def PoststackInversion(data,
wav,
m0=None,
explicit=False,
simultaneous=False,
epsI=None,
epsR=None,
dottest=False,
**kwargs_solver):
r"""Post-stack linearized seismic inversion.
Invert post-stack seismic operator to retrieve an acoustic
impedance profile from band-limited seismic post-stack data.
Depending on the choice of input parameters, inversion can be
trace-by-trace with explicit operator or global with either
explicit or linear operator.
Parameters
----------
data : :obj:`np.ndarray`
Band-limited seismic post-stack data of size :math:`[n_{t0} \times n_x]`
wav : :obj:`np.ndarray`
Wavelet in time domain (must had odd number of elements and centered to zero)
m0 : :obj:`np.ndarray`, optional
Background model of size :math:`[n_{t0} \times n_x]`
explicit : :obj:`bool`, optional
Create a chained linear operator (``False``, preffered for large data)
or a ``MatrixMult`` linear operator with dense matrix
(``True``, preffered for small data)
simultaneous : :obj:`bool`, optional
Simultaneously invert entire data (``True``) or invert
trace-by-trace (``False``) when using ``explicit`` operator
(note that the entire data is always inverted when working with linear operator)
epsI : :obj:`float`, optional
Damping factor for Tikhonov regularization term
epsR : :obj:`float`, optional
Damping factor for additional Laplacian regularization term
dottest : :obj:`bool`, optional
Apply dot-test
**kwargs_solver
Arbitrary keyword arguments for :py:func:`scipy.linalg.lstsq`
solver (if ``explicit=True`` and ``epsR=None``)
or :py:func:`scipy.sparse.linalg.lsqr` solver (if ``explicit=False``
and/or ``epsR`` is not ``None``))
Returns
-------
minv : :obj:`np.ndarray`
Inverted model of size :math:`[n_{t0} \times n_x]`
dr : :obj:`np.ndarray`
Residual data (i.e., data - background data) of
size :math:`[n_{t0} \times n_x]`
Notes
-----
The cost function and solver used in the seismic post-stack inversion module depends on the choice of
``explicit``, ``simultaneous``, ``epsI``, and ``epsR`` parameters:
* ``explicit=True``, ``epsI=None`` and ``epsR=None``: the explicit solver :py:func:`scipy.linalg.lstsq`
is used if ``simultaneous=False`` (or the iterative solver :py:func:`scipy.sparse.linalg.lsqr`
is used if ``simultaneous=True``)
* ``explicit=True`` with ``epsI`` and ``epsR=None``: the regularized normal equations
:math:`\mathbf{W}^T\mathbf{d} = (\mathbf{W}^T \mathbf{W} + \epsilon_I^2 \mathbf{I}) \mathbf{AI}`
are instead fed into the :py:func:`scipy.linalg.lstsq` solver if ``simultaneous=False``
(or the iterative solver :py:func:`scipy.sparse.linalg.lsqr` if ``simultaneous=True``)
* ``explicit=False`` and ``epsR=None``: the iterative solver :py:func:`scipy.sparse.linalg.lsqr` is used
* ``explicit=False`` with ``epsR``: the iterative solver
:py:func:`pylops.optimization.leastsquares.RegularizedInversion` is used
Note that the convergence of iterative solvers such as :py:func:`scipy.sparse.linalg.lsqr`
can be very slow for this type of operator. It is suggested to take a two steps
approach with first a trace-by-trace inversion using the explicit operator,
followed by a regularized global inversion using the outcome of the previous
inversion as initial guess.
