# Stochastic reconstruction of an oolitic limestone by generative adversarial networks

arXiv: https://arxiv.org/abs/1712.02854 · HTML: https://mlsanity.com/paper/1712.02854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2017.

## Built on by (most cited first, 5 of 5)

- [Computationally Efficient Multiscale Neural Networks Applied To Fluid Flow In Complex 3D Porous Media](https://mlsanity.com/paper/2102.07625/) (2021)
- [Constrained Transformer-Based Porous Media Generation to Spatial Distribution of Rock Properties](https://mlsanity.com/paper/2410.21462/) (2024)
- [Conditional Deep Generative Models for Belief State Planning](https://mlsanity.com/paper/2505.11698/) (2025)
- [Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs](https://mlsanity.com/paper/2508.00833/) (2025)
- [Well Log-Guided Synthesis of Subsurface Images from Sparse Petrography Data Using cGANs](https://mlsanity.com/paper/2603.09651/) (2026)

## Abstract (arXiv)

Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stochastic image reconstruction based on generative adversarial neural networks (GANs). GANs represent a framework of unsupervised learning methods that require no a priori inference of the probability distribution associated with the training data. Using a fully convolutional neural network allows fast sampling of large volumetric images.We apply a GAN based workflow of network training and image generation to an oolitic Ketton limestone micro-CT dataset. Minkowski functionals, effective permeability as well as velocity distributions of simulated flow within the acquired images are compared with the synthetic reconstructions generated by the deep neural network. While our results show that GANs allow a fast and accurate reconstruction of the evaluated image dataset, we address a number of open questions and challenges involved in the evaluation of generative network-based methods.