基于SVM的隧道涌水来源识别  

Source identification of water inrush in tunnel based on SVM

作　　者：刘建[1] 刘丹[1] 

机构地区：[1]西南交通大学地球科学与环境工程学院,成都610031

出　　处：《水文地质工程地质》2012年第5期26-30,共5页Hydrogeology & Engineering Geology

基　　金：铁道部科技研究开发计划重点课题(2010Z001-D)

摘　　要：隧道涌水来源识别是隧道涌水预测的前提和隧道水害防治的基础,为向隧道施工和管理等有关部门提供科学参考依据,基于隧道涌水及其可能来源的水化学信息,利用支持向量机(SVM)技术建立了隧道涌水来源识别模型,并以垫邻高速铜锣山隧道进行了实例分析。结果表明,该隧道涌水主要来自区域嘉陵江组和雷口坡组岩溶含水岩系中的地下水,虽有须家河组碎屑岩含水岩系的地下水混入,但在量上不占优,上述结论与隧道地区地表水、井泉水及煤矿水的动态监测结果反映的信息相符,侧面反映出SVM在隧道涌水来源识别应用中具有推广价值。Source identification of water inrush in tunnel is quite important to water flow predication and prevention and cure on groundwater inundation. In order to offer scientific reference to the departments of construction and management, Support Vector Machines is introduced to build a model to identify source of groundwater inrush in tunnel, based on the chemical information of groundwater inrush in tunnel and its possible sources. Application of this model to the Tongluoshan tunnel in Dianjiang to Linshui expressway reveals that the karst water-bearing system in the Jialingjiang formation and Leikoupo formation is the major supplier rather than the water-bearing system in the factured Xujiahe formation, due to the difference of the water abundance. This conclusion corresponds to the phenomena reflected by dynamic monitoring of surface water, wells and springs, as well as water flow from mines, indicating that SVM is valuable to source identification of water inrush in tunnel.

关 键 词：支持向量机 来源识别 隧道涌水 动态监测 

分 类 号：TD745.21[矿业工程—矿井通风与安全]