Support Vector Machine (SVM) pattern recognition to AVO classification

Details Support Vector Machine (SVM) pattern recognition to AVO classification Support Vector Machine (SVM) pattern recognition to AVO classification

Jan 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004georl..3102609l&link_type=abstract

Geophysical Research Letters, Volume 31, Issue 2, CiteID L02609

Statistics

Computation

Exploration Geophysics: Computational Methods, Seismic, Mathematical Geophysics: Nonlinear Dynamics, Mathematical Geophysics: General Or Miscellaneous

Scientific paper

The purpose of this paper is to present a learning algorithm to classify data with nonlinear characteristics. The Support Vector Machine (SVM) is a novel type of learning machine based on statistical learning theory [Vapnik, 1998]. The support vector machine (SVM) implements the following idea: It maps the input vector X into a high-dimensional feature space Z through some nonlinear mapping, chosen a priori. In this space, an optimal separating hyperplane is constructed to separate data groupings. The support vector machine (SVM) learning method can be used to classify seismic data patterns for exploration and reservoir characterization applications. The SVM is particularly good at classifying data with nonlinear characteristics. As an example the SVM method is applied to AVO classification of gas sand and wet sand.

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