一种滑动检测算法下的滑坡位移时序分解方法

A Time Series Decomposition Method for Landslide DisplacementBased on Sliding Detection Algorithm

针对“阶跃式”滑坡位移时序分解模型力学解释性不强的缺陷,根据西原蠕变本构模型与自适应改进遗传算法模型,提出滑动R_(nl)阶跃点检测方法与改进加权移动平均修正阶跃项位移方法,并将该方法应用于白水河滑坡位移时序分解。将滑动R_(nl)阶跃点检测结果与MK检验结果、滑动t检验结果以及Bayes检测结果作对比。结果表明,滑动R_(nl)阶跃点检测结果更加准确与适用;同时将新型滑坡位移时序分解结果与二次移动平均时序分解结果、三次指数平滑时序分解结果以及VMD时序分解结果作对比。结果表明,新型滑坡位移时序分解方法解决了滑坡趋势项位移无规律、无力学解释性的问题,且在时序分解加法模式中单独引入滑坡位移预测中最重要的阶跃项位移,分析预测更具有针对性。因此,新型时序分解模型有一定的工程价值与时序预测借鉴价值。

To address the issue of weak mechanical interpretation in the time-series decomposition model of step-type landslide displacement,we propose a decomposition method incorporating sliding R_(nl) step-point detection and improved weighted moving average method to modify step-term displacement.Both the Nishihara creep constitutive model and a self-adaptive improved genetic algorithm model were utilized.The proposed method was applied to decompose the displacement time series of Baishuihe landslide.The results of the proposed method were compared with those of the MK Test,sliding t test,and the Bayes test,demonstrating that the sliding R_(nl) step-point detection yields more accurate and applicable results.Furthermore,the displacement time series decomposition results were also compared with those obtained from quadratic moving average time series decomposition,cubic exponential smoothing time series decomposition,and VMD time series decomposition.The findings reveal that our proposed decomposition method effectively addresses irregular displacement and enhances the mechanical interpretation of the landslide trend term.Additionally,the introduction of the most critical step-term displacement in landslide displacement prediction enhances the specificity of analysis and prediction.In conclusion,our decomposition model holds significant engineering value and serves as a valuable reference for time series prediction.