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.

翻译：局部本征维度（Local Intrinsic Dimensionality, LID）在识别高维数据中的异常值和离群点方面展现出巨大潜力，已广泛应用于包括颗粒介质滑坡失稳检测在内的诸多实际场景。在滑坡易发区，对失稳区域进行早期精准识别对于有效减轻地质灾害至关重要。现有方法通常依赖于通过统计或机器学习技术分析地表位移数据，但往往难以同时捕捉此类数据固有的空间相关性与时间动态特性。为弥补这一不足，本研究聚焦于地面监测滑坡，提出一种新颖方法，能够联合融入空间与时间信息，从而实现对复杂滑坡的检测，包括同一边坡不同区域发生的多次连续失稳。具体而言，我们的方法基于一种现有的LID技术——sLID，并从以下三个关键方面扩展其功能：（1）运动学增强：将速度纳入sLID计算，以更好地捕捉短期时间依赖性与变形速率关系；（2）空间融合：应用贝叶斯估计聚合空间邻域内的sLID值，从而将空间相关性有效嵌入LID评分中；（3）时间建模：引入一种时间变体tLID，可从时间序列数据中学习长期动态特性，为位移行为提供鲁棒的时间表征。最终，我们将这两个组件集成到一个统一框架中，称为时空LID（stLID），用以识别在单一维度或双维度上呈现异常的样本。大量实验表明，stLID在失稳检测精度与预警提前期方面均持续优于现有方法。