# Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation

arXiv: https://arxiv.org/abs/2006.15472 · HTML: https://mlsanity.com/paper/2006.15472/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior](https://mlsanity.com/paper/2505.08585/) (2025)
- [Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training](https://mlsanity.com/paper/2111.10596/) (2021)
- [Explainable Machine Learning for Hydrocarbon Prospect Risking](https://mlsanity.com/paper/2212.07563/) (2022)

## Abstract (arXiv)

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 r2 coefficient of 79.77%.