Source code for pylops.waveeqprocessing.lsm

__all__ = ["LSM"]

import logging

from scipy.sparse.linalg import lsqr

from pylops.utils import dottest as Dottest
from pylops.waveeqprocessing.kirchhoff import Kirchhoff
from pylops.waveeqprocessing.twoway import AcousticWave2D

    import skfmm
except ModuleNotFoundError:
    skfmm = None
    skfmm_message = (
        "Skfmm package not installed. Choose method=analytical "
        "if using constant velocity or run "
        '"pip install scikit-fmm" or '
        '"conda install -c conda-forge scikit-fmm".'
except Exception as e:
    skfmm = None
    skfmm_message = f"Failed to import skfmm (error:{e})."

logging.basicConfig(format="%(levelname)s: %(message)s", level=logging.WARNING)

[docs]class LSM: r"""Least-squares Migration (LSM). Solve seismic migration as inverse problem given smooth velocity model ``vel`` and an acquisition setup identified by sources (``src``) and receivers (``recs``). Parameters ---------- z : :obj:`numpy.ndarray` Depth axis x : :obj:`numpy.ndarray` Spatial axis t : :obj:`numpy.ndarray` Time axis for data srcs : :obj:`numpy.ndarray` Sources in array of size :math:`\lbrack 2(3) \times n_s \rbrack` recs : :obj:`numpy.ndarray` Receivers in array of size :math:`\lbrack 2(3) \times n_r \rbrack` vel : :obj:`numpy.ndarray` or :obj:`float` Velocity model of size :math:`\lbrack (n_y \times)\, n_x \times n_z \rbrack` (or constant) wav : :obj:`numpy.ndarray` Wavelet wavcenter : :obj:`int` Index of wavelet center y : :obj:`numpy.ndarray` Additional spatial axis (for 3-dimensional problems) kind : :str`, optional Kind of modelling operator (``kirchhoff``, ``twowayac``) mode : :obj:`str`, optional Computation mode (``eikonal``, ``analytic`` - only for constant velocity) engine : :obj:`str`, optional Engine used for computations (``numpy`` or ``numba``) when ``kind=kirchhoff`` is used dottest : :obj:`bool`, optional Apply dot-test Attributes ---------- Demop : :class:`pylops.LinearOperator` Demigration operator operator See Also -------- pylops.waveeqprocessing.Kirchhoff : Kirchhoff operator pylops.waveeqprocessing.AcousticWave2D : AcousticWave2D operator Notes ----- Inverting a demigration operator is generally referred in the literature as least-squares migration (LSM) as historically a least-squares cost function has been used for this purpose. In practice any other cost function could be used, for examples if ``solver='pylops.optimization.sparsity.FISTA'`` a sparse representation of reflectivity is produced as result of the inversion. This routines provides users with a easy-to-use, out-of-the-box least-squares migration application that currently implements: - Kirchhoff LSM: this problem is parametrized in terms of reflectivity (i.e., vertical derivative of the acoustic impedance - or velocity in case of constant density). Currently, a ray-based modelling engine is used for this case (see :class:`pylops.waveeqprocessing.Kirchhoff`). - Born LSM: this problem is parametrized in terms of squared slowness perturbation (in the constant density case) and it is solved using an acoustic two-way eave equation modelling engine (see :class:`pylops.waveeqprocessing.AcousticWave2D`). The following table shows the current status of the LSM application: +------------------+----------------------+-----------+------------+ | | Kirchhoff integral | WKBJ | Wave eq | +==================+======================+===========+============+ | Reflectivity | V | X | X | +------------------+----------------------+-----------+------------+ | Slowness-squared | X | X | V | +------------------+----------------------+-----------+------------+ Finally, it is worth noting that for both cases the first iteration of an iterative scheme aimed at inverting the demigration operator is a simple a projection of the recorded data into the model domain. An approximate (band-limited) image of the subsurface is therefore created. This process is referred to in the literature as *migration*. """ def __init__( self, z, x, t, srcs, recs, vel, wav, wavcenter, y=None, kind="kirchhoff", mode="eikonal", engine="numba", dottest=False, ): self.y, self.x, self.z = y, x, z if kind == "kirchhoff": self.Demop = Kirchhoff( z, x, t, srcs, recs, vel, wav, wavcenter, y=y, mode=mode, engine=engine, ) elif kind == "twowayac": shape = (len(x), len(z)) origin = (x[0], z[0]) spacing = (x[1] - x[0], z[1] - z[0]) self.Demop = AcousticWave2D( shape, origin, spacing, vel, srcs[0], srcs[1], recs[0], recs[1], t[0], len(t), ) else: raise NotImplementedError("kind must be kirchhoff or twowayac") if dottest: Dottest( self.Demop, self.Demop.shape[0], self.Demop.shape[1], raiseerror=True, verb=True, ) def solve(self, d, solver=lsqr, **kwargs_solver): r"""Solve least-squares migration equations with chosen ``solver`` Parameters ---------- d : :obj:`numpy.ndarray` Input data of size :math:`\lbrack n_s \times n_r \times n_t \rbrack` solver : :obj:`func`, optional Solver to be used for inversion **kwargs_solver Arbitrary keyword arguments for chosen ``solver`` Returns ------- minv : :obj:`np.ndarray` Inverted reflectivity model of size :math:`\lbrack (n_y \times) n_x \times n_z \rbrack` """ minv = solver(self.Demop, d.ravel(), **kwargs_solver)[0] if self.y is None: minv = minv.reshape(len(self.x), len(self.z)) else: minv = minv.reshape(len(self.y), len(self.x), len(self.z)) return minv