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

• URL: http://arxiv.org/abs/2601.03569v1
• Date: Wed, 07 Jan 2026 04:29:05 GMT
• Title: Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Complex Catastrophic Slope Failure
• Authors: Yuansan Liu, Antoinette Tordesillas, James Bailey,
• Abstract summary: Local Intrinsic Dimensionality (LID) has shown strong potential for identifying anomalies and outliers in high-dimensional data.<n>We focus on ground-monitored landslides and introduce a novel approach that jointly incorporates spatial and temporal information.
• Score: 5.622625734292367
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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