# Semi-supervised Sequence Modeling for Elastic Impedance Inversion

arXiv: https://arxiv.org/abs/1908.07849 · HTML: https://mlsanity.com/paper/1908.07849/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation](https://mlsanity.com/paper/2302.14644/) (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)
- [Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection](https://mlsanity.com/paper/2108.09605/) (2021)
- [Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning](https://mlsanity.com/paper/2109.11452/) (2021)
- [Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training](https://mlsanity.com/paper/2111.10596/) (2021)
- [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)
- [Seismic full-waveform inversion based on a physics-driven generative adversarial network](https://mlsanity.com/paper/2603.14879/) (2026)
- [Latent Variable-Mediated Cross-Learning for Few-Shot Acoustic Impedance Imaging](https://mlsanity.com/paper/2607.20989/) (2026)

## Abstract (arXiv)

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.