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

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.