【摘要】 为了定量分析阶跃型滑坡位移与诱发因素之间的时滞效应，提高位移预测精度，本文提出新的预测模型并进行对比分析。首先基于时间序列分析将滑坡累计位移分离为趋势项和周期项；然后采用最大信息系数(C_mi)、多元经验模态分解（MEMD）方法进行多因素分析和多尺度分析，构建出多因素-多尺度MEMD预测模型；最后以三峡库区八字门滑坡为例，通过C_mi选取最优滞后期的诱发因素作为模型输入，在用MEMD方法分解多元序列的基础上建立时间多尺度模型，并与单因素-单尺度模型、多因素-单尺度模型及单因素-多尺度EMD（经验模态分解）模型进行对比。结果表明：八字门滑坡降雨和库水位的最优滞后期分别为2 d和4 d;滑坡多元序列经MEMD方法分解后得到3组模态函数，每组均有7个分量，各对应分量的时间尺度一致，其中周期项位移受诱发因素的响应具有时间多尺度特性；多因素-多尺度MEMD预测模型的均方根误差相较于以上3种对比模型分别平均降低49.4%、36.9%和27.4%,平均绝对百分比误差分别平均降低38.0%、26.4%和15.8%。更多还原

【Abstract】 In order to quantitatively analyze the time-lag effect between displacement and inducing factors of step-like landslide, as well as to improve the accuracy of displacement prediction, in this study, the authors proposed a new prediction model and conducted comparative analysis. First, the cumulative displacement was separated into trend term and periodic term based on time series analysis. Then, using maximum information coefficient(C_mi) and multivariate empirical mode decomposition（MEMD） for multi-factor analysis and multi-scale analysis, the multi-factor and multi-scale MEMD prediction model was constructed. Finally, taking Bazimen landslide in Three Gorges Reservoir area as an example, the optimal lag period inducing factors were selected as the model input through C_mi, and multi-scale prediction model was established based on the decomposition of multivariate sequence by MEMD. The proposed model was compared with other models（single-factor and single-scale model, multi-factor and single-scale model, single-factor and multi-scale EMD model）. The results showed that the optimal lag periods of rainfall and reservoir water level in Bazimen landslide were 2 d and 4 d. After decomposing the landslide multivariate sequence by MEMD, three groups of mode functions were obtained, each group had seven components, and the time-scale of each corresponding component was consistent. The response of the periodic term displacement to the inducing factors had a time multi-scale characteristic. Compared with the comparison model, the root mean square error of the multi-factor and multi-scale MEMD prediction model decreased by 49.4%, 36.9% and 27.4% on average, and the mean absolute percentage error decreased by 38.0%, 26.4% and 15.8% on average.更多还原