pymarchenko.imaging.MarchenkoImaging

pymarchenko.imaging.MarchenkoImaging(vsx, vsz, r, s, dr, dt, nt, vel, toff, nsmooth, wav, wav_c, nfmax, igaths, nalpha, jt, data, kind='mck', niter=10, nproc=1, nthreads=1)

Marchenko imaging

Perform one of Marchenko’s redatuming techiniques and apply multi-dimensional deconvolution to the retrieved Green’s functions to obtain the local reflection response. This routine can be run for multiple depth levels and both images and angle-gathers can be produced as outputs

Parameters:

vsxnumpy.ndarray

X-coordinates of virtual sources

vsznumpy.ndarray

Z-coordinates of virtual sources

rnumpy.ndarray

Receiver array

snumpy.ndarray

Source array

drfloat

Sampling of receiver integration axis

dtfloat

Sampling of time integration axis

ntint

Number of samples in time (not required if R is in time)

velnumpy.ndarray

Velocity model

tofffloat

Time-offset to apply to traveltime

nsmoothint

Number of samples of smoothing operator to apply to window

wavnumpy.ndarray, optional

Wavelet to apply to direct arrival when created using trav

wav_cint

Index of center of wavelet

nfmaxint

Index of max frequency to include in deconvolution process

igathslist, optional

Indices of x-axis along which to compute angle gathers

nalphaint

Indices of x-axis along which to compute angle gathers

jtint

Subsampling to apply to time axis of inputs wavefields for MDD

datanumpy.ndarray

Reflection data

kindstr, optional

Marchenko algorithm to apply (mck, nmck, or rmck)

niterint, optional

Number of iterations of Marchenko scheme

nprocint, optional

Number of processes

nthreadint, optional

Number of threads per process

Returns:

issnumpy.ndarray

Single-scattering image

imcknumpy.ndarray

Marchenko image

assnumpy.ndarray, optional

Single-scattering angle gathers

amcknumpy.ndarray, optional

Marchenko angle gathers