Electrical resistivity imaging inversion based on support vector regression
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摘要: 以电阻率成像为应用背景,研究了在有限学习样本下,支持向量机回归在电法反演中的建模方法,对反演建模时样本划分、数据预处理、反演流程、评估指标等关键技术进行了分析,给出了一种基于交叉验证(CV)的支持向量机参数寻优方法;通过比较RBF核函数在不同的参数ε下对反演结果的影响,建立了优化的电阻率成像SVR反演模型.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.
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Key words:
- electrical resistivity imaging /
- support vector regression /
- inversion
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