Stratum Recognition Method Based on Support Vector Machine

Author

Wu Wei-jiang ; Li Guo-he ; Li Hong-qi

Author_Institution

Dept. of Comput. Sci. & Technol., China Univ. of Pet., Beijing

Volume

2

fYear

2009

fDate

22-24 Jan. 2009

Firstpage

317

Lastpage

320

Abstract

In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums, promising a wide application prospect.

Keywords

geophysics computing; optimisation; pattern classification; radial basis function networks; support vector machines; well logging; RBF; SVM classifier; classification accuracy; discriminant function; kernel function; optimization; penalty parameter; stratum recognition method; support vector machine model; well-logging data; Artificial neural networks; Computer science; Data engineering; Electronic mail; Kernel; Machine learning algorithms; Petroleum; Support vector machine classification; Support vector machines; Well logging; SVM; classification accuracy; discriminant function; well logging;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Engineering and Technology, 2009. ICCET '09. International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-3334-6

Type

conf

DOI

10.1109/ICCET.2009.46

Filename

4769613