Lithologic identification method in scientific drilling of the Luzong ore district
DENG Cheng-Xiang1, GAO Wen-Li2, PAN He-Ping1, KONG Guang-Sheng2, FANG Si-Nan1, LIN Zhen-Zhou2
1. Institute of Geophysics and Geomatics, China University of Geosciences(Wuhan), Wuhan 430074, China;
2. Institute of Geophysical and Geochemical Exploration, CAGS, Langfang 065000, China
On the basis of SinoProbe,scientific drilling ZK01 of the Luzong ore district located in the Yangtze River basin of eastern China is an integrated geophysical logging study.This study aims at establishing the physical property of lithologic section and revealing the vertical distribution law of metals in the lower crust.For the purpose of detecting the lithologic distribution of the Luzong ore district and providing the information concerning the distribution of metallic ores and evaluation of reserves,the authors chose Support Vector Machine (SVM) to established automatic lithologic identification model for all of the wells.Three methods,i.e.,Grid Search (GS),Particle Swarm Optimization (PSO) and Genetic Algorithm (GA),were applied to find the best parameters C and γ.GA was the best method becasuse it took 34 seconds to obtain the best parameters as (151.9852,9.1105),and its accuracy was up to 98.6364%.Compared with BP neural network identification results,the GA-SVM model achieved better accuracy of 86.86%.The lithologic identification and automatic zonation results are similar to the core data and artificial lithologic section,and the rationality and feasibility of GA-SVM are verified.
[1] 赵武生,谭伏霖,王志章,等.准噶尔盆地腹部火成岩岩性识别[J].天然气工业,2010,30(2):21-25,135-136.[2] 程国建,郭瑞华.PSO-LSSVM分类模型在岩性识别中的应用[J].西安石油大学学报:自然科学版,2010,25(1):96-99,114.[3] Vapnik V N.统计学习理论的本质[M].张学工,译.北京:清华大学出版社,2000.[4] Cristianini N,Taylor J S.支持向量机导论[M].李国正,王猛,曾华军,译.北京:电子工业出版社,2004.[5] 丁世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10.[6] 杜树新,吴铁军.模式识别中的支持向量机方法[J].浙江大学学报:工学版,2003,37(5):25-31.[7] 白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008.08.[8] 丁勇,秦晓明,何寒晖.支持向量机的参数优化及其文本分类中的应用[J].计算机仿真,2010,27(11):187-190.[9] 张银德,童凯军,郑军,等.支持向量机方法在低阻油层流体识别中的应用[J].石油物探,2008,47(3):306-310,314.[10] Holland J H.Adaptation in natural and artificial systems[M].Ann Arbor:The University of Michigan Press,1975.[11] De Jong K A.Analysis of the behavior of a class of genetic adaptive systems.Ann Arbor:The University of Michigan Press,1975.[12] Goldberg D E,Holland J H.Genetic algorithms and machine learning[J].Machine learning,1988,3(2):95-99.[13] 吴景龙,杨淑霞,刘承水.基于遗传算法优化参数的支持向量机短期负荷预测方法[J].中南大学学报:自然科学版,2009,40(1):180-184.[14] Chapelle O,Vapnik V,Bousquet O,et al.Choosing Multiple Parameters for Support Vector Machines[J].Machine Learning,2002,46(1):131-159.[15] Shao X G,Yang H Z,Chen G.Parameters selection and application of support vector machines based on particle swarm optimization algorithm[J].Control Theory and Applications,2006,23(5):740-743,748.