"""
if data.ndim == 1:
dims = 1
nt0 = data.size
nspat = None
nother = nx = 1
elif data.ndim == 2:
dims = 2
nt0, nx = data.shape
nspat = (nx, )
nother = nx
else:
dims = 3
nt0, nx, ny = data.shape
nspat = (nx, ny)
nother = nx * ny
data = data.reshape(nt0, nother)
# check if background model and data have same shape
if m0 is not None and data.shape != m0.shape:
raise ValueError('data and m0 must have same shape')
# create operator
PPop = PoststackLinearModelling(wav,
nt0=nt0,
ndims=nspat,
explicit=explicit)
if dottest:
assert Dottest(PPop, nt0 * nother, nt0 * nother, verb=True)
# create and remove background data from original data
datar = data.flatten(
) if m0 is None else data.flatten() - PPop * m0.flatten()
# invert model
if epsR is None:
# inversion without spatial regularization
if explicit:
if epsI is None and not simultaneous:
# solve unregularized equations indipendently trace-by-trace
minv = lstsq(PPop.A,
datar.reshape(nt0, nother).squeeze(),
**kwargs_solver)[0]
elif epsI is None and simultaneous:
# solve unregularized equations simultaneously
minv = lsqr(PPop, datar, **kwargs_solver)[0]
elif epsI is not None:
# create regularized normal equations
PP = np.dot(PPop.A.T, PPop.A) + epsI * np.eye(nt0)
datar = np.dot(PPop.A.T, datar.reshape(nt0, nother))
if not simultaneous:
# solve regularized normal equations indipendently trace-by-trace
minv = lstsq(PP, datar.reshape(nt0, nother),
**kwargs_solver)[0]
else:
# solve regularized normal equations simultaneously
PPop_reg = MatrixMult(PP, dims=nother)
minv = lsqr(PPop_reg, datar.flatten(), **kwargs_solver)[0]
else:
# create regularized normal equations and solve them simultaneously
PP = np.dot(PPop.A, PPop.A) + epsI * np.eye(nt0)
datar = PPop.A.T * datar.reshape(nt0, nother)
PPop_reg = MatrixMult(PP, dims=nother)
minv = lstsq(PPop_reg, datar.flatten(), **kwargs_solver)[0]
else:
# solve unregularized normal equations simultaneously with lop
minv = lsqr(PPop, datar, **kwargs_solver)[0]
else:
# inversion with spatial regularization
if dims == 1:
Regop = SecondDerivative(nt0, dtype=PPop.dtype)
elif dims == 2:
Regop = Laplacian((nt0, nx), dtype=PPop.dtype)
else:
Regop = Laplacian((nt0, nx, ny), dirs=(1, 2), dtype=PPop.dtype)
minv = RegularizedInversion(PPop, [Regop],
data.flatten(),
x0=m0.flatten(),
epsRs=[epsR],
returninfo=False,
**kwargs_solver)
if dims == 1:
minv = minv.squeeze()
datar = datar.squeeze()
elif dims == 2:
minv = minv.reshape(nt0, nx)
datar = datar.reshape(nt0, nx)
else:
minv = minv.reshape(nt0, nx, ny)
datar = datar.reshape(nt0, nx, ny)
if m0 is not None and epsR is None:
minv = minv + m0
return minv, datar
def PoststackInversion(data,
wav,
m0=None,
explicit=False,
simultaneous=False,
epsI=None,
epsR=None,
dottest=False,
epsRL1=None,
**kwargs_solver):
r"""Post-stack linearized seismic inversion.
Invert post-stack seismic operator to retrieve an elastic parameter of
choice from band-limited seismic post-stack data.
Depending on the choice of input parameters, inversion can be
trace-by-trace with explicit operator or global with either
explicit or linear operator.
Parameters
----------
data : :obj:`np.ndarray`
Band-limited seismic post-stack data of size
:math:`[n_{t0} (\times n_x \times n_y)]`
wav : :obj:`np.ndarray`
Wavelet in time domain (must have odd number of elements
and centered to zero). If 1d, assume stationary wavelet for the entire
time axis. If 2d of size :math:`[n_{t0} \times n_h]` use as
non-stationary wavelet
m0 : :obj:`np.ndarray`, optional
Background model of size :math:`[n_{t0} (\times n_x \times n_y)]`
explicit : :obj:`bool`, optional
Create a chained linear operator (``False``, preferred for large data)
