Synthetic Geology -- Structural Geology Meets Deep Learning

• URL: http://arxiv.org/abs/2506.11164v1
• Date: Wed, 11 Jun 2025 20:42:28 GMT
• Title: Synthetic Geology -- Structural Geology Meets Deep Learning
• Authors: Simon Ghyselincks, Valeriia Okhmak, Stefano Zampini, George Turkiyyah, David Keyes, Eldad Haber,
• Abstract summary: Building on techniques of generative artificial intelligence applied to voxelated images, we demonstrate a method that extends surface geological data to a three-dimensional subsurface region by training a neural network.<n>We close this data gap in the development of subsurface deep learning by designing a synthetic data-generator process that mimics eons of geological activity.<n>A foundation model trained on such synthetic data is able to generate a 3D image of the subsurface from a previously unseen map of surface topography and geology.
• Score: 3.216132991084434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visualizing the first few kilometers of the Earth's subsurface, a long-standing challenge gating a virtually inexhaustible list of important applications, is coming within reach through deep learning. Building on techniques of generative artificial intelligence applied to voxelated images, we demonstrate a method that extends surface geological data supplemented by boreholes to a three-dimensional subsurface region by training a neural network. The Earth's land area having been extensively mapped for geological features, the bottleneck of this or any related technique is the availability of data below the surface. We close this data gap in the development of subsurface deep learning by designing a synthetic data-generator process that mimics eons of geological activity such as sediment compaction, volcanic intrusion, and tectonic dynamics to produce a virtually limitless number of samples of the near lithosphere. A foundation model trained on such synthetic data is able to generate a 3D image of the subsurface from a previously unseen map of surface topography and geology, showing increasing fidelity with increasing access to borehole data, depicting such structures as layers, faults, folds, dikes, and sills. We illustrate the early promise of the combination of a synthetic lithospheric generator with a trained neural network model using generative flow matching. Ultimately, such models will be fine-tuned on data from applicable campaigns, such as mineral prospecting in a given region. Though useful in itself, a regionally fine-tuned models may be employed not as an end but as a means: as an AI-based regularizer in a more traditional inverse problem application, in which the objective function represents the mismatch of additional data with physical models with applications in resource exploration, hazard assessment, and geotechnical engineering.

Related papers

• Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data [77.40388962445168]
  This study explores the feasibility of deriving Mercury's regolith elemental composition from in-situ measurements of its neutral exosphere using deep neural networks (DNNs)<n>We present a supervised feed-forward DNN architecture that predicts the chemical elements of the surface regolith below.<n>It serves as an estimator for the surface-exosphere interaction and the processes leading to exosphere formation.
  arXiv  Detail & Related papers  (2025-05-16T09:52:45Z)
• Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction [84.26340606752763]
  In this paper, we introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework.<n>The network is designed to conform to the general symmetry conservation law via symmetry where conservative and non-conservative information passes over a multiscale space by a latent temporal marching strategy.<n>Results demonstrate that CiGNN exhibits remarkable baseline accuracy and generalizability, and is readily applicable to learning for prediction of varioustemporal dynamics.
  arXiv  Detail & Related papers  (2024-12-30T13:55:59Z)
• Geometry Distributions [51.4061133324376]
  We propose a novel geometric data representation that models geometry as distributions. Our approach uses diffusion models with a novel network architecture to learn surface point distributions. We evaluate our representation qualitatively and quantitatively across various object types, demonstrating its effectiveness in achieving high geometric fidelity.
  arXiv  Detail & Related papers  (2024-11-25T04:06:48Z)
• DreamPolish: Domain Score Distillation With Progressive Geometry Generation [66.94803919328815]
  We introduce DreamPolish, a text-to-3D generation model that excels in producing refined geometry and high-quality textures. In the geometry construction phase, our approach leverages multiple neural representations to enhance the stability of the synthesis process. In the texture generation phase, we introduce a novel score distillation objective, namely domain score distillation (DSD), to guide neural representations toward such a domain.
  arXiv  Detail & Related papers  (2024-11-03T15:15:01Z)
• ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis [14.013976303831313]
  ImplicitTerrain is an implicit neural representation (INR) approach for modeling high-resolution terrain continuously and differentiably. Our experiments demonstrate superior surface fitting accuracy, effective topological feature retrieval, and various topographical feature extraction.
  arXiv  Detail & Related papers  (2024-05-31T23:05:34Z)
• Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data [0.0]
  This research uses deep learning to estimate the topology of sparse, unordered point cloud scenes in 3D. The experimental results of this pilot study support the hypothesis that, with the aid of sophisticated synthetic data generation, neural networks can perform segmentation-based topological data analysis.
  arXiv  Detail & Related papers  (2023-09-29T04:37:35Z)
• Semi-Automated Segmentation of Geoscientific Data Using Superpixels [4.035753155957697]
  Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy. Inspired by the concept of superpixels, we propose a deep-learning based approach to segmentized survey data into regions with similar characteristics.
  arXiv  Detail & Related papers  (2023-03-20T19:21:46Z)
• PetroGAN: A novel GAN-based approach to generate realistic, label-free petrographic datasets [0.0]
  We develop a novel deep learning framework based on generative adversarial networks (GANs) to create the first realistic synthetic petrographic dataset. The training dataset consists of 10070 images of rock thin sections both in plane- and cross-polarized light. The algorithm trained for 264 GPU hours and reached a state-of-the-art Fr'echet Inception Distance (FID) score of 12.49 for petrographic images.
  arXiv  Detail & Related papers  (2022-04-07T01:55:53Z)
• Predictive Geological Mapping with Convolution Neural Network Using Statistical Data Augmentation on a 3D Model [0.0]
  We develop a data augmentation workflow that uses a 3D geological and magnetic susceptibility model as input. A Gated Shape Convolutional Neural Network algorithm was trained on a generated synthetic dataset to perform geological mapping. The validation conducted on a portion of the synthetic dataset and data from adjacent areas shows that the methodology is suitable to segment the surficial geology.
  arXiv  Detail & Related papers  (2021-10-27T13:56:40Z)
• Generating Physically-Consistent Satellite Imagery for Climate Visualizations [53.61991820941501]
  We train a generative adversarial network to create synthetic satellite imagery of future flooding and reforestation events. A pure deep learning-based model can generate flood visualizations but hallucinates floods at locations that were not susceptible to flooding. We publish our code and dataset for segmentation guided image-to-image translation in Earth observation.
  arXiv  Detail & Related papers  (2021-04-10T15:00:15Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.