Title :
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
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;
Conference_Titel :
Computer Engineering and Technology, 2009. ICCET '09. International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-3334-6
DOI :
10.1109/ICCET.2009.46
