GPU-accelerated Bayesian inference for block-cave mine monitoring via muon tomography

2026-03-30

AI Summary

This study addresses the high-dimensional geometric inversion challenge in muon tomography for caving mine monitoring by proposing an efficient Bayesian inference framework. The method introduces a low-dimensional geometric parameterization to represent stope morphology, constructs a physics-driven forward model to formulate the likelihood function, and incorporates a prior distribution informed by realistic caving characteristics, thereby substantially reducing problem dimensionality and computational complexity. An efficient GPU-accelerated Markov chain Monte Carlo (MCMC) algorithm is employed to sample the posterior distribution, enabling probabilistic reconstruction of stope geometry. Simulated experiments demonstrate that the approach successfully generates diverse yet physically plausible stope morphologies, validating its accuracy, robustness, and computational efficiency.

Abstract

We describe a Bayesian framework for an inverse problem arising from monitoring block caving operations via muon tomography. We work with a low dimensional surface-based representation of the geometry of the block cave, which dramatically reduces the computational requirements of the model while allowing realistic geometries. Adopting a Bayesian approach, we define a prior distribution on the space of geometries that favors realistic cave shapes. Pairing this prior with a likelihood based on the muon tomography forward model, we obtain a posterior distribution over cave geometries using Bayes rule. We obtain approximate samples from this posterior distribution using Markov chain Monte Carlo algorithms running on GPUs, resulting in fast and accurate sampling. We test the fidelity of our methodology by applying it to a simulated block caving scenario for which the ground truth is known. Results show that our method produces a diverse array of sensible geometries that are simultaneously compatible with the data.

Problem

• muon tomography
• block caving
• Bayesian inference
• inverse problem
• cave geometry

Innovation

• Bayesian inference
• muon tomography
• GPU acceleration
• Markov chain Monte Carlo
• block caving monitoring