Creating a big data source of landslide deformation stages: New thoughts on identifying displacement warning thresholds

Abstract

The determination of displacement warning thresholds is a difficult and crucial task for landslide early warning, as it requires a deep understanding of displacement monitoring curve patterns and significantly impacts the effectiveness of natural hazard mitigation efforts. In recent years, the emergence of advanced technologies such as the global navigation satellite system (GNSS) and interferometric synthetic aperture radar has enabled large-scale monitoring of landslide surface displacement, while also introducing new challenges in establishing displacement warning thresholds. While displacement monitoring curves exhibit various patterns in large-region monitoring environments, the conventional Saito method assumes a landslide deformation trend of three creep stages and cannot achieve ideal early warning results. Consequently, this study is conducted based on an analysis of a GNSS surface displacement curve database built by the China Institute of Geological Environmental Monitoring. An identification method for identifying landslide deformation stages is developed based on the idea of morphology, leading to the creation of a big data source of landslide deformation stages. The spatial distribution of these deformation stages within the data source serves as the foundation for developing a technique for identifying deformation thresholds and a corresponding multilevel early warning method. The early warning performance is validated using long-duration displacement monitoring curves, and the spatiotemporal distribution characteristics of the deformation stages are discussed. This study can provides valuable insights for displacement monitoring, early warning, and hazard prevention of large-scale landslides.