A semi-supervised learning framework for gas chimney detection based on sparse autoencoder and TSVM
Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seis...
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| Vydané v: | Journal of geophysics and engineering Ročník 16; číslo 1; s. 52 - 61 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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Oxford University Press
01.02.2019
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| ISSN: | 1742-2132, 1742-2140 |
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| Abstract | Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seismic interpretation results, we take advantage of the information of unlabeled data by unsupervised feature extraction and semi-supervised classification methods. The sparse autoencoder (SAE) is an unsupervised learning method that can extract the features of data without labels, and the transductive support vector machine (TSVM) is a semi-supervised method that trains a classifier according to both labeled and unlabeled data. In this paper, we propose a semi-supervised learning framework that combines SAE and TSVM to detect gas chimneys. In this framework, SAE is used to extract features from data and TSVM is used to classify the labeled and unlabeled features. Therefore, the unlabeled data is taken advantage of in both unsupervised feature extraction and semi-supervised classification to improve accuracy. In order to improve the precision of detection, the attributes of neighborhood regions are also utilized. Due to the information learned from plenty of unlabeled data, the proposed framework performs well. Numerical experiments are carried out on sample sets and field data. The proposed framework has higher testing accuracy than the traditional MLP method, especially when the labeled training set is small. In field data experiments, the proposed framework also gets good prediction results for gas chimney locations. |
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| AbstractList | Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seismic interpretation results, we take advantage of the information of unlabeled data by unsupervised feature extraction and semi-supervised classification methods. The sparse autoencoder (SAE) is an unsupervised learning method that can extract the features of data without labels, and the transductive support vector machine (TSVM) is a semi-supervised method that trains a classifier according to both labeled and unlabeled data. In this paper, we propose a semi-supervised learning framework that combines SAE and TSVM to detect gas chimneys. In this framework, SAE is used to extract features from data and TSVM is used to classify the labeled and unlabeled features. Therefore, the unlabeled data is taken advantage of in both unsupervised feature extraction and semi-supervised classification to improve accuracy. In order to improve the precision of detection, the attributes of neighborhood regions are also utilized. Due to the information learned from plenty of unlabeled data, the proposed framework performs well. Numerical experiments are carried out on sample sets and field data. The proposed framework has higher testing accuracy than the traditional MLP method, especially when the labeled training set is small. In field data experiments, the proposed framework also gets good prediction results for gas chimney locations. |
| Author | Lu, Wenkai Xu, Pengcheng Wang, Benfeng |
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| Cites_doi | 10.1016/j.jappgeo.2014.11.007 10.1016/j.jappgeo.2015.11.006 10.1190/INT-2015-0098.1 10.1088/1742-2140/aaa2f0 10.1190/geo2014-0065.1 10.1038/323533a0 10.1016/j.jappgeo.2015.04.004 10.1088/1742-2140/aaa4db 10.1088/1742-2132/8/4/011 10.1126/science.1127647 10.1088/1742-2140/aa8433 10.1071/EG00481 10.1080/01431161.2016.1171928 10.1190/1.1438976 10.3997/2214-4609.20148759 10.1109/MSP.2017.2785979 10.1088/1742-2140/aa5b5b 10.3997/2214-4609.20141200 10.1016/j.jappgeo.2013.12.004 10.1016/j.jappgeo.2016.03.027 10.1190/1.3479999 10.1016/j.jappgeo.2014.06.012 10.3997/2214-4609.201700920 10.1190/1.2392789 10.1190/geo2017-0495.1 10.1088/1742-2132/11/6/065005 10.1190/1.1437657 10.1109/LGRS.2015.2482520 10.1190/geo2010-0185.1 10.1016/S0920-4105(01)00090-0 10.1109/LGRS.2017.2785834 10.1109/ICOSP.2006.346109 |
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| Keywords | transductive support vector machine gas chimney detection multi-attribute classification semi-supervised learning sparse autoencoder |
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