# Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network

arXiv: https://arxiv.org/abs/1906.02684 · HTML: https://mlsanity.com/paper/1906.02684/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

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

- [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)
- [Explainable Machine Learning for Hydrocarbon Prospect Risking](https://mlsanity.com/paper/2212.07563/) (2022)
- [Seismic Acoustic Impedance Inversion Framework Based on Conditional Latent Generative Diffusion Model](https://mlsanity.com/paper/2506.13529/) (2025)
- [Latent Variable-Mediated Cross-Learning for Few-Shot Acoustic Impedance Imaging](https://mlsanity.com/paper/2607.20989/) (2026)

## Abstract (arXiv)

In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data. Knowledge of physical properties can prove helpful in identifying key structures in the subsurface for hydrocarbon exploration. In this work, we propose a workflow for predicting acoustic impedance (AI) from seismic data using a network architecture based on Temporal Convolutional Network by posing the problem as that of sequence modeling. The proposed workflow overcomes some of the problems that other network architectures usually face, like gradient vanishing in Recurrent Neural Networks, or overfitting in Convolutional Neural Networks. The proposed workflow was used to predict AI on Marmousi 2 dataset with an average r2 coefficient of 91% on a hold-out validation set.