Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kecman, V.: Learning and Soft Computing, Support Vector Machines, Neural Networks and Fuzzy Logic Models. The MIT Press, Cambridge (2001)
Wang, L.P., Fu, X.J.: Data Mining with Computational Intelligence. Springer, Berlin (2005)
MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Ahalt, S.C., Krishnamurty, A.K., Chen, P., Melton, D.E.: Competitive Learning Algorithms for Vector Quantization. Neural Networks 3, 277–291 (1990)
Banerjee, A., Ghosh, J.: Frequency Sensitive Competitive Learning for Scalable Balanced Clustering on High-dimensional Hyperspheres. IEEE Transactions on Neural Networks 15(3), 702–719 (2004)
Xu, L., Krzyzak, A., Oja, E.: Rival penalized competitive learning for cluster analysis RBF net and Curve Detection. IEEE Trans. Neural Network 4(4), 636–649 (1993)
Ma, J.W., Wang, T.J.: A Cost-function Approach to Rival Penalized Competitive Learning (RPCL).  IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 36(4), 722–737 (2006)
Krzyzak, A., Linder, T., Lugosi, G.: Nonparametric Estimation and Classification using Radial Basis Function nets and Empirical Risk Minimization. IEEE Trans. Neural Network 7(2), 475–487 (1996)
Matos, M.C., Osorio, P.L.M., Johann, P.R.S.: Unsupervised Seismic Facies Analysis using Wavelet Transform and Self-organizing Maps. Geophysics 72(1), 9–21 (2007)
Saggaf, M.M., ToksÖzz, M.N., Marhoon, M.I.: Seismic Facies Classification and Identification by Competitive Neural Networks. Geophysics 68(6), 1984–1999 (2003)
Fernando, A.N., Harold, T.: Multi-attribute Seismic Volume Facies Classification for Predicting Fractures in Carbonate Reservoirs. The Leading Edge 25(6), 698–700 (2006)
Gao, D.L.: Application of Seismic Texture Model Regression to Seismic Facies Characterization and Interpretation. The Leading Edge 27(3), 394–397 (2008)
Marroquin, I.D., Brault, J.J., Hart, B.S.: A Visual Data-mining Methodology for Seismic Facies Analysis: Part 1-Testing and Comparison with other Unsupervised Clustering Methods. Geophysics 74(1), 1–11 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Li, Y., Li, L. (2013). Application of Dynamic Rival Penalized Competitive Learning on the Clustering Analysis of Seismic Data. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-38715-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
eBook Packages: Computer ScienceComputer Science (R0)