or a ``MatrixMult`` linear operator with dense matrix
(``True``, preferred for small data)
simultaneous : :obj:`bool`, optional
Simultaneously invert entire data (``True``) or invert
trace-by-trace (``False``) when using ``explicit`` operator
(note that the entire data is always inverted when working
with linear operator)
epsI : :obj:`float`, optional
Damping factor for Tikhonov regularization term
epsR : :obj:`float`, optional
Damping factor for additional Laplacian regularization term
dottest : :obj:`bool`, optional
Apply dot-test
epsRL1 : :obj:`float`, optional
Damping factor for additional blockiness regularization term
**kwargs_solver
Arbitrary keyword arguments for :py:func:`scipy.linalg.lstsq`
solver (if ``explicit=True`` and ``epsR=None``)
or :py:func:`scipy.sparse.linalg.lsqr` solver (if ``explicit=False``
and/or ``epsR`` is not ``None``)
Returns
-------
minv : :obj:`np.ndarray`
Inverted model of size :math:`[n_{t0} (\times n_x \times n_y)]`
datar : :obj:`np.ndarray`
Residual data (i.e., data - background data) of
size :math:`[n_{t0} (\times n_x \times n_y)]`
Notes
-----
The cost function and solver used in the seismic post-stack inversion
module depends on the choice of ``explicit``, ``simultaneous``, ``epsI``,
and ``epsR`` parameters:
* ``explicit=True``, ``epsI=None`` and ``epsR=None``: the explicit
solver :py:func:`scipy.linalg.lstsq` is used if ``simultaneous=False``
(or the iterative solver :py:func:`scipy.sparse.linalg.lsqr` is used
if ``simultaneous=True``)
* ``explicit=True`` with ``epsI`` and ``epsR=None``: the regularized
normal equations :math:`\mathbf{W}^T\mathbf{d} = (\mathbf{W}^T
\mathbf{W} + \epsilon_I^2 \mathbf{I}) \mathbf{AI}` are instead fed
into the :py:func:`scipy.linalg.lstsq` solver if ``simultaneous=False``
(or the iterative solver :py:func:`scipy.sparse.linalg.lsqr`
if ``simultaneous=True``)
* ``explicit=False`` and ``epsR=None``: the iterative solver
:py:func:`scipy.sparse.linalg.lsqr` is used
* ``explicit=False`` with ``epsR`` and ``epsRL1=None``: the iterative
solver :py:func:`pylops.optimization.leastsquares.RegularizedInversion`
is used to solve the spatially regularized problem.
* ``explicit=False`` with ``epsR`` and ``epsRL1``: the iterative
solver :py:func:`pylops.optimization.sparsity.SplitBregman`
is used to solve the blockiness-promoting (in vertical direction)
and spatially regularized (in additional horizontal directions) problem.
Note that the convergence of iterative solvers such as
:py:func:`scipy.sparse.linalg.lsqr` can be very slow for this type of
operator. It is suggested to take a two steps approach with first a
trace-by-trace inversion using the explicit operator, followed by a
regularized global inversion using the outcome of the previous
inversion as initial guess.
"""
ncp = get_array_module(wav)
# check if background model and data have same shape
if m0 is not None and data.shape != m0.shape:
raise ValueError('data and m0 must have same shape')
# find out dimensions
if data.ndim == 1:
dims = 1
nt0 = data.size
nspat = None
nspatprod = nx = 1
elif data.ndim == 2:
dims = 2
nt0, nx = data.shape
nspat = (nx, )
nspatprod = nx
else:
dims = 3
nt0, nx, ny = data.shape
nspat = (nx, ny)
nspatprod = nx * ny
data = data.reshape(nt0, nspatprod)
# create operator
PPop = PoststackLinearModelling(wav,
nt0=nt0,
spatdims=nspat,
explicit=explicit)
if dottest:
Dottest(PPop,
nt0 * nspatprod,
nt0 * nspatprod,
raiseerror=True,
backend=get_module_name(ncp),
verb=True)
# create and remove background data from original data
datar = data.flatten() if m0 is None else \
data.flatten() - PPop * m0.flatten()
# invert model
if epsR is None:
# inversion without spatial regularization
if explicit:
