# Semi-supervised Learning for Acoustic Impedance Inversion

## TL;DR

This paper introduces a semi-supervised deep learning framework for acoustic impedance inversion in seismic data, effectively reducing the need for labeled data while maintaining high accuracy.

## Contribution

It presents a novel semi-supervised neural network approach combining convolutional and recurrent layers for seismic inversion, leveraging well logs and a learned forward model as constraints.

## Key findings

- Achieves 98% correlation between estimated and true impedance.
- Uses only 20 labeled traces for training with high accuracy.
- Incorporates geophysical constraints via a learned seismic forward model.

## Abstract

Recent applications of deep learning in the seismic domain have shown great potential in different areas such as inversion and interpretation. Deep learning algorithms, in general, require tremendous amounts of labeled data to train properly. To overcome this issue, we propose a semi-supervised framework for acoustic impedance inversion based on convolutional and recurrent neural networks. Specifically, seismic traces and acoustic impedance traces are modeled as time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for acoustic impedance. The proposed workflow uses well log data to guide the inversion. In addition, it utilizes a learned seismic forward model 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 elastic impedance using 20 AI traces for training.