IntraSeismic: a coordinate-based learning approach to seismic inversion
Juan Romero Wolfgang Heidrich Nick Luiken Matteo Ravasi
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.