Cluster Analysis of Seismic Data Based on Region Scalable Fitting Model

Abstract:

In order to analysis seismic data, we propose a cluster analysis method based on region scalable fitting model which aims to adapt the image segmentation algorithm to cluster analysis. The method can be divided into 4 parts: dimension reduction, data segmentation, feature extraction, and feature cluster. Finally, we do cluster validity evaluation. The validity evaluation results show that the computational complexity of the new algorithm is relatively lower than the ordinary ones and the inhomogeneous data of high dimension can be analyzed effectively by our model. In this paper, the RSF model was applied in the high-dimensional space. Since the original sample quantity is dramatically reduced by the new algorithm, our model can be incorporated into the algorithms which have strict requirements for the size of sample.

• Periodical: Advanced Materials Research (Volumes 1049-1050)
• Pages: 1631-1636
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1631
• Online since: October 2014
• Authors: Wen Jia Lu*, Qing Qiang Wu, Dong Yun Lin
• Keywords: Cluster, Image Segmentation, Region Scalable Fitting Model, Seismic Data

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