 0.)
v = np.empty(shape, dtype=np.float32)
v[:, :51] = 1.5
v[:, 51:] = 2.5
grid = Grid(shape=shape, extent=extent, origin=origin)
t0 = 0.
tn = 1000.
dt = 1.6
time_range = TimeAxis(start=t0, stop=tn, step=dt)
f0 = 0.010
src = RickerSource(name='src',
grid=grid,
f0=f0,
npoint=1,
time_range=time_range)
domain_size = np.array(extent)
src.coordinates.data[0, :] = domain_size * .5
src.coordinates.data[0, -1] = 20.
rec = Receiver(name='rec',
grid=grid,
npoint=101,
time_range=time_range)
rec.coordinates.data[:, 0] = np.linspace(0, domain_size[0], num=101)
rec.coordinates.data[:, 1] = 20.
u = TimeFunction(name="u", grid=grid, time_order=2, space_order=2)
m = Function(name='m', grid=grid)
m.data[:] = 1. / (v * v)
pde = m * u.dt2 - u.laplace
stencil = Eq(u.forward, solve(pde, u.forward))
src_term = src.inject(field=u.forward, expr=src * dt**2 / m)
rec_term = rec.interpolate(expr=u.forward)
op = Operator([stencil] + src_term + rec_term,
opt=opt,
language='openmp')
# Make sure we've indeed generated OpenMP offloading code
assert 'omp target' in str(op)
op(time=time_range.num - 1, dt=dt)
assert np.isclose(norm(rec), 490.55, atol=1e-2, rtol=0)
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 test_acousticJ(shape, space_order):
t0 = 0.0 # Start time
tn = 500. # Final time
nrec = shape[0] # Number of receivers
nbpml = 10 + space_order / 2
spacing = [15. for _ in shape]
# Create two-layer "true" model from preset with a fault 1/3 way down
model = demo_model('layers-isotropic', ratio=3, vp_top=1.5, vp_bottom=2.5,
spacing=spacing, space_order=space_order, shape=shape,
nbpml=nbpml, dtype=np.float64)
# 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 = RickerSource(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] = 30.
# Define receiver geometry (same as source, but spread across x)
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:]
# Create solver object to provide relevant operators
solver = AcousticWaveSolver(model, source=src, receiver=rec,
kernel='OT2', space_order=space_order)
# Create initial model (m0) with a constant velocity throughout
model0 = demo_model('layers-isotropic', ratio=3, vp_top=1.5, vp_bottom=1.5,
spacing=spacing, space_order=space_order, shape=shape,
nbpml=nbpml, dtype=np.float64)
# Compute the full wavefield u0
_, u0, _ = solver.forward(save=True, m=model0.m)
# Compute initial born perturbation from m - m0
dm = model.m.data - model0.m.data
du, _, _, _ = solver.born(dm, m=model0.m)
# Compute gradientfrom initial perturbation
im, _ = solver.gradient(du, u0, m=model0.m)
# Adjoint test: Verify <Ax,y> matches <x, A^Ty> closely
term1 = np.dot(im.data.reshape(-1), dm.reshape(-1))
term2 = linalg.norm(du.data)**2
info('<Ax,y>: %f, <x, A^Ty>: %f, difference: %12.12f, ratio: %f'
% (term1, term2, term1 - term2, term1 / term2))
assert np.isclose(term1 / term2, 1.0, atol=0.001)
def _setup_model_and_acquisition(self, space_order, shape, spacing, nbpml,
tn):
nrec = shape[0]
model = demo_model('layers-isotropic',
space_order=space_order,
shape=shape,
spacing=spacing,
nbpml=nbpml)
self.model = model
t0 = 0.0
time_range = TimeAxis(start=t0, stop=tn, step=self.dt)
self.nt = time_range.num
# Define source geometry (center of domain, just below surface)
src = RickerSource(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]
self.src = src
# Define receiver geometry (spread across x, just below surface)
# We need two receiver fields - one for the true (verification) run
rec_t = Receiver(name='rec',
grid=model.grid,
time_range=time_range,
npoint=nrec)
rec_t.coordinates.data[:, 0] = np.linspace(0.,
model.domain_size[0],
num=nrec)
rec_t.coordinates.data[:, 1:] = src.coordinates.data[0, 1:]
self.rec_t = rec_t
# and the other for the smoothed run
self.rec = Receiver(name='rec',
grid=model.grid,
time_range=time_range,
npoint=nrec,
coordinates=rec_t.coordinates.data)
# Receiver for Gradient
self.rec_g = Receiver(name="rec",
coordinates=self.rec.coordinates.data,
grid=model.grid,
time_range=time_range)
# Gradient symbol
self.grad = Function(name="grad", grid=model.grid)
def test_receiver():
grid = Grid(shape=(3,))
time_range = TimeAxis(start=0., stop=1000., step=0.1)
nreceivers = 3
rec = Receiver(name='rec', grid=grid, time_range=time_range, npoint=nreceivers,
coordinates=[(0.,), (1.,), (2.,)])
rec.data[:] = 1.
