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Authors: Xiaohan Du 1 ; Feng Qian 2 and Xiangqin Ou 2

Affiliations: 1 Beijing University of Posts and Telecommunications, China ; 2 University of Electronic Science and Technology of China, China

Keyword(s): Seismic, Waveform Classification, High-level Semantic Extraction, LDA.

Abstract: With the improvement of Natural energy exploration technologies, the Seismic interpretation member need to deal with more and more information and parameters. How to better use seismic characteristic parameter to detect hydrocarbon becomes increasingly complex. In this article, we deeply studied the seismic waveform classification, and propose a seismic waveform classification method based combine various characters. After reducing the dimensions of seismic wave, we classify it using the high-level semantic feature extraction technique in pattern recognition. Experiments proved that, the classification result improved in continuity and details, and reduced the redundancy of seismic signal, increased performance of classification.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Du, X.; Qian, F. and Ou, X. (2015). 3D Seismic Waveform Classification Study based on High-level Semantic Feature. In Proceedings of the 1st International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM, ISBN 978-989-758-099-4; ISSN 2184-500X, pages 29-33. DOI: 10.5220/0005402600290033

@conference{gistam15,
author={Xiaohan Du. and Feng Qian. and Xiangqin Ou.},
title={3D Seismic Waveform Classification Study based on High-level Semantic Feature},
booktitle={Proceedings of the 1st International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM,},
year={2015},
pages={29-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005402600290033},
isbn={978-989-758-099-4},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM,
TI - 3D Seismic Waveform Classification Study based on High-level Semantic Feature
SN - 978-989-758-099-4
IS - 2184-500X
AU - Du, X.
AU - Qian, F.
AU - Ou, X.
PY - 2015
SP - 29
EP - 33
DO - 10.5220/0005402600290033