IGGCAS OpenIR  > 油气资源研究院重点实验室
Seismic fault detection using an encoder-decoder convolutional neural network with a small training set
Li, Shengrong1,2,3; Yang, Changchun1,2; Sun, Hui1,2,3; Zhang, Hao1,2
2019-02-01
Source PublicationJOURNAL OF GEOPHYSICS AND ENGINEERING
ISSN1742-2132
Volume16Issue:1Pages:175-189
AbstractIn seismic interpretation, fault detection is a crucial step that often requires considerable manual labor and time. The convolutional neural network (CNN) is state-of-the-art deep learning technology that can perform even better than humans at image recognition. However, traditional methods of using CNNs for prediction require a very large dataset to train the network, which is impractical for common researchers and interpreters in geophysics who have difficulty obtaining sufficient quantities of labeled real data. In this paper, we propose a method for seismic fault detection using a CNN that requires only a very small training set. We treat the fault detection process as a semantic segmentation task and train an encoder-decoder CNN, namely, a U-Net, to perform a pixel-by-pixel prediction on the seismic section to determine whether each pixel is a fault or non-fault. Using this type of CNN in the experiments, we obtain good prediction results on real data. When interpreting a new seismic volume with the proposed method, interpreters need only to pick and label several 2D sections; subsequently, the model can predict faults in any other section of the same volume, greatly improving the interpretation efficiency. To evaluate the performance of the proposed method, we introduce a fault detection accuracy index that describes the accuracy of the prediction results. In this paper, we show that using only seven seismic sections of a seismic volume to train a CNN can allow us to predict faults successfully in any other section of the same volume.
Keywordfault detection interpretation CNN deep learning real data
DOI10.1093/jge/gxy015
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDA14040100] ; National Natural Science Foundation of China[41804129] ; China Postdoctoral Science Foundation[2018T110137]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000466715700015
PublisherOXFORD UNIV PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/91486
Collection油气资源研究院重点实验室
Corresponding AuthorYang, Changchun
Affiliation1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Li, Shengrong,Yang, Changchun,Sun, Hui,et al. Seismic fault detection using an encoder-decoder convolutional neural network with a small training set[J]. JOURNAL OF GEOPHYSICS AND ENGINEERING,2019,16(1):175-189.
APA Li, Shengrong,Yang, Changchun,Sun, Hui,&Zhang, Hao.(2019).Seismic fault detection using an encoder-decoder convolutional neural network with a small training set.JOURNAL OF GEOPHYSICS AND ENGINEERING,16(1),175-189.
MLA Li, Shengrong,et al."Seismic fault detection using an encoder-decoder convolutional neural network with a small training set".JOURNAL OF GEOPHYSICS AND ENGINEERING 16.1(2019):175-189.
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