First Published: The Leading Edge, May 2025, by Faqi Liu, Cosmin Macesanu, Hao Xing, Matvey Romanenko, Ge Zhan, Carlos Calderón-Macías and Bin Wang, TGS
Abstract
Full-waveform inversion (FWI) has become the key algorithm in seismic processing workflows to derive high-resolution velocity models. Benefiting from accurate wavefield propagation in geologically complex areas, elastic FWI is able to produce superior velocity models with greatly improved resolution when compared to acoustic algorithms. In this paper, we apply FWI (both acoustic and elastic) to data acquired using different survey geometries including sparse-node data, legacy narrow-azimuth data, and distributed acoustic sensing-vertical seismic profile survey data to demonstrate the advantages. The increased physics in elastic FWI allows for significantly improved focusing of velocity interfaces with strong contrast, resulting in improved imaging of underneath structures.
Introduction
Full-waveform inversion (FWI) has been developed as the core of a seismic processing workflow to derive accurate velocity models with high resolution, from which a synthetic data set can
best match the recorded one (Routh et al., 2017). FWI algorithms are largely differentiated with the different definition of “best match” represented in the objective functions. The derived models from FWI are typically used for depth migration. On the other hand, high-frequency FWI models have also been used to directly derive a reflectivity model commonly referred to as FWI image (Wang et al., 2021a) or FWI-derived reflectivity (Kumar and Ali, 2024). This high-resolution reflectivity is theoretically equivalent to an image from a nonlinear version of least-squares reverse time migration (RTM) (Wang et al., 2021a).
Zoomed part of the velocity model and the corresponding FWI images after acoustic DM FWI in (a) and (c), and elastic DM FWI in (b) and (d), respectively. A depth slice in the FWI images is shown for (e) AFWI and (f) EFWI velocities.
Due to the elastic nature of the earth, elastic FWI (EFWI) has intrinsic advantages over the acoustic version, especially in geologically complex areas like those with massive salt bodies
(Raknes et al., 2015; Wang et al., 2021b; Liu et al., 2024). The elastic wave equations can simulate both compressional (P waves) and shear waves (S waves), capturing more detailed information
about the subsurface. Even though EFWI has mainly been focusing on inverting VP here, incorporating elastic effects in the inversion flow can result in more accurate P-wave propagation.
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