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| Electrical resistivity imaging inversion based on support vector regression |
| DONG Li1,2, JIANG Fei-Bo2, 3, DAI Qian-Wei2, FU Yu-Hang4 |
1. Department of Information Science and Engineering, Hunan International Economics University, Changsha 410205, China;
2. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
3. College of Physics and Information Science, Hunan Normal University, Changsha 410081, China;
4. Hunan Power Transmission and Distribution Survey and Design Consulting Co., Ltd., Changsha 410114, China |
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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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Received: 20 May 2016
Published: 20 June 2017
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