Seismic pattern recognition based upon Hopfield Neural Networks
Rafael Banchs 1 Oscar Rondon 2 31 Signal Processing and Communications Department, Universitat Politècnica de Catalunya, Campus Nord, D4-100, Barcelona 08034, Spain.
2 Snowden Group, 87 Colin Street, West Perth, Perth, Western Australia, Australia, WA 6005, Australia.
3 Corresponding author. Email: orondon@snowdengroup.com
Exploration Geophysics 38(4) 220-224 https://doi.org/10.1071/EG07024
Submitted: 19 September 2006 Accepted: 2 October 2007 Published: 6 December 2007
Abstract
A classification method based on the use of seismic attribute pattern recognition by means of Hopfield Neural Networks is presented. The method is suitable for exploration projects and it can be used to simultaneously perform the analysis of several references or classes and for constructing seismic similarity maps or volumes. First, a brief description of Hopfield Neural Networks and their operational principles is presented, and the most relevant issues of the proposed classification methodology are described. Then, the method is demonstrated by using a synthetic dataset and two real case studies, illustrating the potential of the method as a useful tool for exploration geophysics and reservoir characterisation.
Key words: pattern recognition, Hopfield neural networks, seismic attributes, seismic similarity, classification.
Acknowledgments
We thank Dr Juan Jimenez for his valuable help with the geological description of the regions analysed in the case studies, as well as the interpretation of some of the results.
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