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Support Vector Machine (SVM) pattern recognition to AVO classification
Author(s) -
Li Jiakang,
Castagna John
Publication year - 2004
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2003gl018299
Subject(s) - support vector machine , hyperplane , pattern recognition (psychology) , statistical learning theory , artificial intelligence , relevance vector machine , structured support vector machine , feature vector , nonlinear system , computer science , a priori and a posteriori , machine learning , feature (linguistics) , least squares support vector machine , mathematics , physics , philosophy , linguistics , geometry , epistemology , quantum mechanics
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.