Journal of Jilin University(Earth Science Edition) ›› 2015, Vol. 45 ›› Issue (2): 639-648.doi: 10.13278/j.cnki.jjuese.201502305

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Application of Least Squares Support Vector Machine to Lithology Identification: Taking Intermediate/Basaltic Rocks of Liaohe Basin as an Example

Mou Dan1, Wang Zhuwen1, Huang Yulong2, Xu shi1, Zhou Dapeng1   

  1. 1. College of GeoExploration Scinece and Technology, Jilin University, Changchun 130026, China;
    2. College of Earth Sciences, Jilin University, Changchun 130061, China
  • Received:2014-07-02 Published:2015-03-26

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Abstract:

Least squares support vector machine (LS-SVM) is a pattern recognition method developed from the statistical learning theory. Compared with the traditional statistics, LS-SVM can effectively resolve the problems of the finite of samples,non-linearity and high-dimension, and it can achieve accurate prediction. The essence of lithology identification is for classification. We take the advantage of the classification by LS-SVM: at first, the sample space is established by using the conventional logging curves of GR、CNL、DEN、AC、RLLD; and then, the classifier of LS-SVM is optimized by searching optimal parameters using the simulated annealing algorithm and cross validation method; finally, the model of LS-SVM lithology identification is determined. Through the description of core section and the analysis of core/cuttings, 2 520 samples from the 315 m section of the 40 wells are taken as training samples in Liaohe basin, for establishing the standard of lithology identification. 110 928 logging data from 13 866 m section of the 8 wells are taken as the predicting samples. 8 types of volcanic rocks have been identified such as vesicular basalt,compact basalt,trachyte, et al. In comparison with the 316 samples from 8 wells,the corresponding identification rate is 75.2%. The accuracy and velocity of the LS-SVM lithology identification is improved distinctly compared with other well logging methods.

Key words: LS-SVM, Liaohe eastern depression, volcanic rocks, lithology classification

CLC Number: 

  • P631.8

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