为研究露天矿内排土场工后沉降规律，定义内排土场下沉系数为地表最终沉降量与初始覆土高度的比值，并以内蒙古锡林浩特市胜利一号露天矿内排土场为研究区，利用41期高时间分辨率sentinel-1 A数据采用小基线集(SBAS)技术进行内排土场沉降监测，在此基础上引入混沌理论中的相空间重构理论结合二阶Volterra自适应滤波对沉降时间序列进行单步预测。结果表明:① 内排土场沉降剖面呈现明显的半漏斗状，总体上累积沉降量与到矿坑的距离成反比，通过沉降时间序列分析可得Ⅰ,Ⅱ区域处于沉降活跃期，存在滑坡、泥石流风险，是后期沉降监测的重点区域，Ⅲ~Ⅶ区域步入稳定过渡期，Ⅷ区域已基本稳定，初步判断该区已基本满足了土地复垦及建设简单构筑物的基本要求。② 经曲线拟合，得观测周期内，内排土场下沉系数约为0.639 cm/m。③ 经最大Lyapunov指数验证，通过SBAS技术得到的4组沉降量时间序列均具有混沌特征。④ 运用混沌理论中的相空间重构理论结合二阶Volterra自适应滤波对沉降量进行单步预测，预测结果显示其可在短期内较好地反应真实值变化趋势，前10步预测结果的平均绝对误差(MAE)、平均相对误差(MAPE)和均方根误差(RMSE)均在6%以下，但随预测步数的增加，预测精度逐渐下降，这表明二阶Volterra自适应滤波仅可用于SBAS获取的一维沉降观测数据的短期预测，将其应用于长期预测的结果不可靠。

In order to explore the settlement laws of inner dump in open pit after construction,the subsidence coeffi- cient of inner dump is defined as the ratio of the final settlement of dump surface to the initial height of dump. The in- ner dump of Shengli No. 1 Open-pit Mine in Xilinhot City,China,is taken as the research area in this study. Forty-one high temporal resolution sentinel-1A images are used to monitor the settlement of inner dump by using the small base- line subset (SBAS) technology. On this basis,the phase space reconstruction theory in the chaos theory and the sec- ond-order Volterra adaptive filtering are introduced to realize a sin-gle-step prediction of the settlement time series. The results show that ① the subsidence profile of the inner dump is obviously semi-funnel-shaped,and the cumulative set- tlement is inversely proportional to the distance from the pit. Through the analysis of the subsidence time series,it can be concluded that the areas I and II are in the active period of subsidence,with the risk of landslide and debris flow. They are the key areas for the later subsidence monitoring. The areas III-VII enter the transition period. While the area Ⅷ has been basically stable,the area basically meets the basic requirements of land reclamation and construction of simple structures. ② By curve fitting,the subsidence coefficient of the inner dump is estimated to be 0. 639 cm / m in the observation period. ③ Verified by the maximum Lyapunov index,the four sets of settlement time series ob-tained by SBAS technology all have chaotic characteristics. ④ The phase space reconstruction theory in the chaos theory and the second-order Volterra adaptive filtering are combined to realize a single-step prediction of the settlement time se- ries. The prediction results show that it can better reflect the change trend of real value in a short time. The average ab- solute error (MAE),average relative error (MAPE) and root mean square error ( RMSE) of the first ten prediction results are all below 6% ,but with the increase of prediction steps,the prediction accuracy gradually decreases. This proves that the second-order Volterra adaptive filtering can only be used for a short-term prediction of one-dimensional settlement observation data acquired by SBAS,while the long-term prediction results are unreliable.