The use of SVM to classify the reflection from submarine random two-phase medium

You Jiachun; Li Hongxing

doi:10.3969/J.ISSN.0253-4193.2014.03.015

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You Jiachun, Li Hongxing. The use of SVM to classify the reflection from submarine random two-phase medium[J]. Haiyang Xuebao, 2014, 36(3): 134-142. doi: 10.3969/J.ISSN.0253-4193.2014.03.015

Citation:

You Jiachun, Li Hongxing. The use of SVM to classify the reflection from submarine random two-phase medium[J]. Haiyang Xuebao, 2014, 36(3): 134-142. doi: 10.3969/J.ISSN.0253-4193.2014.03.015

You Jiachun, Li Hongxing. The use of SVM to classify the reflection from submarine random two-phase medium[J]. Haiyang Xuebao, 2014, 36(3): 134-142. doi: 10.3969/J.ISSN.0253-4193.2014.03.015

Citation:

You Jiachun, Li Hongxing. The use of SVM to classify the reflection from submarine random two-phase medium[J]. Haiyang Xuebao, 2014, 36(3): 134-142. doi: 10.3969/J.ISSN.0253-4193.2014.03.015

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The use of SVM to classify the reflection from submarine random two-phase medium

doi: 10.3969/J.ISSN.0253-4193.2014.03.015

You Jiachun¹,
Li Hongxing^2
,

1.
School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China
2.
School of Nuclear Engineering and Geophysics, East China Institute of Technology, Fuzhou 344000, China

Received Date: 2012-12-15
Rev Recd Date: 2013-10-25

Abstract

Abstract

In this paper,to better simulate the actual heterogeneity of the seabed sediment,the random medium theory is introduced into the two-phase medium theory. Firstly,through the high-order staggered-mesh finite different simulation of random two-phase media,simulated the propagation of the seismic wave of three different the sediments,which are shaly sand,mudstones,muddy conglomerate. Then,the wavelet transformation technology is used to obtain the envelopes of reflection,called as the feature vector,which will be used as the input term of neural network. Finally,support vector machine neural network based on particle swarm optimization was applied to classify these data. To further investigate the anti-noise ability of the proposed method,the 10%,30% and 50% of Gaussian white noise was added into the original data and the optimized support vector machines still achieved good classification prediction. Based on the repeatable,convenient of the computer simulation and the relevant high accuracy and the robustness of SVM,a total solution of a classification,which will be easier,deeper,further to sturdy the feature of reflection of sediments is proposed in the article.
- random two-phase medium,
- support vector machine,
- equivalent medium theory,
- particle swarm optimization

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References(42)

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