Zhang Q Z, He X Y, Zhang L Q, et al.Deformation monitoring and early warning analysis of high-position long-runout landslides based on GNSS/accelerometer fusion and rainfall infiltration numerical simulation[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(6):80-90.

doi:10.16186/j.cnki.1673-9787.2025100011

Received:2025/10/09

Revised:2026/01/27

Published:2026/10/09

Deformation monitoring and early warning analysis of high-position long-runout landslides based on GNSS/accelerometer fusion and rainfall infiltration numerical simulation

Zhang Qiuzhao1,2,3, He Xinyue1,2, Zhang Longqiang1,2, Liu Xin1,2,3, Zheng Nanshan1,2,3

1.State Key Laboratory of Disaster Prevention and Ecology Protection in Open-pit Coal Mines， China University of Mining and Technology， Xuzhou  221116， Jiangsu， China；2.School of Environment and Spatial Informatics， China University of Mining and Technology， Xuzhou  221116， Jiangsu， China；3.Key Laboratory of Land Environment and Disaster Monitoring， China University of Mining and Technology， Xuzhou  221116， Jiangsu， China

Abstract: Objectives To address the monitoring and early warning challenges posed by high-position， long-runout landslides triggered by rainfall infiltration in key ecological corridors of cold and high-altitude regions—characterized by large elevation differences， long sliding distances， and short acceleration phases—a graded early warning method that integrates a GNSS/accelerometer multi-rate Kalman filter with velocity threshold criteria is proposed， based on synthetic observational data generated through numerical simulation. Methods Using the COMSOL Multiphysics platform， a two-dimensional finite element model coupling solid mechanics with the Richards equation was developed to simulate surface seepage and rock damage， generating reference time series of displacement and acceleration at multiple monitoring points. Synthetic GNSS and accelerometer observations were then constructed by adding noise and slow drift to the reference data. A multi-rate Kalman filter was applied for data fusion： high-rate acceleration data drive the state prediction， while low-rate GNSS data perform the measurement update， enabling joint estimation under sampling-ratio constraints. A velocity-threshold-based graded early warning scheme was subsequently established.  Results Under simulated conditions， the fusion scheme outperforms GNSS-only monitoring in key metrics： RMSE improves by 37.4%， displacement trajectories become smoother， and warning trigger times align more closely with the model’s acceleration turning points.  Conclusions The systematic evaluation—encompassing numerical simulation， synthetic observation， multi-rate Kalman fusion， and early warning analysis—demonstrates that the proposed method can maintain timely and stable high-level alerts during the middle-to-late acceleration stage before failure of rainfall-induced high-position long-runout landslides. This study aims to provide a reference for field monitoring and warning parameter determination for landslides in cold and high-altitude ecological corridors.

Key words: high-position long-runout landslide; GNSS; accelerometer; data fusion; deformation monitoring and early warning