# Semi-supervised Learning for Acoustic Impedance Inversion

arXiv: https://arxiv.org/abs/1905.13412 · HTML: https://mlsanity.com/paper/1905.13412/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 8 of 8)

- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [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)
- [DuRIN: A Deep-unfolded Sparse Seismic Reflectivity Inversion Network](https://mlsanity.com/paper/2104.04704/) (2021)
- [Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training](https://mlsanity.com/paper/2111.10596/) (2021)
- [NuSPAN: A Proximal Average Network for Nonuniform Sparse Model -- Application to Seismic Reflectivity Inversion](https://mlsanity.com/paper/2105.00003/) (2021)
- [Learning velocity model for complex media with deep convolutional neural networks](https://mlsanity.com/paper/2110.08626/) (2021)
- [Deep Preconditioners and their application to seismic wavefield processing](https://mlsanity.com/paper/2207.09938/) (2022)
- [Improved impedance inversion by the iterated graph Laplacian](https://mlsanity.com/paper/2404.16324/) (2024)

## Abstract (arXiv)

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.