物探与化探2017，Vol.41Issue(3)：505-512,8.DOI:10.11720/wtyht.2017.3.16
基于支持向量机回归的电阻率成像反演
Electrical resistivity imaging inversion based on support vector regression
摘要
Abstract
Support Vector Regression is a Learning Machine based on statistic learning theory.It has better performance of generalization and fitting precision than traditional neural network inversion under the condition of small samples learning.Under the application background of electrical resistivity imaging,SVR inversion method based on limited learning samples was studied in this paper.The key issues of sample division and data preprocessing,inversion flow and evaluation indicators were analyzed.A multi-parameter optimization method based on cross validation was presented.The optimized SVR inversion model by comparing the influence of RBF kernel functions with different ε values with the inversion results was established.Data simulation and model inversion show that the proposed inversion method has better inversion accuracy and imaging quality than traditional least squares inversion and RBFNN inversion,and is equivalent to BPNN,but it has disadvantage of only one output dimension.关键词
电阻率成像/支持向量机回归/反演Key words
electrical resistivity imaging/support vector regression/inversion分类
天文与地球科学引用本文复制引用
董莉,江沸菠,戴前伟,傅宇航..基于支持向量机回归的电阻率成像反演[J].物探与化探,2017,41(3):505-512,8.基金项目
国家自然科学基金资助项目(41604117、41374118) （41604117、41374118）
中国博士后科学基金资助项目(2015M580700) （2015M580700）
湖南省教育厅优秀青年基金资助项目(15B138) （15B138）
湖南省科技创新计划资助项目(2016JJ3086) （2016JJ3086）
