随着国民经济快速发展，煤炭资源需求不断增加，大面积煤炭开采导致地表发生沉陷，由此产生严重的地质灾害，加强对开采区的监测和治理显得尤为重要。煤炭的持续开采形成了采空区，对采空区上方地表沉降进行持续监测和预测，可为矿区合理开采煤炭和地质灾害防治提供决策依据。

本文以彬长矿区的孟村煤矿为研究区，基于时序InSAR技术获取研究区形变信息。建立改进支持向量机回归算法（SVR）模型预测最大沉陷量，与时序InSAR技术监测结果对比验证。主要研究内容及结果总结如下：

（1）对比不同时序InSAR技术在矿区地表沉陷监测结果。采用2018年1月至2020年12月近三年时间Sentinel-1A影像，分别利用PS-InSAR技术、SBAS-InSAR技术以及优化选取GCP的SBAS-InSAR技术获取了彬长矿区孟村煤矿时间序列沉降量结果，并结合GPS观测数据对比验证，结果表明优化选取GCP的SBAS-InSAR技术精度最高。

（2）对优化选取GCP的SBAS-InSAR技术监测结果进行分析，揭示研究区401101工作面地表沉陷规律。监测结果中存在明显沉陷，其中R1处沉降漏斗位于401101工作面正上方。为了分析开采过程中地表沉陷规律，利用观测点分别沿401101工作面走向和倾向提取形变信息，发现沉陷速度均表现为先增大后减少，工作面最大沉陷量达到326mm。

（3）针对开采沉陷量与开采因素之间复杂的非线性关系，分别采用遗传算法（GA）和粒子群算法（PSO）对支持向量机回归（SVR）预测模型进行优化。构建SVR、GA-SVR、PSO-SVR三种预测模型，选取绝对误差（AE）、相对误差（RE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方根误差（RMSE）五个评价指标对预测结果进行评价。结果表明：SVR、GA-SVR、PSO-SVR三种预测模型均可用于矿区形变预测，PSO-SVR模型精度更高。通过与优化选取GCP的SBAS-InSAR技术监测结果进行比较，相对误差为8.6%。

With the rapid development of national economy and increasing demand for coal resources, large coal mining has led to surface subsidence, resulting in serious geological disasters, and it is especially important to strengthen the monitoring and management of mining areas. The continuous mining of coal has formed the mining area, and the continuous monitoring and prediction of surface subsidence above the mining area can provide a decision basis for the reasonable mining of coal and geological disaster prevention and control in the mining area.

In this paper, Mengcun coal mine in Binchang mining area is used as the study area, and the deformation information of the study area is obtained based on the time-series InSAR technique. An improved support vector machine regression (SVR) model is developed to predict the maximum subsidence, and the results are compared with the monitoring results of the time-series InSAR technique for verification. The main research contents and results are summarized as follows:

(1) Comparison of surface subsidence monitoring results of different time-series InSAR techniques in mining areas. Using Sentinel-1A images for nearly three years from January 2018 to December 2020, the time series subsidence results of Mengcun coal mine in Binchang mine area were obtained using PS-InSAR technique, SBAS-InSAR technique and SBAS-InSAR technique with optimized selection of GCP, respectively, and were verified by comparing with GPS observation data, and the results showed that the optimized selection of The results show that the SBAS-InSAR technique with GCP has the highest accuracy.

(2) The monitoring results of SBAS-InSAR technology with optimized selection of GCP were analyzed to reveal the surface subsidence pattern of working face 401101 in the study area. There is obvious subsidence in the monitoring results, in which the subsidence funnel at R1 is located directly above the working face of 401101. In order to analyze the surface subsidence pattern during mining, the observation points were used to extract the deformation information along the direction and tendency of 401101 working face respectively, and it was found that the subsidence rate all showed an increase and then a decrease, and the maximum subsidence amount of the working face reached 326mm.

(3) The support vector machine regression (SVR) prediction models were optimized using genetic algorithm (GA) and particle swarm algorithm (PSO), respectively, for the complex nonlinear relationship between mining subsidence volume and mining factors. Three prediction models of SVR, GA-SVR and PSO-SVR were constructed, and five evaluation indexes of absolute error (AE), relative error (RE), mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) were selected to evaluate the prediction results. The results show that all three prediction models, SVR, GA-SVR and PSO-SVR, can be used for mine deformation prediction, and the PSO-SVR model has higher accuracy. The relative error is 8.6% by comparing with the monitoring results of SBAS-InSAR technique with optimized selection of GCP.

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