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Subspace Methods in Multi-Parameter Seismic Full Waveform Inversion

Communications in Computational Physics 2020年第6期28卷 228-248页

作者： Yu Geng kristopher a.innanen Wenyong Pan Department of Geosciences CREWES ProjectUniversity of CalgaryAlberta T2N 1N4Canada Department of Geoscience CREWES ProjectUniversity of CalgaryAB T2N 1N4Canada Los Alamos National Laboratory Geophysics GroupMS D452Los AlamosNM 87545USA

In full waveform inversion(FWI)high-resolution subsurface model param-eters are *** is normally treated as a nonlinear least-squares inverse problem,in which the minimum of the corresponding misfit function is found by updating the model *** multiple elastic or acoustic properties are solved for,simple gradient methods tend to confuse parameter *** is referred to as parameter cross-talk;it leads to incorrect model solutions,poor convergence and strong dependence on the scaling of the different parameter *** step lengths in a subspace domain,rather than directly in terms of gradients of different parameters,is a potentially valuable approach to address this *** particular subspace used can be defined over a span of different sets of data or different parameter classes,provided it involves a small number of vectors compared to those contained in the whole model *** a subspace method,the basis vectors are defined first,and a local min-imum is found in the space spanned by *** examine the application of the sub-space method within acoustic FWI in determining simultaneously updates for velocity and *** first discuss the choice of basis vectors to construct the spanned space,from linear updates by distinguishing only the contributions of different parameter classes towards nonlinear updates by adding the contributions of higher-order pertur-bations of each parameter *** numerical character of FWI solutions generated via subspace methods involving different basis vectors is then analyzed and compared with traditional FWI *** subspace methods can provide better reconstructions of the model,especially for the velocity,as well as improved convergence rates,while the computational costs are still comparable with the traditional FWI methods.

关键词： Waveform inversion inverse problem subspace method

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