pkl_rec = pickle.dumps(rec)
new_rec = pickle.loads(pkl_rec)
assert np.all(new_rec.data == 1)
assert np.all(new_rec.coordinates.data == [[0.], [1.], [2.]])
def overthrust_setup(filename,
kernel='OT2',
space_order=2,
nbpml=40,
**kwargs):
model = from_hdf5(filename,
space_order=space_order,
nbpml=nbpml,
datakey='m0',
dtype=np.float64)
spacing = model.spacing
shape = model.vp.shape
nrec = shape[0]
tn = round(2 * max(model.domain_size) / np.min(model.vp))
# Derive timestepping from model spacing
dt = model.critical_dt * (1.73 if kernel == 'OT4' else 1.0)
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=0.01,
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
solver = AcousticWaveSolver(model,
source=src,
receiver=rec,
kernel=kernel,
space_order=space_order,
**kwargs)
return solver
def overthrust_setup(filename,
kernel='OT2',
tn=4000,
space_order=2,
nbpml=40,
dtype=np.float32,
**kwargs):
model = from_hdf5(filename,
space_order=space_order,
nbpml=nbpml,
datakey='m0',
dtype=dtype)
shape = model.vp.shape
spacing = model.spacing
nrec = shape[0]
# Derive timestepping from model spacing
dt = model.critical_dt * (1.73 if kernel == 'OT4' else 1.0)
t0 = 0.0
time_range = TimeAxis(start=t0, stop=tn, step=dt)
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]
# Create solver object to provide relevant operator
geometry = AcquisitionGeometry(model,
rec_coordinates,
src_coordinates,
t0=0.0,
tn=tn,
src_type='Ricker',
f0=0.010)
solver = AcousticWaveSolver(model,
geometry,
kernel=kernel,
space_order=space_order,
**kwargs)
return solver
def test_position(shape):
t0 = 0.0 # Start time
tn = 500. # Final time
nrec = 130 # Number of receivers
# Create model from preset
model = demo_model('constant-isotropic', spacing=[15. for _ in shape],
shape=shape, nbl=10)
# Derive timestepping from model spacing
dt = model.critical_dt
time_range = TimeAxis(start=t0, stop=tn, step=dt)
# Source and receiver geometries
src_coordinates = np.empty((1, len(shape)))
src_coordinates[0, :] = np.array(model.domain_size) * .5
src_coordinates[0, -1] = 30.
rec_coordinates = np.empty((nrec, len(shape)))
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='Ricker', f0=0.010)
# Create solver object to provide relevant operators
solver = AcousticWaveSolver(model, geometry, time_order=2, space_order=4)
rec, u, _ = solver.forward(save=False)
# Define source geometry (center of domain, just below surface) with 100. origin
src = RickerSource(name='src', grid=model.grid, f0=0.01, time_range=time_range)
src.coordinates.data[0, :] = np.array(model.domain_size) * .5 + 100.
src.coordinates.data[0, -1] = 130.
