IntraSeismic: a coordinate-based learning approach to seismic inversion
This addresses seismic imaging challenges for energy, construction, and geotechnical sectors, offering a novel hybrid approach with incremental improvements over existing methods.
The paper tackles the ill-posed inverse problem of extracting quantitative acoustic impedance models from band-limited and noisy seismic data by introducing IntraSeismic, a hybrid method combining coordinate-based learning with physics-based modeling, achieving unparalleled performance in 2D and 3D post-stack seismic inversion.
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.