pylops.waveeqprocessing.BlendingContinuous#
- class pylops.waveeqprocessing.BlendingContinuous(nt, nr, ns, dt, times, dtype='float64', name='B')[source]#
Continuous blending operator
Blend seismic shot gathers in continuous mode based on pre-defined sequence of firing times. The size of input model vector must be \(n_s \times n_r \times n_t\), whilst the size of the data vector is \(n_r \times n_{t,tot}\).
- Parameters
- nt
int Number of time samples
- nr
int Number of receivers
- ns
int Number of sources
- dt
float Time sampling in seconds
- times
np.ndarray Absolute ignition times for each source
- nproc
int, optional Number of processors used when applying operator
- dtype
str, optional Operator dtype
- name
str, optional Name of operator (to be used by
pylops.utils.describe.describe)
- nt
Notes
Simultaneous shooting or blending is the process of acquiring seismic data firing consecutive sources at short time intervals (shorter than the time requires for all significant waves to come back from the Earth interior).
Continuous blending refers to an acquisition scenario where a source towed behind a single vessel is fired at irregular time intervals (
times) to create a continuous recording whose modelling operator is\[\Phi = [\Phi_1, \Phi_2, ..., \Phi_N]\]where each \(\Phi_i\) operator applies a time-shift equal to the absolute ignition time provided in the variable
times.Methods
__init__(nt, nr, ns, dt, times[, dtype, name])adjoint()apply_columns(cols)Apply subset of columns of operator
cond([uselobpcg])Condition number of linear operator.
conj()Complex conjugate operator
div(y[, niter, densesolver])Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(x)Matrix-matrix or matrix-vector multiplication.
eigs([neigs, symmetric, niter, uselobpcg])Most significant eigenvalues of linear operator.
matmat(X)Matrix-matrix multiplication.
matvec(x)Matrix-vector multiplication.
reset_count()Reset counters
rmatmat(X)Matrix-matrix multiplication.
rmatvec(x)Adjoint matrix-vector multiplication.
todense([backend])Return dense matrix.
toimag([forw, adj])Imag operator
toreal([forw, adj])Real operator
tosparse()Return sparse matrix.
trace([neval, method, backend])Trace of linear operator.
transpose()
