IntraSeismic: a coordinate-based learning approach to seismic inversion

TL;DR

IntraSeismic introduces a hybrid coordinate-based learning method for seismic inversion that improves accuracy, convergence, and data integration in subsurface imaging, validated on synthetic and real data.

Contribution

The paper presents IntraSeismic, a novel hybrid approach combining coordinate-based learning with seismic physics for enhanced inversion performance.

Findings

Superior inversion accuracy in 2D and 3D cases

Fast convergence rates demonstrated

Effective integration of well data and uncertainty quantification

Abstract

Seismic imaging is the numerical process of creating a volumetric representation of the subsurface geological structures from elastic waves recorded at the surface of the Earth. As such, it is widely utilized in the energy and construction sectors for applications ranging from oil and gas prospection, to geothermal production and carbon capture and storage monitoring, to geotechnical assessment of infrastructures. Extracting quantitative information from seismic recordings, such as an acoustic impedance model, is however a highly ill-posed inverse problem, due to the band-limited and noisy nature of the data. This paper introduces IntraSeismic, a novel hybrid seismic inversion method that seamlessly combines coordinate-based learning with the physics of the post-stack modeling operator. Key features of IntraSeismic are i) unparalleled performance in 2D and 3D post-stack seismic inversion, ii) rapid convergence rates, iii) ability to seamlessly include hard constraints (i.e., well data) and perform uncertainty quantification, and iv) potential data compression and fast randomized access to portions of the inverted model. Synthetic and field data applications of IntraSeismic are presented to validate the effectiveness of the proposed method.