江西盘古山钨矿区开采沉陷预计的GA-SVA算法

传统矿山开采沉陷监测方法存在耗时较多且精度不高等不足,且难以对矿区开采沉陷发展趋势进行准确预计。以江西盘古山钨矿区为例,将遗传算法(Genetic algorithm,GA)与支持向量机(Support vector machine,SVM)算法相结合,提出了一种基于GA-SVM算法的开采沉陷预计方法。首先利用遗传算法(GA)对支持向量机(SVM)进行选择、变异和交叉,生成精度符合要求的数据集群;然后采用GA-SVM算法对概率积分法开采沉陷预计参数进行了训练,对矿区开采沉陷进行了预计。研究表明:基于GA-SVA算法的开采沉陷预计值与实测值的误差小于5%,基于该算法的预计值构建的矿区数字高程模型(Digital elevation model,DEM)与基于实测数据构建的数字高程模型(DEM)具有高度的一致性,表明利用所提算法预计矿山开采沉陷具有较高的精度。

Mining Subsidence Prediction Based on GA-SVM Algorithm of Pangushan Tungsten Deposit in Jiangxi Province

Traditional mining subsidence monitoring method with the shortcomings of time-consuming and low precision,which can not be used to predict the development trends of mining subsidence.Taking Pangushan tungsten mining area of Jiangxi Province as the study example,a new mining subsidence prediction method based on GA-SVM algorithm is proposed by the combination of GA (genetic algorithm) and SVM (support vector machine) algorithm.Firstly,the SVM algorithm is optimized by using GA,the operations of selection,mutate and crossover of SVM are cone by adopting GA;then,the mining subsidence prediction parameters of probability integral method based on GA-SVM algorithm,and the mining subsidence of the mining area is predicted.The study results show that the error between the mining subsidence prediction data of GA-SVM method and the actual measured data is lower than 5%,the DEM (digital elevation model) established by the mining subsidence prediction data by using GA-SVM algorithm is basically consistence to the one established by the actual measured data,which further indicated that the prediction precise of GA-SVM algorithm proposed in this paper is good.