Seismic fault detection using an encoder-decoder convolutional neural network with a small training set

doi:10.1093/jge/gxy015

CORC > 地质与地球物理研究所 > 中国科学院地质与地球物理研究所 > 中国科学院油气资源研究重点实验室

	Seismic fault detection using an encoder-decoder convolutional neural network with a small training set
	Li, Shengrong 1,2,3; Yang, Changchun 1,2; Sun, Hui 1,2,3; Zhang, Hao 1,2
刊名	JOURNAL OF GEOPHYSICS AND ENGINEERING
	2019-02-01
卷号	16 期号:1 页码:175-189
关键词	fault detection interpretation CNN deep learning real data
ISSN号	1742-2132
DOI	10.1093/jge/gxy015
英文摘要	In 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.
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDA14040100] ; National Natural Science Foundation of China[41804129] ; China Postdoctoral Science Foundation[2018T110137]
WOS研究方向	Geochemistry & Geophysics
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:000466715700015
资助机构	Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program of Chinese Academy of Sciences ; 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 ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; 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 ; 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 ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; 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 ; 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 ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; 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 ; 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 ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation ; China Postdoctoral Science Foundation
内容类型	期刊论文
源URL	[http://ir.iggcas.ac.cn/handle/132A11/91486]
专题	地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者	Yang, Changchun
作者单位	1.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
推荐引用方式 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.