Elastic full-waveform inversion: Enhance imaging for legacy and modern acquisition

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.