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<p>Characterising rock mass behaviour beyond Earth presents fundamental challenges: limited in-situ measurements, inability to conduct conventional testing, and mission decisions under extreme uncertainty. This paper presents an intelligent machine learning framework for planetary rock mechanics, with particular emphasis on Martian regolith and rock characteristics documented across multiple mission sites. Diverse data sources, including&#xa0;Mars rover measurements, orbital datasets, meteorite analyses, lunar samples, and terrestrial analogues were synthesised, revealing substantial spatial variability and unique mechanical behaviours of these rocks. This review clarifies the role of machine learning in planetary rock mechanics as a practical tool for integrating heterogeneous, incomplete observations (e.g. imagery, spectra, thermal proxies, drilling telemetry, and sparse ground truth) into quantitative, uncertainty-aware predictions that support mission design and operations. The author summarises how feature engineering and representation learning can convert these multi-modal data streams into physically meaningful predictors, and how transfer learning and domain adaptation may enable cautious reuse of knowledge across planetary bodies under a strong domain shift. Uncertainty quantification distinguishes aleatoric (measurement noise) from epistemic (model) uncertainty, enabling confidence-bounded predictions for landing site selection and risk mitigation. Bayesian neural networks, ensemble methods, and Monte Carlo dropout provide decision support frameworks. This integrated approach advances planetary rock mechanics from qualitative interpretation toward quantitative predictions, reducing mission risk and maximizing scientific return from severely constrained exploration opportunities.</p>

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Intelligent Planetary Rock Mechanics: Machine Learning Pathways for Characterising Rock Mass Behaviour Beyond Earth

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Junzhe Liu

摘要

Characterising rock mass behaviour beyond Earth presents fundamental challenges: limited in-situ measurements, inability to conduct conventional testing, and mission decisions under extreme uncertainty. This paper presents an intelligent machine learning framework for planetary rock mechanics, with particular emphasis on Martian regolith and rock characteristics documented across multiple mission sites. Diverse data sources, including Mars rover measurements, orbital datasets, meteorite analyses, lunar samples, and terrestrial analogues were synthesised, revealing substantial spatial variability and unique mechanical behaviours of these rocks. This review clarifies the role of machine learning in planetary rock mechanics as a practical tool for integrating heterogeneous, incomplete observations (e.g. imagery, spectra, thermal proxies, drilling telemetry, and sparse ground truth) into quantitative, uncertainty-aware predictions that support mission design and operations. The author summarises how feature engineering and representation learning can convert these multi-modal data streams into physically meaningful predictors, and how transfer learning and domain adaptation may enable cautious reuse of knowledge across planetary bodies under a strong domain shift. Uncertainty quantification distinguishes aleatoric (measurement noise) from epistemic (model) uncertainty, enabling confidence-bounded predictions for landing site selection and risk mitigation. Bayesian neural networks, ensemble methods, and Monte Carlo dropout provide decision support frameworks. This integrated approach advances planetary rock mechanics from qualitative interpretation toward quantitative predictions, reducing mission risk and maximizing scientific return from severely constrained exploration opportunities.