文献类型：会议论文

英文题名：Recognition and predicting lava underground based on regression support vector machine using seismic signal

作者：Gao, Meijuan[1,2]; Tian, Jingwen[1,2]; Li, Jin[2]

第一作者：高美娟;Gao, Meijuan

通讯作者：Gao, M.

机构：[1] Department of Automatic Control, Beijing Union University, Beijing, China; [2] School of Information Science, Beijing University of Chemical Technology, Beijing, China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]Department of Automatic Control, Beijing Union University, Beijing, China|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006

会议日期：June 25, 2006 - June 28, 2006

会议地点：Luoyang, China

语种：英文

外文关键词：Carbon dioxide - Geothermal energy - Natural gas - Parameter estimation - Regression analysis - Seismic waves - Support vector machines

摘要：Deep lava maybe contain petroleum and natural gas or CO2 gas. It is very important to find lava but the lava is in deep stratum and has complex structure and less sample data, so it is difficult to find lava stratum. There were some methods to predict where the lava is but some problem also was in them such as the precision of recognition and predicting was not high limited by the small number of sample, a regression SVM method of recognition and predicting underground lava is proposed, moreover, we propose a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters, thereby enhancing the converging speed and the predicting accuracy. The prediction results of an example prove this method validity and practicability. ? 2006 IEEE.