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Global maximum horizontal principal stress orientation: A high-precision machine learning framework based on multi-source heterogeneous data fusion.

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Title: Global maximum horizontal principal stress orientation: A high-precision machine learning framework based on multi-source heterogeneous data fusion.
Authors: Song, Zebin1,2 (AUTHOR), Jiang, Quan1 (AUTHOR) qjiang@whrsm.ac.cn, Zhang, Shishu3 (AUTHOR), Xia, Yong3 (AUTHOR), Li, Long1 (AUTHOR), Liu, Jian1 (AUTHOR)
Source: Engineering Geology. Sep2025, Vol. 356, pN.PAG-N.PAG. 1p.
Subjects: Machine learning, Multisensor data fusion, Earth sciences, Geotechnical engineering, Transformer models, Stress concentration, Structural geology
Abstract: Understanding the continuous spatial distribution of in-situ stress orientations is essential for safe and efficient underground engineering; however, traditional measurement methods are constrained by prohibitive costs and time requirements. We present a novel AI-driven framework that fuses neighborhood stress orientation patterns with topographic features through Vision Transformer (ViT) architecture and multi-layer attention mechanisms. This approach enables the first continuous, high-precision global mapping of maximum horizontal principal stress (S Hmax) orientations. Trained and validated on 32,464 quality-controlled records, the model extracts latent spatial stress orientation patterns across diverse tectonic settings, achieving 77.4 % prediction accuracy. Validation across four tectonically distinct regions confirms the framework's robustness, including its successful application to the Sichuan-Tibet Railway corridor where prediction errors reached as low as 1.3°. This approach overcomes the spatial continuity and cost limitations inherent in traditional stress orientations characterization, revealing significant application prospects from infrastructure planning to fault activity prediction. Together, these results demonstrate that integrating heterogeneous geoscientific data within an artificial intelligence framework enables high-precision prediction of stress orientations, offering novel insights into the evolution of global tectonic stress fields. • Integrates multi-source data using Vision Transformer and attention mechanisms. • Achieves high-precision mapping of global S Hmax orientations for the first time. • Validated in four tectonic settings and along the Sichuan–Tibet Railway corridor. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Understanding the continuous spatial distribution of in-situ stress orientations is essential for safe and efficient underground engineering; however, traditional measurement methods are constrained by prohibitive costs and time requirements. We present a novel AI-driven framework that fuses neighborhood stress orientation patterns with topographic features through Vision Transformer (ViT) architecture and multi-layer attention mechanisms. This approach enables the first continuous, high-precision global mapping of maximum horizontal principal stress (S Hmax) orientations. Trained and validated on 32,464 quality-controlled records, the model extracts latent spatial stress orientation patterns across diverse tectonic settings, achieving 77.4 % prediction accuracy. Validation across four tectonically distinct regions confirms the framework's robustness, including its successful application to the Sichuan-Tibet Railway corridor where prediction errors reached as low as 1.3°. This approach overcomes the spatial continuity and cost limitations inherent in traditional stress orientations characterization, revealing significant application prospects from infrastructure planning to fault activity prediction. Together, these results demonstrate that integrating heterogeneous geoscientific data within an artificial intelligence framework enables high-precision prediction of stress orientations, offering novel insights into the evolution of global tectonic stress fields. • Integrates multi-source data using Vision Transformer and attention mechanisms. • Achieves high-precision mapping of global S Hmax orientations for the first time. • Validated in four tectonic settings and along the Sichuan–Tibet Railway corridor. [ABSTRACT FROM AUTHOR]
ISSN:00137952
DOI:10.1016/j.enggeo.2025.108276