# Define receiver geometry (same as source, but spread across x)
rec2 = Receiver(name='rec', grid=model.grid, time_range=time_range, npoint=nrec)
rec2.coordinates.data[:, 0] = np.linspace(100., 100. + model.domain_size[0],
num=nrec)
rec2.coordinates.data[:, 1:] = src.coordinates.data[0, 1:]
ox_g, oy_g, oz_g = tuple(o.dtype(o.data+100.) for o in model.grid.origin)
rec1, u1, _ = solver.forward(save=False, src=src, rec=rec2,
o_x=ox_g, o_y=oy_g, o_z=oz_g)
assert(np.allclose(rec.data, rec1.data, atol=1e-5))
def analytic_response():
"""
Computes analytic solution of 2D acoustic wave-equation with Ricker wavelet
peak frequency fpeak, temporal padding 20x per the accuracy notebook:
examples/seismic/acoustic/accuracy.ipynb
u(r,t) = 1/(2 pi) sum[ -i pi H_0^2(k,r) q(w) e^{i w t} dw
where:
r = sqrt{(x_s - x_r)^2 + (z_s - z_r)^2}
w = 2 pi f
q(w) = Fourier transform of Ricker source wavelet
H_0^2(k,r) Hankel function of the second kind
k = w/v (wavenumber)
"""
sx, sz = src_coords[0, :]
rx, rz = rec_coords[0, :]
ntpad = 20 * (nt - 1) + 1
tmaxpad = dt * (ntpad - 1)
time_axis_pad = TimeAxis(start=tmin, stop=tmaxpad, step=dt)
srcpad = RickerSource(name='srcpad',
grid=model.grid,
f0=fpeak,
npoint=1,
time_range=time_axis_pad,
t0w=t0w)
nf = int(ntpad / 2 + 1)
df = 1.0 / tmaxpad
faxis = df * np.arange(nf)
# Take the Fourier transform of the source time-function
R = np.fft.fft(srcpad.wavelet[:])
R = R[0:nf]
nf = len(R)
# Compute the Hankel function and multiply by the source spectrum
U_a = np.zeros((nf), dtype=complex)
for a in range(1, nf - 1):
w = 2 * np.pi * faxis[a]
r = np.sqrt((rx - sx)**2 + (rz - sz)**2)
U_a[a] = -1j * np.pi * hankel2(0.0, w * r / v0) * R[a]
# Do inverse fft on 0:dt:T and you have analytical solution
U_t = 1.0 / (2.0 * np.pi) * np.real(np.fft.ifft(U_a[:], ntpad))
# Note that the analytic solution is scaled by dx^2 to convert to pressure
return (np.real(U_t) * (dx**2)), srcpad
def resample(rec, num):
start, stop = rec._time_range.start, rec._time_range.stop
dt0 = rec._time_range.step
new_time_range = TimeAxis(start=start, stop=stop, num=num)
dt = new_time_range.step
to_interp = np.asarray(rec.data)
data = np.zeros((num, to_interp.shape[1]))
for i in range(to_interp.shape[1]):
tck = interpolate.splrep(rec._time_range.time_values,
to_interp[:, i],
k=3)
data[:, i] = interpolate.splev(new_time_range.time_values, tck)
coords_loc = np.asarray(rec.coordinates.data)
# Return new object
return data, coords_loc
def test_tilted_boundary(self, spec):
"""
Check that gathers for a tilted boundary match those generated with a
conventional horizontal free surface and the same geometry.
"""
tilt, toggle_normals = spec
max_thres = 0.09
avg_thres = 0.006
# Define a physical size
shape = (101, 101, 101) # Number of grid point (nx, ny, nz)
spacing = (10., 10., 10.
) # Grid spacing in m. The domain size is 1x1x1km
origin = (0., 0., 0.)
