边坡地下水渗透压力数据时序分解方法研究

Research on Time Series Decomposition Method for Groundwater Seepage Pressure on Slopes

地下水渗透压力观测数据是典型的具有非线性、非平稳、模糊性特征的时间序列,其统计量是时变的函数,难以通过直接观察的方法确定各类因素对地下水渗透压力的影响程度及周期性变化规律,为此需针对地下水渗透压力监测数据选择适用于非平稳时间序列和良好分解效果的分解方法。借助MATLAB软件,使用了经验模态分解(EMD)、变分模态分解(VMD)以及基于麻雀算法优化的变分模态分解(SSA-VMD)分析连云港某边坡的地下水渗透压力数据。结果表明,通过麻雀搜索算法对VMD参数组合寻优可以极大地保留了各分量的物理意义,分解后的结果可以体现出各分量地下水渗透压力的时频分布特性以及环境因素对各个分量的影响,证明参数优化后的VMD具有更好的工程实践意义。

The observation data of groundwater seepage pressure is a typical time series with nonlinear,non-stationary,and fuzzy characteristics,and its statistics are time-varying functions.It is difficult to determine the degree of influence and periodic changes of various factors on groundwater seepage pressure through direct observation methods.Therefore,it is necessary to choose a decomposition method suitable for non-stationary time series and good decomposition effect for groundwater seepage pressure monitoring data.Using MATLAB software,empirical mode decomposition(EMD),variational mode decomposition(VMD),and variational mode decomposition(SSA-VMD)based on sparrow search algorithm were used to analyze the groundwater seepage pressure data of a slope in Lianyungang.The results show that the sparrow search algorithm can greatly preserve the physical meaning of each component in optimizing the combination of VMD parameters.The decomposed results can reflect the time-frequency distribution characteristics of groundwater seepage pressure in each component and the influence of environmental factors on each component,proving that the optimized VMD parameters have better engineering practical significance.