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<p>Landslide main sliding direction is a core parameter for slope stability analysis and disaster prevention in open-pit mining, a topic vital to both mining engineering and geological science. Existing methods for determining main sliding direction mostly focus on theoretical innovations (e.g., mathematical models, numerical simulations) or laboratory tests. However, open-pit mines have rock stress states and sliding mechanisms distinct from natural landslides, and these methods lack sufficient verification in actual mining scenarios or integration with on-site monitoring practices—leading to inaccuracies when applied to open-pit mine landslides. Thus, there is a critical need for a method tailored to open-pit mines that directly leverages on-site monitoring data. Here we show a new method to determine the main sliding direction of open-pit mine landslides using ground-based Interferometric Synthetic Aperture Radar (InSAR) monitoring images. The method introduces the Image Cumulative Difference Degree-Time (CDD-T) concept to quantify spatiotemporal differences in sub-regional images of deformation areas. It combines OpenCV technology, image similarity algorithms (screened via simulated annealing), and uniform image segmentation. We then extract center coordinates of sub-regions with the maximum cumulative difference degree in each row/column for linear fitting, and select the fitted straight line with the highest proportion as the main sliding direction. Unlike traditional methods, this approach avoids over-reliance on theoretical assumptions and directly integrates with on-site radar monitoring data. Applied to the southern slope of an open-pit mine in Inner Mongolia, the method identifies the main sliding direction as due north—supported by a fitted straight line (F4 = 120) accounting for 46.15% of all fitting results. Verification via 83.3% of GNSS monitoring points and the displacement vector cloud map of a true 3D geological model confirms consistency with actual displacement directions. This data-driven methodology addresses the gap in engineering applicability of traditional theoretical methods, providing practical technical support for slope stability analysis and landslide disaster prevention in open-pit mining.</p>

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Operational determination of landslide sliding direction using ground radar images: a case study in open-pit mining

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Li Yin,
  • Dong Wang,
  • Yiming Wu,
  • Xiaoyu Xing,
  • Shougong Wang

摘要

Landslide main sliding direction is a core parameter for slope stability analysis and disaster prevention in open-pit mining, a topic vital to both mining engineering and geological science. Existing methods for determining main sliding direction mostly focus on theoretical innovations (e.g., mathematical models, numerical simulations) or laboratory tests. However, open-pit mines have rock stress states and sliding mechanisms distinct from natural landslides, and these methods lack sufficient verification in actual mining scenarios or integration with on-site monitoring practices—leading to inaccuracies when applied to open-pit mine landslides. Thus, there is a critical need for a method tailored to open-pit mines that directly leverages on-site monitoring data. Here we show a new method to determine the main sliding direction of open-pit mine landslides using ground-based Interferometric Synthetic Aperture Radar (InSAR) monitoring images. The method introduces the Image Cumulative Difference Degree-Time (CDD-T) concept to quantify spatiotemporal differences in sub-regional images of deformation areas. It combines OpenCV technology, image similarity algorithms (screened via simulated annealing), and uniform image segmentation. We then extract center coordinates of sub-regions with the maximum cumulative difference degree in each row/column for linear fitting, and select the fitted straight line with the highest proportion as the main sliding direction. Unlike traditional methods, this approach avoids over-reliance on theoretical assumptions and directly integrates with on-site radar monitoring data. Applied to the southern slope of an open-pit mine in Inner Mongolia, the method identifies the main sliding direction as due north—supported by a fitted straight line (F4 = 120) accounting for 46.15% of all fitting results. Verification via 83.3% of GNSS monitoring points and the displacement vector cloud map of a true 3D geological model confirms consistency with actual displacement directions. This data-driven methodology addresses the gap in engineering applicability of traditional theoretical methods, providing practical technical support for slope stability analysis and landslide disaster prevention in open-pit mining.