A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA  

作　　者：DONG Hanchuan LIU Shuang PANG Lili LIU Dunlong DENG Longsheng FANG Lide ZHANG Zhonghua 

机构地区：[1]College of Quality and Technical Supervision,Hebei University,Baoding 071002,China [2]Technology Innovation Center for Geological Environment Monitoring,Ministry of Natural Resources,Tianjin 300304,China [3]Center for Hydrogeology and Environmental Geology Survey,China Geological Survey,Tianjin 300304,China [4]Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610299,China [5]College of Software Engineering,Chengdu University of Information and Technology,Chengdu 610225,China [6]School of Geological Engineering and Geomatics,Chang’an University,Xi’an 710054,China

出　　处：《Journal of Mountain Science》2024年第12期4019-4032,共14页山地科学学报（英文）

基　　金：funded by National Key R&D Program of China(No.2022YFC3003403);Sichuan Science and Technology Program(No.2024NSFSC0072);Natural Science Foundation of Hebei Province(No.F2021201031);Geological Survey Project of China Geological Survey(No.DD20230442).

摘　　要：Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.

关 键 词：Debris flow infrasound Variational Mode Decomposition Sparrow search algorithm ARIMA model Hilbert transform 

分 类 号：P642.23[天文地球—工程地质学]