class pylops.signalprocessing.ChirpRadon2D(taxis, haxis, pmax, dtype='float64')[source]

Apply Radon forward (and adjoint) transform using Fast Fourier Transform and Chirp functions to a 2-dimensional array of size $$[n_x \times n_t]$$ (both in forward and adjoint mode).

Note that forward and adjoint are swapped compared to the time-space implementation in pylops.signalprocessing.Radon2D and a direct inverse method is also available for this implementation.

Parameters: taxis : np.ndarray Time axis haxis : np.ndarray Spatial axis pmax : np.ndarray Maximum slope defined as $$\tan$$ of maximum stacking angle in $$x$$ direction $$p_{max} = \tan(\alpha_{x, max})$$. If one operates in terms of minimum velocity $$c_0$$, set $$p_{x, max}=c_0 dy/dt$$. dtype : str, optional Type of elements in input array.

Notes

Refer to  for the theoretical and implementation details.

  Andersson, F and Robertsson J. “Fast $$\tau-p$$ transforms by chirp modulation”, Geophysics, vol 84, NO.1, pp. A13-A17, 2019.
Attributes: shape : tuple Operator shape explicit : bool Operator contains a matrix that can be solved explicitly (True) or not (False)

Methods

 __init__(taxis, haxis, pmax[, dtype]) Initialize this LinearOperator. adjoint() Hermitian adjoint. apply_columns(cols) Apply subset of columns of operator cond([uselobpcg]) Condition number of linear operator. conj() Complex conjugate operator div(y[, niter]) 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. inverse(x) matmat(X) Matrix-matrix multiplication. matvec(x) Matrix-vector multiplication. 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. transpose() Transpose this linear operator.

## Examples using pylops.signalprocessing.ChirpRadon2D¶ 