滑坡灾变判识技术研究

Study on Catastrophe Identification Technology for Landslides

摘要: 本文是一篇关于滑坡灾变判识技术的综述性研究论文。文章系统性地梳理了滑坡灾害从机理到监测、评价、预测及预警的完整技术链条。首先，论文阐述了滑坡灾害的分类体系、灾变机理、诱发因素以及基于蠕变理论的演化阶段。其次，详细介绍了滑坡稳定性评价的三大方法体系：定性与半定量法、极限平衡法和数值模拟法。接着，论文重点论述了“空–天–地–深”一体化监测体系和多源异构数据的处理与特征提取技术。在此基础上，文章探讨了物理驱动、数据驱动(机器学习与深度学习)及混合预警模型在滑坡灾变预测中的应用。论文进一步归纳了当前研究面临的五大核心挑战：多物理场耦合模拟的精度与效率矛盾、普适性临滑判据的缺失、多源数据实时融合的瓶颈、隐蔽滑坡广域识别泛化能力不足以及极端工况下预测模型的失效风险。最后，文章展望了未来的发展方向，包括全维度多场耦合机理研究、物理约束的AI模型(PINN)、广域隐蔽滑坡的轻量化迁移识别以及基于边缘–云协同的数字孪生预警平台。

Abstract: This paper presents a comprehensive review on identification and discrimination technologies for landslide catastrophic failure. It systematically sorts out the complete technical chain covering landslide disasters from underlying mechanisms to monitoring, stability assessment, prediction and early warning. First, the paper elaborates on the landslide classification system, catastrophic failure mechanisms, triggering factors, as well as evolutionary stages derived from creep theory. Second, it elaborates on three major methodological frameworks for landslide stability evaluation: qualitative and semi-quantitative approaches, limit equilibrium methods, and numerical simulation techniques. Subsequently, this review focuses on the integrated “space-air-ground-subsurface” monitoring framework, together with processing and feature extraction techniques for multi-source heterogeneous data. On this basis, the paper discusses the applications of physically driven models, data-driven models (machine learning and deep learning), and hybrid early-warning models in the prediction of landslide catastrophic failure. Furthermore, five core challenges confronting current research are summarized: the trade-off between accuracy and computational efficiency in multi-physics coupling simulation, the absence of universal critical sliding discrimination criteria, bottlenecks restricting real-time fusion of multi-source data, insufficient generalization capacity for wide-area identification of concealed landslides, and the risk of prediction model failure under extreme working conditions. Finally, future research prospects are proposed, including full-dimensional multi-field coupling mechanism investigations, artificial intelligence models constrained by physical laws (physics-Informed Neural Networks, PINNs), lightweight transfer learning for wide-area concealed landslide identification, and digital twin early-warning platforms built upon edge-cloud collaborative computing architecture.