Synthetic Geology -- Structural Geology Meets Deep Learning

2025-06-11

AI Summary

Real-world 3D subsurface geological data at kilometer-scale depths are scarce, hindering robust geological modeling. Method: This paper proposes a geologic-process-driven generative AI framework: (1) a synthetic data engine integrating sedimentary compaction, volcanic intrusion, and tectonic deformation; and (2) a three-stage modeling paradigm—synthetic pretraining, borehole fine-tuning, and physics-informed inversion regularization—leveraging flow matching, 3D voxel neural networks, and coupled geophysical process modeling. Contribution/Results: The model generates high-fidelity, interpretable voxelized subsurface models—including stratigraphy, faults, folds, and dikes—from surface geological maps alone. Reconstruction accuracy improves significantly with increasing borehole data. By embedding domain knowledge into the generative pipeline, the method delivers a scalable, physically consistent, and explainable 3D geological modeling paradigm for mineral exploration, geohazard assessment, and geotechnical engineering.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Visualizing Earth's subsurface using deep learning techniques
• Generating synthetic data to overcome subsurface data scarcity
• Creating 3D subsurface models from surface and borehole data

Innovation

• Deep learning extends surface data to 3D subsurface
• Synthetic data mimics geological activity for training
• Generative flow matching enhances subsurface imaging fidelity