v = 1.5
model = Model(vp=v,
origin=origin,
shape=shape,
spacing=spacing,
space_order=4,
nbl=10,
bcs="damp")
t0 = 0. # Simulation starts a t=0
tn = 500. # Simulation last 0.5 seconds (500 ms)
dt = model.critical_dt # Time step from model grid spacing
time_range = TimeAxis(start=t0, stop=tn, step=dt)
f0 = 0.010 # Source peak frequency is 10Hz (0.010 kHz)
ref = reference_shot(model, time_range, f0)
tilted = tilted_shot(model,
time_range,
f0,
tilt,
toggle_normals=toggle_normals)
assert np.amax(np.absolute(ref - tilted)) < max_thres
assert np.mean(np.absolute(ref - tilted)) < avg_thres
def main(tilt, toggle_normals):
"""For troubleshooting the sectioning"""
# Define a physical size
shape = (101, 101, 101) # Number of grid point (nx, ny, nz)
spacing = (10., 10., 10.) # Grid spacing in m. The domain size is 1x1x1km
origin = (0., 0., 0.)
v = 1.5
model = Model(vp=v,
origin=origin,
shape=shape,
spacing=spacing,
space_order=4,
nbl=10,
bcs="damp")
t0 = 0. # Simulation starts a t=0
tn = 500. # Simulation last 0.5 seconds (500 ms)
dt = model.critical_dt # Time step from model grid spacing
time_range = TimeAxis(start=t0, stop=tn, step=dt)
f0 = 0.010 # Source peak frequency is 10Hz (0.010 kHz)
ref = reference_shot(model, time_range, f0)
tilted = tilted_shot(model,
time_range,
f0,
tilt,
qc=True,
toggle_normals=toggle_normals)
plt.imshow(ref)
plt.show()
plt.imshow(tilted)
plt.show()
plt.imshow(ref - tilted)
plt.show()
def ib_ref():
"""Generate a high-accuracy immersed-boundary reference"""
# Number of grid point (nx, ny, nz)
shape = (201, 201, 201)
# Grid spacing in m. The domain size is 1x1x1km
spacing = (5., 5., 5.)
# Needs to account for damping layers
origin = (100., 100., 0.)
v = 1.5
model = Model(vp=v,
origin=origin,
shape=shape,
spacing=spacing,
space_order=8,
nbl=10,
bcs="damp")
t0 = 0. # Simulation starts at t=0
tn = 450. # Simulation last 0.45 seconds (450 ms)
dt = 0.6038 # Hardcoded timestep to keep stable
time_range = TimeAxis(start=t0, stop=tn, step=dt)
f0 = 0.015 # Source peak frequency is 15Hz (0.015 kHz)
src, rec = setup_srcrec(model, time_range, f0)
# Define the wavefield with the size of the model and the time dimension
u = TimeFunction(name="u",
grid=model.grid,
time_order=2,
space_order=8,
coefficients='symbolic')
shot, wavefield = ib_shot(model, u, time_range, dt, src, rec)
return shot, wavefield
def tti_setup(shape=(50, 50, 50),
spacing=(20.0, 20.0, 20.0),
tn=250.0,
space_order=4,
nbpml=10,
preset='layers-tti',
**kwargs):
nrec = 101
# Two layer model for true velocity
model = demo_model(preset, shape=shape, spacing=spacing, nbpml=nbpml)
# 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
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,
**kwargs)
def default_setup_iso(npad,
shape,
dtype,
sigma=0,
qmin=0.1,
qmax=100.0,
tmin=0.0,
tmax=2000.0,
bvalue=1.0 / 1000.0,
vvalue=1.5,
space_order=8):
"""
For isotropic propagator build default model with 10m spacing,
and 1.5 m/msec velocity
Return:
dictionary of velocity, buoyancy, and wOverQ
TimeAxis defining temporal sampling
Source locations: one located at center of model, z = 1dz
Receiver locations, one per interior grid intersection, z = 2dz
2D: 1D grid of receivers center z covering interior of model
3D: 2D grid of receivers center z covering interior of model
"""
d = 10.0
origin = tuple([0.0 - d * npad for s in shape])
extent = tuple([d * (s - 1) for s in shape])
# Define dimensions
if len(shape) == 2:
