Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation

TL;DR

This paper introduces a deep learning seismic inversion method that models both spatial and temporal information in seismic images, significantly improving reservoir property estimation accuracy.

Contribution

It presents a novel spatiotemporal deep learning workflow for seismic inversion, outperforming traditional and purely temporal models in accuracy.

Findings

Achieved an average r² coefficient of 79.77% on SEAM dataset.

Outperformed other sequence modeling neural networks in seismic inversion.

Utilized spatial structure information for more efficient rock property estimation.

Abstract

Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially. This utilizes information-relatedness in seismic traces in depth and spatial directions to make efficient rock property estimations. We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally. Our results on the SEAM dataset demonstrate that, compared to the other architectures used in the study, the proposed workflow is able to achieve the best performance, with an average $r^{2}$ coefficient of 79.77\%.