Data-Assimilation-Driven Geohazard Monitoring and Early Warning Along Railways: A Review and the PAD Framework

Abstract

Conventional early-warning methods for geohazards along railways rely largely on single observations, empirical criteria or static analysis, and struggle to meet the demands of corridor-scale screening and dynamic tracking. To address this gap, a railway-oriented Perception–Assimilation–Decision (PAD) closed-loop early-warning framework is proposed. The evolutionary patterns of typical geohazards along railways, including landslides, rockfalls, debris flows and settlement, are reviewed together with the application scope and limitations of multi-source monitoring techniques. Additionally, differentiated assimilation strategies are clarified, with continuous deformation and hydro-mechanical state updating for plastic failure and damage-sensitive evidence and critical-state identification for brittle failure. On this basis, a mechanism–data dual-driven assimilation paradigm and a two-scale PAD organisation, comprising corridor-scale spatial screening and site-scale state updating, are introduced. Recent applications show that data assimilation has shifted from correcting a single monitoring variable toward the dynamic coupling of multi-source observations with physical models. By establishing the logical chain from multi-source perception to state updating and finally to railway engineering response, the PAD framework transforms the traditional anomaly-identification-based warning mode into closed-loop risk management. The results provide a reference for building geohazard early-warning systems and engineering-oriented response along railways.