x = SpaceDimension(name='x', spacing=Constant(name='h_x', value=d))
z = SpaceDimension(name='z', spacing=Constant(name='h_z', value=d))
grid = Grid(extent=extent,
shape=shape,
origin=origin,
dimensions=(x, z),
dtype=dtype)
else:
x = SpaceDimension(name='x', spacing=Constant(name='h_x', value=d))
y = SpaceDimension(name='y', spacing=Constant(name='h_y', value=d))
z = SpaceDimension(name='z', spacing=Constant(name='h_z', value=d))
grid = Grid(extent=extent,
shape=shape,
origin=origin,
dimensions=(x, y, z),
dtype=dtype)
b = Function(name='b', grid=grid, space_order=space_order)
v = Function(name='v', grid=grid, space_order=space_order)
b.data[:] = bvalue
v.data[:] = vvalue
dt = dtype("%.6f" % (0.8 * compute_critical_dt(v)))
time_axis = TimeAxis(start=tmin, stop=tmax, step=dt)
# Define coordinates in 2D and 3D
if len(shape) == 2:
nr = shape[0] - 2 * npad
src_coords = np.empty((1, len(shape)), dtype=dtype)
rec_coords = np.empty((nr, len(shape)), dtype=dtype)
src_coords[:, 0] = origin[0] + extent[0] / 2
src_coords[:, 1] = 1 * d
rec_coords[:, 0] = np.linspace(0.0, d * (nr - 1), nr)
rec_coords[:, 1] = 2 * d
else:
# using numpy outer product here for array iteration speed
xx, yy = np.ogrid[:shape[0] - 2 * npad, :shape[1] - 2 * npad]
x1 = np.ones((shape[0] - 2 * npad, 1))
y1 = np.ones((1, shape[1] - 2 * npad))
xcoord = (xx * y1).reshape(-1)
ycoord = (x1 * yy).reshape(-1)
nr = len(xcoord)
src_coords = np.empty((1, len(shape)), dtype=dtype)
rec_coords = np.empty((nr, len(shape)), dtype=dtype)
src_coords[:, 0] = origin[0] + extent[0] / 2
src_coords[:, 1] = origin[1] + extent[1] / 2
src_coords[:, 2] = 1 * d
rec_coords[:, 0] = d * xcoord
rec_coords[:, 1] = d * ycoord
rec_coords[:, 2] = 2 * d
return b, v, time_axis, src_coords, rec_coords
#==============================================================================
vhomo = np.empty(shape,dtype=np.float32)
vhomo[:,:] = vmodelmin
model0 = Model(vp=vhomo,origin=origin,shape=shape,spacing=spacing,space_order=sou,nbl=nbl,bcs="damp")
rec_homo = np.zeros((number_xfontpos,ntmax+1,nrec))
#==============================================================================
if(verbosity>0):
rplot.graph2dvel(model0.vp.data,teste,0,-1)
#==============================================================================
# Construção Parâmetros Temporais do Modelo Hohomogeneo
#==============================================================================
dt_ref0 = model0.critical_dt
dt0 = np.float32((tn-t0)/(ntmax))
time_range0 = TimeAxis(start=t0,stop=tn,step=dt0)
dt = dt0
time_range = time_range0
print("dt: ", dt0, " dt_ref: ", dt_ref0)
#==============================================================================
#==============================================================================
# Construção Fonte de Ricker Modelo Homogeneo
#==============================================================================
src0 = RickerSource(name='src0',grid=model0.grid,f0=f0,npoint=nfonte,time_range=time_range0)
src0.coordinates.data[:, 0] = nxfontposv[0]
src0.coordinates.data[:, 1] = nzfontpos
#==============================================================================
#==============================================================================
# Construção Receivers Homogeneo
def subsampled_gradient(factor=1, tn=2000.):
t0 = 0. # Simulation starts a t=0
shape = (100, 100)
origin = (0., 0.)
spacing = (15., 15.)
