# Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1802.03065 · HTML: https://mlsanity.com/paper/1802.03065/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2018.

## Built on by (most cited first, 12 of 12)

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- [Latent Diffusion Model for Conditional Reservoir Facies Generation](https://mlsanity.com/paper/2311.01968/) (2023)
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- [Generating unrepresented proportions of geological facies using Generative Adversarial Networks](https://mlsanity.com/paper/2203.09639/) (2022)
- [A stable deep adversarial learning approach for geological facies generation](https://mlsanity.com/paper/2305.13318/) (2023)
- [Diffusion models for multivariate subsurface generation and efficient probabilistic inversion](https://mlsanity.com/paper/2507.15809/) (2025)
- [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)
- [Deep autoregressive modeling for land use land cover](https://mlsanity.com/paper/2401.01395/) (2024)
- [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)
- [Leveraging generative adversarial networks with spatially adaptive denormalization for multivariate stochastic seismic data inversion](https://mlsanity.com/paper/2512.02863/) (2025)
- [Robustness and Transferability of Pix2Geomodel for Bidirectional Facies Property Translation in a Complex Reservoir](https://mlsanity.com/paper/2605.03919/) (2026)

## Abstract (arXiv)

An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations. Typically, this is done using spatial interpolation methods or by reproducing patterns from a reference image. However, these algorithms fail to produce realistic patterns and do not exhibit the wide range of uncertainty inherent in the prediction of geology. In this paper, we show how semantic inpainting with Generative Adversarial Networks can be used to generate varied realizations of geology which honor physical measurements while matching the expected geological patterns. In contrast to other algorithms, our method scales well with the number of data points and mimics a distribution of patterns as opposed to a single pattern or image. The generated conditional samples are state of the art.