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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Veröffentlicht in:	Journal of geophysics and engineering Jg. 16; H. 1; S. 52 - 61
Hauptverfasser:	Xu, Pengcheng, Lu, Wenkai, Wang, Benfeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Oxford University Press 01.02.2019
Schlagworte:	Accuracy Algorithms Classification Engineering Geophysics Lithology Neural networks Support vector machines transductive support vector machine gas chimney detection multi-attribute classification semi-supervised learning sparse autoencoder
ISSN:	1742-2132, 1742-2140
Online-Zugang:	Volltext
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