Airborne electromagnetic inversion in one-dimensional frequency-domain based on support vector regression

被引：0

作者：

Yao Y. ^{[1
]}

Zhang Z.-H. ^{[1
]}

Shi Z.-Y. ^{[1
]}

Liu P.-F. ^{[1
]}

Zhao S.-W. ^{[2
]}

Zhang T.-Y. ^{[1
]}

Zhao M.-H. ^{[1
]}

机构：

[1] Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu

[2] China Railway Eryuan Geotechnical Engineering Limited Company, Chengdu

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2022年 / 56卷 / 01期

关键词：

Airborne electromagnetic; End-to-end; Least square support vector machine; Multiple output; One-dimensional frequency-domain inversion;

D O I：

10.3785/j.issn.1008-973X.2022.01.023

中图分类号：

学科分类号：

摘要：

The machine learning method was applied to the inversion of airborne electromagnetic data in order to improve the accuracy of airborne electromagnetic inversion in one-dimensional frequency-domain. An end-to-end inversion method of one-dimensional frequency-domain airborne electromagnetic data was proposed based on multiple-output least square support vector regression (MLS-SVR). Forward calculations of different geological models were conducted to obtain sample data set. The framework of MLS-SVR model was constructed. The input end was normalized vertical magnetic field component, and the output end was geological parameters. Then the grid-search method and the K-fold cross-validation method were applied to search for the best parameters of the MLS-SVR model. The parameters of geological model were predicted via MLS-SVR. The experimental results show that the geological parameters can be accurately predicted with MLS-SVR. MLS-SVR has the advantage of high-precision compared with single support vector regression (S-SVR) and multiple-output support vector regression (M-SVR). The inversion of the measured data shows the effectiveness of the method. Copyright ©2022 Journal of Zhejiang University (Engineering Science). All rights reserved.

引用

页码：202 / 212

页数：10

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