space_order = 4
vp = np.empty(shape, dtype=np.float64)
vp[:, :51] = 1.5
vp[:, 51:] = 2.5
model = Model(vp=vp, origin=origin, shape=shape, spacing=spacing,
space_order=space_order, nbl=10)
dt = model.critical_dt # Time step from model grid spacing
time_range = TimeAxis(start=t0, stop=tn, step=dt)
nt = time_range.num # number of time steps
f0 = 0.010 # Source peak frequency is 10Hz (0.010 kHz)
src = RickerSource(
name='src',
grid=model.grid,
f0=f0,
time_range=time_range)
src.coordinates.data[0, :] = np.array(model.domain_size) * .5
src.coordinates.data[0, -1] = 20. # Depth is 20m
rec = Receiver(
name='rec',
grid=model.grid,
npoint=101,
time_range=time_range) # new
rec.coordinates.data[:, 0] = np.linspace(0, model.domain_size[0], num=101)
rec.coordinates.data[:, 1] = 20. # Depth is 20m
save_elements = (nt + factor - 1) // factor
print(save_elements)
time_subsampled = ConditionalDimension(
't_sub', parent=model.grid.time_dim, factor=factor)
usave = TimeFunction(name='usave', grid=model.grid, time_order=2,
space_order=space_order, save=save_elements,
time_dim=time_subsampled)
u = TimeFunction(name="u", grid=model.grid, time_order=2,
space_order=space_order)
pde = model.m * u.dt2 - u.laplace + model.damp * u.dt
stencil = Eq(u.forward, solve(pde, u.forward))
src_term = src.inject(
field=u.forward,
expr=src * dt**2 / model.m,
offset=model.nbl)
rec_term = rec.interpolate(expr=u, offset=model.nbl)
fwd_op = Operator([stencil] + src_term + [Eq(usave, u)] + rec_term,
subs=model.spacing_map) # operator with snapshots
v = TimeFunction(name='v', grid=model.grid, save=None,
time_order=2, space_order=space_order)
grad = Function(name='grad', grid=model.grid)
rev_pde = model.m * v.dt2 - v.laplace + model.damp * v.dt.T
rev_stencil = Eq(v.backward, solve(rev_pde, v.backward))
gradient_update = Inc(grad, - usave.dt2 * v)
s = model.grid.stepping_dim.spacing
receivers = rec.inject(field=v.backward, expr=rec*s**2/model.m)
rev_op = Operator([rev_stencil] + receivers + [gradient_update],
subs=model.spacing_map)
fwd_op(time=nt - 2, dt=model.critical_dt)
rev_op(dt=model.critical_dt, time=nt-16)
return grad.data
def test_full_model():
shape = (50, 50, 50)
spacing = [10. for _ in shape]
nbl = 10
# Create two-layer model from preset
model = demo_model(preset='layers-isotropic',
vp_top=1.,
vp_bottom=2.,
spacing=spacing,
shape=shape,
nbl=nbl)
# Test Model pickling
pkl_model = pickle.dumps(model)
new_model = pickle.loads(pkl_model)
assert np.isclose(np.linalg.norm(model.vp.data[:] - new_model.vp.data[:]),
0)
f0 = .010
dt = model.critical_dt
t0 = 0.0
tn = 350.0
time_range = TimeAxis(start=t0, stop=tn, step=dt)
# Test TimeAxis pickling
pkl_time_range = pickle.dumps(time_range)
new_time_range = pickle.loads(pkl_time_range)
assert np.isclose(np.linalg.norm(time_range.time_values),
np.linalg.norm(new_time_range.time_values))
# Test Class Constant pickling
pkl_origin = pickle.dumps(model.grid.origin)
new_origin = pickle.loads(pkl_origin)
for a, b in zip(model.grid.origin, new_origin):
assert a.compare(b) == 0
# Test Class TimeDimension pickling
time_dim = TimeDimension(name='time',
spacing=Constant(name='dt', dtype=np.float32))
pkl_time_dim = pickle.dumps(time_dim)
new_time_dim = pickle.loads(pkl_time_dim)
assert time_dim.spacing._value == new_time_dim.spacing._value
# Test Class SteppingDimension
stepping_dim = SteppingDimension(name='t', parent=time_dim)
pkl_stepping_dim = pickle.dumps(stepping_dim)
new_stepping_dim = pickle.loads(pkl_stepping_dim)
assert stepping_dim.is_Time == new_stepping_dim.is_Time
# Test Grid pickling
pkl_grid = pickle.dumps(model.grid)
new_grid = pickle.loads(pkl_grid)
assert model.grid.shape == new_grid.shape
assert model.grid.extent == new_grid.extent
assert model.grid.shape == new_grid.shape
for a, b in zip(model.grid.dimensions, new_grid.dimensions):
assert a.compare(b) == 0
ricker = RickerSource(name='src',
grid=model.grid,
f0=f0,
time_range=time_range)
pkl_ricker = pickle.dumps(ricker)
new_ricker = pickle.loads(pkl_ricker)
assert np.isclose(np.linalg.norm(ricker.data),
np.linalg.norm(new_ricker.data))
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, 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)
spacing=spacing,
nbpml=40)
model0 = demo_model('circle-isotropic',
vp=2.5,
vp_background=2.5,
origin=origin,
shape=shape,
spacing=spacing,
nbpml=40,
grid=model.grid)
# time
t0 = 0.
