def create_operators(self):
if not self.sequential:
self.mixing_weights = np.zeros([self.nsrc], dtype=np.float32)
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*self.spacing[1]
for s in range(self.nsrc):
self.mixing_weights[s] = np.random.randn()/np.sqrt(self.nsrc)
self.src.data[:, s] *= self.mixing_weights[s]
def f(dm=self.dm, model0=self.model0, src=self.src, rec=self.rec):
return self.forward(self.T(dm), model0, src, rec, device=self.device)
else:
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*self.spacing[1]
for s in range(self.nsrc):
if s != self.seq_src_idx:
self.src.data[:, s] *= 0.0
self.seq_src_idx += 1
def f(dm=self.dm, model0=self.model0, src=self.src, rec=self.rec):
return self.forward(self.T(dm), model0, src, rec, device=self.device)
return f
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
model0 = Model(shape=shape,
origin=origin,
spacing=spacing,
vp=np.sqrt(1 / m0),
dm=dm)
# 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,
v2 = np.empty(shape, dtype=np.float32) v2[:, :41] = 1.5 v2[:, 41:71] = 2.5 v2[:, 71:] = 4.5 model2 = Model(shape=shape, origin=origin, spacing=spacing, vp=v2, rho=rho) # Time axis t0 = 0. tn = 1000. dt = model2.critical_dt nt = int(1 + (tn - t0) / dt) time_axis = np.linspace(t0, tn, nt) # Source f1 = 0.008 src1 = RickerSource(name='src', grid=model1.grid, f0=f1, time=time_axis) src1.coordinates.data[0, :] = np.array(model1.domain_size) * 0.5 src1.coordinates.data[0, -1] = 20. f2 = 0.012 src2 = RickerSource(name='src', grid=model2.grid, f0=f2, time=time_axis) src2.coordinates.data[0, :] = np.array(model2.domain_size) * 0.5 src2.coordinates.data[0, -1] = 20. # Receiver for observed data rec_t = Receiver(name='rec_t', grid=model1.grid, npoint=401, ntime=nt) rec_t.coordinates.data[:, 0] = np.linspace(100, 900, num=401) rec_t.coordinates.data[:, 1] = 20. # Test data and source d_hat, _ = forward_modeling(model1,