基于贝叶斯网络的地震相分类

顾元; 朱培民; 荣辉; 曾凡平; 海洋

doi:10.3799/dqkx.2013.114

Volume 38 Issue 5

Sep. 2013

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GU Yuan, ZHU Pei-min, RONG Hui, ZENG Fan-ping, HAI Yang, 2013. Seismic Facies Classification Based on Bayesian Networks. Earth Science, 38(5): 1143-1152. doi: 10.3799/dqkx.2013.114

Citation:

GU Yuan, ZHU Pei-min, RONG Hui, ZENG Fan-ping, HAI Yang, 2013. Seismic Facies Classification Based on Bayesian Networks. Earth Science, 38(5): 1143-1152. doi: 10.3799/dqkx.2013.114

GU Yuan, ZHU Pei-min, RONG Hui, ZENG Fan-ping, HAI Yang, 2013. Seismic Facies Classification Based on Bayesian Networks. Earth Science, 38(5): 1143-1152. doi: 10.3799/dqkx.2013.114

Citation:

GU Yuan, ZHU Pei-min, RONG Hui, ZENG Fan-ping, HAI Yang, 2013. Seismic Facies Classification Based on Bayesian Networks. Earth Science, 38(5): 1143-1152. doi: 10.3799/dqkx.2013.114

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Seismic Facies Classification Based on Bayesian Networks

doi: 10.3799/dqkx.2013.114

1.
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2.
Faculty of Earth Resource, China University of Geosciences, Wuhan 430074, China
3.
Dagang Branch of GRI, BGP Inc., CNPC, Tianjin 300280, China
4.
Geophysical Research Institute of Gansu Integrated Engineering Investigation of Railway, Lanzhou 730000, China

Received Date: 2012-11-13
Publish Date: 2013-09-15

Abstract

Abstract

There are three challenging issues in traditional seismic facies classification based on seismic attributes. Firstly, it is difficult to introduce priori-information into the processing of classification to enhance the result of seismic facies classification. Secondly, it is difficult to quantitatively evaluate reliability of the result for seismic facies classification. Thirdly, it is difficult to determine the weights of all parameters of Bayesian networks in classification. In order to solve the above-mentioned problems, this paper proposes a new approach of seismic facies classification based on Bayesian networks, which effectively combines the priori-information and probability distribution of the training samples to construct a reasonable classification model, and deduce the probability for each of seismic facies. According to the probability distribution of each seismic facies, we could estimate the reliability of the classification results in a quantitative manner. The principles and workflow are presented in detail for applying Bayesian networks to seismic facies classification. The numerical experiment proves that this method is correct and feasible.
- Bayesian networks,
- seismic facies classification,
- seismic attribute,
- data mining

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References(22)

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