tn = 1000.
f0 = 0.010
time_axis = TimeAxis(start=t0, stop=tn, step=model.critical_dt)
nt = time_axis.num
# source
nshots = 9
src_coordinates = np.empty((1, 2))
source_locations = np.empty((nshots, 2), dtype=np.float32)
source_locations[:, 0] = 30.
source_locations[:, 1] = np.linspace(0., 1000, num=nshots)
# receiver
nreceivers = 101
rec_coordinates = np.empty((nreceivers, 2))
rec_coordinates[:, 1] = np.linspace(0, model.domain_size[0], num=nreceivers)
rec_coordinates[:, 0] = 980.
def _setup_devito(self, velIndex=0):
origin = (0.0, 0.0)
self.spacing = (7.5, 7.5)
self.nbpml = 40
self.num_rec = 401
self.shape = [
self.image_size0 - 2 * self.nbpml,
self.image_size1 - 2 * self.nbpml
]
tn, vp = velLoader(velIndex=velIndex, datapath=self.data_path)
self.model = Model(origin,
self.spacing,
self.shape,
2,
vp,
nbpml=self.nbpml)
dt = self.model.critical_dt
t0 = 0.0
nt = int(1 + (tn - t0) / dt)
self.virt_timestep = int(nt // self.correction_num)
rec_samp = np.linspace(0., self.model.domain_size[0], num=self.num_rec)
rec_samp = rec_samp[1] - rec_samp[0]
xsrc = 100
time_range = TimeAxis(start=t0, stop=tn, step=dt)
self.src = RickerSource(name='src',
grid=self.model.grid,
f0=0.025,
time_range=time_range,
space_order=1,
npoint=1)
self.src.coordinates.data[0, :] = np.array([xsrc * self.spacing[0], 2 * \
self.spacing[1]]).astype(np.float32)
self.src_zero = RickerSource(name='src_zero',
grid=self.model.grid,
f0=0.025,
time_range=time_range,
space_order=1,
npoint=1)
self.src_zero.data.fill(0.)
self.src_zero.coordinates.data[0, :] = np.array([xsrc * self.spacing[0], 2 * \
self.spacing[1]]).astype(np.float32)
self.rec = Receiver(name='rec', grid=self.model.grid, time_range=time_range, \
npoint=self.num_rec)
self.rec.coordinates.data[:, 0] = np.linspace(0., self.model.domain_size[0], \
num=self.num_rec)
self.rec.coordinates.data[:, 1:] = self.src.coordinates.data[0, 1:]
self.rec_zero = Receiver(name='rec_zero', grid=self.model.grid, time_range=time_range, \
npoint=self.num_rec)
self.rec_zero.coordinates.data[:, 0] = np.linspace(0., self.model.domain_size[0], \
num=self.num_rec)
self.rec_zero.coordinates.data[:,
1:] = self.src_zero.coordinates.data[0,
1:]
self.solverLF = AcousticWaveSolver(self.model, source=self.src, receiver=self.rec, \
kernel='OT2', space_order=2)
self.solverHF = AcousticWaveSolver(self.model, source=self.src, receiver=self.rec, \
kernel='OT2', space_order=20)
self.u_HF = TimeFunction(name="u",
grid=self.model.grid,
time_order=2,
space_order=20)
self.u_LF = TimeFunction(name="u",
grid=self.model.grid,
time_order=2,
space_order=2)
