Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Complex Catastrophic Slope Failure

2026-01-07

arXiv.org

AI Summary

Existing landslide monitoring approaches struggle to simultaneously capture spatial correlations and temporal dynamics, limiting early identification of multi-stage slope instabilities. This work proposes a spatiotemporal Local Intrinsic Dimensionality (stLID) method that innovatively incorporates velocity information into LID computation. By integrating Bayesian spatial fusion with time-series modeling, stLID establishes, for the first time, a unified spatiotemporal LID framework capable of characterizing continuous failure processes across multiple regions. Evaluated on several real-world landslide scenarios, stLID substantially outperforms current methods, achieving significant improvements in both early warning lead time and detection accuracy.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Local Intrinsic Dimensionality (LID) has shown strong potential for identifying anomalies and outliers in high-dimensional data across a wide range of real-world applications, including landslide failure detection in granular media. Early and accurate identification of failure zones in landslide-prone areas is crucial for effective geohazard mitigation. While existing approaches typically rely on surface displacement data analyzed through statistical or machine learning techniques, they often fall short in capturing both the spatial correlations and temporal dynamics that are inherent in such data. To address this gap, we focus on ground-monitored landslides and introduce a novel approach that jointly incorporates spatial and temporal information, enabling the detection of complex landslides and including multiple successive failures occurring in distinct areas of the same slope. To be specific, our method builds upon an existing LID-based technique, known as sLID. We extend its capabilities in three key ways. (1) Kinematic enhancement: we incorporate velocity into the sLID computation to better capture short-term temporal dependencies and deformation rate relationships. (2) Spatial fusion: we apply Bayesian estimation to aggregate sLID values across spatial neighborhoods, effectively embedding spatial correlations into the LID scores. (3) Temporal modeling: we introduce a temporal variant, tLID, that learns long-term dynamics from time series data, providing a robust temporal representation of displacement behavior. Finally, we integrate both components into a unified framework, referred to as spatiotemporal LID (stLID), to identify samples that are anomalous in either or both dimensions. Extensive experiments show that stLID consistently outperforms existing methods in failure detection precision and lead-time.

Problem

• Local Intrinsic Dimensionality
• landslide failure detection
• spatiotemporal dynamics
• geohazard mitigation
• anomaly detection

Innovation

• Local Intrinsic Dimensionality
• spatiotemporal modeling
• landslide early detection
• Bayesian spatial fusion
• kinematic enhancement