Semi-supervised Sequence Modeling for Elastic Impedance Inversion

TL;DR

This paper introduces a semi-supervised neural network framework that models seismic and elastic impedance data as time series, incorporating geophysical constraints to improve inversion accuracy in seismic interpretation.

Contribution

It develops a novel semi-supervised sequence modeling approach using recurrent neural networks that enforces geophysical constraints during elastic impedance inversion.

Findings

Achieves 98% correlation between estimated and true EI on synthetic data.

Effectively incorporates well-log data and seismic forward modeling as constraints.

Demonstrates high accuracy in seismic inversion with limited training data.

Abstract

Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints - the lack of which might lead to undesirable results. To overcome this issue, we have developed a semi-supervised sequence modeling framework based on recurrent neural networks for elastic impedance inversion from multi-angle seismic data. Specifically, seismic traces and elastic impedance (EI) traces are modeled as a time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for EI. The proposed workflow uses well-log data to guide the inversion. In addition, it uses seismic forward modeling to regularize the training and to serve as a geophysical constraint for the inversion. The proposed workflow achieves an average correlation of 98% between the estimated and target EI using 10 well logs for training on a synthetic data set.