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

## TL;DR

This paper introduces a workflow using Temporal Convolutional Networks to predict acoustic impedance from seismic data, addressing common neural network issues and achieving high accuracy on a standard dataset.

## Contribution

The work presents a novel application of Temporal Convolutional Networks for seismic inversion, improving prediction accuracy and overcoming limitations of traditional neural network architectures.

## Key findings

- Achieved 91% r^2 coefficient on Marmousi 2 dataset.
- Overcame gradient vanishing and overfitting issues.
- Demonstrated effective sequence modeling for seismic inversion.

## Abstract

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 $r^{2}$ coefficient of $91\%$ on a hold-out validation set.