Statistically Accurate and Robust Generative Prediction of Rock Discontinuities with A Tabular Foundation Model

TL;DR

This paper introduces a robust tabular foundation model for generative prediction of rock discontinuities, effectively capturing complex distributions from sparse data to improve geotechnical safety and design.

Contribution

It presents a novel application of a tabular foundation model for accurate, robust prediction of rock discontinuities from limited measurements, outperforming existing methods.

Findings

Superior accuracy over traditional models

Enhanced robustness in data-sparse scenarios

Effective modeling of complex distribution patterns

Abstract

Rock discontinuities critically govern the mechanical behavior and stability of rock masses. Their internal distributions remain largely unobservable and are typically inferred from surface-exposed discontinuities using generative prediction approaches. However, surface-exposed observations are inherently sparse, and existing generative prediction approaches either fail to capture the underlying complex distribution patterns or lack robustness under data-sparse conditions. Here, we proposed a simple yet robust approach for statistically accurate generative prediction of rock discontinuities by utilizing a tabular foundation model. By leveraging the powerful sample learning capability of the foundation model specifically designed for small data, our approach can effectively capture the underlying complex distribution patterns within limited measured discontinuities. Comparative experiments on ten datasets with diverse scales and distribution patterns of discontinuities demonstrate superior accuracy and robustness over conventional statistical models and deep generative approaches. This work advances quantitative characterization of rock mass structures, supporting safer and more reliable data-driven geotechnical design.