基于支持向量机的翟镇矿小断层预测  

Forecast of Small Fault Based on SVM in Zhaizhen Mine

作　　者：滕超[1] 施龙青[1,2] 邱梅[1] 

机构地区：[1]山东科技大学地球科学与工程学院,山东青岛266590 [2]山东省沉积成矿作用与沉积矿产重点实验室,山东青岛266590

出　　处：《煤炭技术》2016年第5期125-127,共3页Coal Technology

基　　金：国家自然科学基金(41572244);教育部高等学校博士学科点专项科研基金(20133718110004);山东省自然科学基金(ZR2015DM013);山东科技大学科研创新团队支持计划资助(2012KYTD101)

摘　　要：以翟镇煤矿实测小断层数据为研究对象,利用散点图法和灰色关联法对断层要素间的相关性进行分析,发现断层延展长度与倾向、走向、倾角、落差间非线性关系。基于支持向量机建立小断层延展长度非线性预测模型。通过对比翟镇、良庄煤矿小断层实测数据与预测数据,发现预测模型预测准确度高,还能进行相似地质条件下预测。Based on faults data of 11 coal seam in Zhaizhen coal mine, study the correlativity between elements of faults by scatter diagram and gray correlation analysis method. It turned out that there is non-liner relationship between extending length and other elements of small faults like dip, strike, dip angel and fault throw. SVM is chosen to build forecast model of small faults. In comparison with raw data in Zhaizhen coal mine and Liangzhuang coal mine, forecasts are accurate. SVM model can be applied to forecast small faults in Zhaizhen coal are similar. mine and other coal mines whose geological conditions

关 键 词：小断层 非线性回归 支持向量机 

分 类 号：TD163[矿业工程—矿山地质测量]