if epsI is None and not simultaneous:
# solve unregularized equations indipendently trace-by-trace
minv = \
get_lstsq(data)(PPop.A, datar.reshape(nt0, nspatprod).squeeze(),
**kwargs_solver)[0]
elif epsI is None and simultaneous:
# solve unregularized equations simultaneously
if ncp == np:
minv = lsqr(PPop, datar, **kwargs_solver)[0]
else:
minv = \
cgls(PPop, datar,
x0=ncp.zeros(int(PPop.shape[1]), PPop.dtype),
**kwargs_solver)[0]
elif epsI is not None:
# create regularized normal equations
PP = ncp.dot(PPop.A.T, PPop.A) + \
epsI * ncp.eye(nt0, dtype=PPop.A.dtype)
datarn = ncp.dot(PPop.A.T, datar.reshape(nt0, nspatprod))
if not simultaneous:
# solve regularized normal eqs. trace-by-trace
minv = get_lstsq(data)(PP, datarn, **kwargs_solver)[0]
else:
# solve regularized normal equations simultaneously
PPop_reg = MatrixMult(PP, dims=nspatprod)
if ncp == np:
minv = lsqr(PPop_reg, datar.ravel(),
**kwargs_solver)[0]
else:
minv = cgls(PPop_reg,
datar.ravel(),
x0=ncp.zeros(int(PPop_reg.shape[1]),
PPop_reg.dtype),
**kwargs_solver)[0]
else:
# create regularized normal eqs. and solve them simultaneously
PP = ncp.dot(PPop.A.T, PPop.A) + \
epsI * ncp.eye(nt0, dtype=PPop.A.dtype)
datarn = PPop.A.T * datar.reshape(nt0, nspatprod)
PPop_reg = MatrixMult(PP, dims=nspatprod)
minv = \
get_lstsq(data)(PPop_reg.A, datarn.flatten(),
**kwargs_solver)[0]
else:
# solve unregularized normal equations simultaneously with lop
if ncp == np:
minv = lsqr(PPop, datar, **kwargs_solver)[0]
else:
minv = \
cgls(PPop, datar,
x0=ncp.zeros(int(PPop.shape[1]), PPop.dtype),
**kwargs_solver)[0]
else:
if epsRL1 is None:
# L2 inversion with spatial regularization
if dims == 1:
Regop = SecondDerivative(nt0, dtype=PPop.dtype)
elif dims == 2:
Regop = Laplacian((nt0, nx), dtype=PPop.dtype)
else:
Regop = Laplacian((nt0, nx, ny), dirs=(1, 2), dtype=PPop.dtype)
minv = RegularizedInversion(
PPop, [Regop],
data.flatten(),
x0=None if m0 is None else m0.flatten(),
epsRs=[epsR],
returninfo=False,
**kwargs_solver)
else:
# Blockiness-promoting inversion with spatial regularization
if dims == 1:
RegL1op = FirstDerivative(nt0,
kind='forward',
dtype=PPop.dtype)
RegL2op = None
elif dims == 2:
RegL1op = FirstDerivative(nt0 * nx,
dims=(nt0, nx),
dir=0,
kind='forward',
dtype=PPop.dtype)
RegL2op = SecondDerivative(nt0 * nx,
dims=(nt0, nx),
dir=1,
dtype=PPop.dtype)
else:
RegL1op = FirstDerivative(nt0 * nx * ny,
dims=(nt0, nx, ny),
dir=0,
kind='forward',
dtype=PPop.dtype)
RegL2op = Laplacian((nt0, nx, ny),
dirs=(1, 2),
dtype=PPop.dtype)
if 'mu' in kwargs_solver.keys():
mu = kwargs_solver['mu']
kwargs_solver.pop('mu')
else:
mu = 1.
if 'niter_outer' in kwargs_solver.keys():
niter_outer = kwargs_solver['niter_outer']
kwargs_solver.pop('niter_outer')
else:
niter_outer = 3
if 'niter_inner' in kwargs_solver.keys():
niter_inner = kwargs_solver['niter_inner']
kwargs_solver.pop('niter_inner')
else:
niter_inner = 5
if not isinstance(epsRL1, (list, tuple)):
epsRL1 = list([epsRL1])
if not isinstance(epsR, (list, tuple)):
epsR = list([epsR])
minv = SplitBregman(PPop, [RegL1op],
data.ravel(),
RegsL2=[RegL2op],
epsRL1s=epsRL1,
epsRL2s=epsR,
mu=mu,
niter_outer=niter_outer,
niter_inner=niter_inner,
x0=None if m0 is None else m0.flatten(),
**kwargs_solver)[0]
# compute residual
if epsR is None:
datar -= PPop * minv.ravel()
else:
datar = data.ravel() - PPop * minv.ravel()
# reshape inverted model and residual data
if dims == 1:
minv = minv.squeeze()
datar = datar.squeeze()
elif dims == 2:
minv = minv.reshape(nt0, nx)
datar = datar.reshape(nt0, nx)
else:
minv = minv.reshape(nt0, nx, ny)
datar = datar.reshape(nt0, nx, ny)
if m0 is not None and epsR is None:
minv = minv + m0
return minv, datar