An efficient multiscale method for time-domain waveform tomography

Abstract

This efficient multiscale method for time-domain waveform tomography incorporates filters that are more efficient than Hamming-window filters. A strategy for choosing optimal frequency bands is proposed to achieve computational efficiency in the time domain. A staggered-grid, explicit finite-difference method with fourth-order accuracy in space and second-order accuracy in time is used for forward modeling and the adjoint calculation. The adjoint method is utilized in inverting for an efficient computation of the gradient directions. In the multiscale approach, multifrequency data and multiple grid sizes are used to overcome somewhat the severe local minima problem of waveform tomography. The method is applied successfully to 1D and 2D heterogeneous models; it can accurately recover low-and high-wavenumber components of the velocity models. The inversion result for the 2D model demonstrates that the multiscale method is computationally efficient and converges faster than a conventional, single-scale method.

3D Multisource Full‐Waveform Inversion using Dynamic Random Phase Encoding

“…The gradient of the misfit function is computed by the zero-lag correlation between the forward-propagated wavefields and backprojected wavefield residuals (Tarantola, 1984;Luo and Schuster, 1991;Zhou et al, 1995Zhou et al, , 1997Sheng et al, 2006;Boonyasiriwat et al, 2009a):…”

Efficient full waveform inversion using the excitation representation of the source wavefield

“…The most popular one is the multiscale strategy (Bunks et al 1995;Sirgue & Pratt 2004;Boonyasiriwat et al 2009) in which we gradually append the high-frequency details of the data to the inversion result of low frequency. Alternatively, Biondi & Almomin (2014) successfully inverts the full bandwidth of the data.…”

Skeletonized wave-equation inversion in vertical symmetry axis media without too much math

“…There are 100 shots with a shot interval of 37.5 m, and each shot is recorded by a 6 km long cable with 480 receivers spaced at a 12.5 m receiver interval. The nearest receiver offset from the source is 200 m and the data are processed by a 25-Hz Wiener filter (Boonyasiriwat et al, 2009). Here the source wavelet is extracted from the raw data by stacking the time-shifted reflection events together from 200 to 250 m offset in the shot gather.…”

“…To avoid local minima in the misfit function, the data are lowpass filtered and then higher frequencies are gradually added to the traces to get more detail in the velocity model (Bunks et al, 1995;Operto et al, 2004). Various strategies are used for increasing the frequency content (Boonyasiriwat et al, 2009). Also, divingwave traveltimes are first inverted, then reflection traveltimes are inverted to get velocity estimates of deeper sediments.…”