Application of Dynamic Rival Penalized Competitive Learning on the Clustering Analysis of Seismic Data

摘要：

　　Rival penalized competitive learning (RPCL) has provided attractive ways to perform clustering without knowing the exact cluster number.In this paper, a new variant of the rival penalized competitive learning is proposed and it performs automatic clustering analysis of seismic data.In the proposed algorithm, a new cost function and some parameter learning methods will be introduced to effectively operate the process of clustering analysis.Simulations results are presented showing that the performance of the new RPCL algorithm is better than other traditional competitive algorithms.Finally, by clustering the seismic data, a kind of geological characteristic, underground rivers, can be extracted directly from the 3D seismic data volume.

关键词： Rival penalized competitive learning (RPCL) Pattern recognition Clustering analysis Geological characteristic

作者: Hui Wang Yan Li Lei Li

作者单位: School of Banking and Finance, University of International Business and Economics,Beijing, China School of Insurance and Economics, University of International Business and Economics,Beijing, China BGP INC., China National Petroleum Corporation

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 130-136

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）