中文摘要：

针对矿区地形条件复杂,常规水准测量工作量大、时效性差的问题,基于统计学习理论,提出利用支持向量回归算法计算高程异常精化似大地水准面,将GPS高程应用到矿区快速水准测定。采用基于遗传算法的全局搜索优化支持向量回归训练参数,解决了回归模型训练中人为选取参数的盲目性,提高了算法的泛化能力和回归精度。最后采用矿区实测数据,对比多项式拟合、径向基神经网络计算高程异常,结果表明：基于遗传算法的支持向量回归算法结构简单,回归精度优于多项式拟合和径向基神经网络,可以应用于矿区GPS高程拟合。

英文摘要：

Due to the complexity of the mining environment and the problems of heavy measurement workload and time-delay existing in the conventional leveling,it is proposed that the support vector regression algorithm calculating is used to calculate the refining quasi-geoid of height anomaly based on the theory of statistics,and GPS height was applied to the mine fast leveling survey.The global search optimal parameter of support vector regression training based on genetic algorithms solved the human blindness in selecting parameters in regression model,and improved the generalization ability of the algorithm and the regression accuracy.Finally,the mine field data was used to compute height anomaly by contrasting with polynomial fitting and radial basis function neural network.The results showed that the support vector regression based on genetic algorithm is simple in structure,and its accuracy is better than that by polynomial fitting and radial basis function neural network.It can be applied into mine GPS elevation fitting.