Semi-supervised fast algorithm for seismic waveform classification-Reference-Cited by-同舟云学术

Semi-supervised fast algorithm for seismic waveform classification

Published:2018-12-11 Issue: Volume: Page:
ISSN:
Container-title:International Geophysical Conference, Beijing, China, 24-27 April 2018
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Author:

Cai Hanpeng¹,Ren Haiyang²,Wu Qingping²,Peng Longkang²,Wen Chuanyong²

Affiliation:

1. School of Resources and Environment, University of Electronic Science and Technology of China, and Center for Information Geoscience, University of Electronic Science and Technology of China

2. School of Resources and Environment, University of Electronic Science and Technology of China

Publisher

Society of Exploration Geophysicists and Chinese Petroleum Society

Reference10 articles.

1. Arshin, B. M., A. R. Ghazali, Y. K. Amin, and A. E. Banes, 2014, Hybrid waveform classification applied to delineatecompartments in a complex reservoir in the Malay Basin: International Petroleum Technology Conference, http://dx.doi.org/10.2523/IPTC-18052-MS.

2. Seismic waveform classification: renewing the interest in Barrolka field, SW Queensland, Cooper Basin

3. Chopra, S., and K. J. Marfurt, 2014, Seismic facies analysis using generative topographic mapping: 84th SEG Annual Meeting, SEG, Expanded Abstracts, 1390–1394.

4. Ghoggali N, Melgani F, 2008, Genetic SVM Approach to Semisupervised Multitemporal Classification: IEEE Geoscience & Remote Sensing Letters, 5(2):212–216.

5. Jolliffe I T, 1986, Principal Component Analysis: Springer-Verlag.

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