An overview of full-waveform inversion in exploration geophysics

摘要

Full-waveform inversion FWI is a challenging data-fittingprocedure based on full-wavefield modeling to extract quantitativeinformation from seismograms. High-resolution imaging athalf the propagated wavelength is expected. Recent advances inhigh-performance computing and multifold/multicomponentwide-aperture and wide-azimuth acquisitions make 3D acousticFWI feasible today. Key ingredients of FWI are an efficient forward-modeling engine and a local differential approach, inwhich the gradient and the Hessian operators are efficiently estimated.Local optimization does not, however, prevent convergenceof the misfit function toward local minima because of thelimited accuracy of the starting model, the lack of low frequencies,the presence of noise, and the approximate modeling of thewave-physics complexity. Different hierarchical multiscalestrategies are designed to mitigate the nonlinearity and ill-posednessof FWI by incorporating progressively shorter wavelengthsin the parameter space. Synthetic and real-data case studies addressreconstructing various parameters, from VP and VS velocitiesto density, anisotropy, and attenuation. This review attemptsto illuminate the state of the art of FWI. Crucial jumps, however,remain necessary to make it as popular as migration techniques.The challenges can be categorized as 1 building accurate startingmodels with automatic procedures and/or recording low frequencies,2 defining new minimization criteria to mitigate thesensitivity of FWI to amplitude errors and increasing the robustnessof FWI when multiple parameter classes are estimated, and3 improving computational efficiency by data-compressiontechniques to make 3D elastic FWI feasible.