Large-scale highly-accurate extended full waveform inversion using convergent Born series
Hossein S. Aghamiry, Ali Gholami, Kamal Aghazade, Mahdi Sonbolestan,, and Stephane Operto

TL;DR
This paper introduces a scalable, accurate extended full waveform inversion method using convergent Born series and sketching techniques, enabling efficient 3D seismic imaging with improved computational cost and accuracy.
Contribution
It proposes a novel approach combining convergent Born series and sketching to compute highly accurate data-assimilated wavefields efficiently in extended FWI.
Findings
Reconstructed subsurface models comparable to classical extended FWI.
Achieved efficient 3D seismic imaging with reduced computational cost.
Validated on a salt benchmark dataset.
Abstract
Full-waveform inversion (FWI) is a seismic imaging method that provides quantitative inference about subsurface properties with a wavelength-scale resolution. Its frequency-domain formulation is computationally efficient when processing only a few discrete frequencies. However, classical FWI, which is formulated on the reduced-parameter space, requires starting the inversion with a sufficiently-accurate initial model and low frequency to prevent being stuck in local minima due to cycle skipping. FWI with extended search space has been proposed to mitigate this issue. It contains two main steps: first, data-assimilated (DA) wavefields are computed by allowing for wave-equation errors to match the data at receivers closely. Then, subsurface parameters are estimated from these wavefields by minimizing the wave-equation errors. The DA wavefields are the least-squares solution of an…
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Taxonomy
TopicsSeismic Imaging and Inversion Techniques · Seismic Waves and Analysis · Seismology and Earthquake Studies
