基于InSAR技术和SA-SVR算法的矿区沉降预测模型  

Prediction Model of Mining Area Subsidence Based on InSAR Technique and SA-SVR Algorithm

作　　者：张予东 马春艳[2] ZHANG Yudong;MA Chunyan(Department of Surveying and Mapping Engineering,Henan College Surveying and Mapping,Zhengzhou 450015,China;School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454003,China)

机构地区：[1]河南测绘职业学院测绘工程系,河南郑州450015 [2]河南理工大学测绘与国土信息工程学院,河南焦作454003

出　　处：《金属矿山》2020年第11期197-202,共6页Metal Mine

摘　　要：为了解决矿区沉降预测模型精度低、预测模型与实际开采情形不符的问题,提出了一种基于合成孔径雷达干涉测量(Synthetic Aperture Radar Interferometry,InSAR)技术、支持向量回归算法(Support Vector Regression,SVR)以及模拟退火算法(Simulated Annealing,SA)相结合的新型矿区沉降预测模型。首先,以InSAR技术获取矿区沉降监测数据,对数据进行处理得到测试点的累计沉降量,并将其与GPS实际测量结果进行比较,发现二者吻合性较好。然后,进行矿区沉降预测模型构建,通过SVR算法得到静态沉降预计模型,再利用SA算法得到模型中的参数最优取值。为了使预测数据符合矿区开采实际情况,引入嵌入维数公式,得到矿区沉降预测动态模型及精度评价指标。最后,将构建的沉降动态模型应用于陕西省大柳塔矿区,得到预测值和实际监测值之间绝对误差的最大值为9 mm,相对误差的最大值为3%;模型评价指标通过计算得到试验区平均绝对误差的最大值为2.5%,最小的相关性指数为0.8,表明该模型预测精度较高。In order to solve the problems of low accuracy of the prediction model of the settlement of the mining area and the inconsistency between the prediction model and the actual mining,a new subsidence prediction model of mining area based on synthetic aperture radar interferometry(InSAR),support vector regression(SVR)and simulated annealing(SA)was proposed.Firstly,the mining subsidence monitoring data were obtained by InSAR technique,the accumulated subsidence data of the test point was obtained by processing the data.The accumulated subsidence data is basically consistent with the actual GPS monitoring results by comparing with them.Then,the subsidence prediction model of mining area was established,the static subsidence prediction model was obtained by SVR algorithm,the optimal parameters of the model was determined by SA algorithm.In order to make the prediction results conform to the actual mining situation,the embedded dimension formula is introduced to obtain the subsidence prediction model of mining area and accuracy evaluation indexes.Finally,the established model is applied to Daliuta mining area of Shaanxi Province,the maximum absolute error between the predicted value and the actual monitored value is 9 mm,and the maximum relative error is 3%,the calculation results of accuracy evaluation indexes of the model show that the maximum value of the average absolute error of the test area is 2.5%,and the minimum correlation index is 0.8,which indicate that the prediction accuracy of the model is high.

关 键 词：开采沉陷 INSAR SA-SVR算法 沉降预测 

分 类 号：TD325[矿业工程—矿井建设]