# Parametrization and generation of geological models with generative adversarial networks

arXiv: https://arxiv.org/abs/1708.01810 · HTML: https://mlsanity.com/paper/1708.01810/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2017.

## Built on by (most cited first, 9 of 9)

- [When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning](https://mlsanity.com/paper/2203.16797/) (2022)
- [Applications of physics-informed scientific machine learning in subsurface science: A survey](https://mlsanity.com/paper/2104.04764/) (2021)
- [Generating unrepresented proportions of geological facies using Generative Adversarial Networks](https://mlsanity.com/paper/2203.09639/) (2022)
- [A Generative-AI Modeling Framework for Explainable Decision Support in Complex Geosteering Scenarios](https://mlsanity.com/paper/2503.08509/) (2025)
- [Seismic Inverse Modeling Method based on Generative Adversarial Network](https://mlsanity.com/paper/2106.04197/) (2021)
- [Generation of non-stationary stochastic fields using Generative Adversarial Networks](https://mlsanity.com/paper/2205.05469/) (2022)
- [Stable Rivers: A Case Study in the Application of Text-to-Image Generative Models for Earth Sciences](https://mlsanity.com/paper/2312.07833/) (2023)
- [History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions](https://mlsanity.com/paper/2507.10201/) (2025)
- [Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference](https://mlsanity.com/paper/2603.14907/) (2026)

## Abstract (arXiv)

One of the main challenges in the parametrization of geological models is the ability to capture complex geological structures often observed in the subsurface. In recent years, generative adversarial networks (GAN) were proposed as an efficient method for the generation and parametrization of complex data, showing state-of-the-art performances in challenging computer vision tasks such as reproducing natural images (handwritten digits, human faces, etc.). In this work, we study the application of Wasserstein GAN for the parametrization of geological models. The effectiveness of the method is assessed for uncertainty propagation tasks using several test cases involving different permeability patterns and subsurface flow problems. Results show that GANs are able to generate samples that preserve the multipoint statistical features of the geological models both visually and quantitatively. The generated samples reproduce both the geological structures and the flow statistics of the reference geology.