干旱区生态环境知识资源中心(Arid): DESERT SEISMIC DATA DENOISING AND EFFECTIVE SIGNAL RECOVERY BY USING IMPROVED SHEARLET TRANSFORM BASED ON THE DEEP-LEARNING COEFFICIENT SELECTION

Arid

	DESERT SEISMIC DATA DENOISING AND EFFECTIVE SIGNAL RECOVERY BY USING IMPROVED SHEARLET TRANSFORM BASED ON THE DEEP-LEARNING COEFFICIENT SELECTION
	Dong, Xintong; Li, Yue; Yang, Baojun
通讯作者	Li, Y (corresponding author), Jilin Univ, Coll Commun Engn, Jilin, Jilin, Peoples R China.
来源期刊	JOURNAL OF SEISMIC EXPLORATION
ISSN	0963-0651
出版年	2021
卷号	30 期号:5 页码:455-479
英文摘要	Contamination of seismic data by background noise causes difficulties for imaging, reservoir fluid prediction, and stratigraphic interpretation. Desert seismic data poses a particular problem mainly due to two reasons: (1) low signal-to-noise ratio (SNR); (2) serious frequency spectrum overlapping between the effective signals and low-frequency noise (mainly including random noise and surface waves). Therefore, when apply sparse-transform-based methods to denoise desert seismic data, conventional threshold functions fail to distinguish the effective signal coefficients and low-frequency noise coefficients, which is likely to result in residual noise and signal leakage. To solve this problem, we utilize the convolutional neural network (CNN) to act as a threshold function, thereby establishing an optimal non-linear relationship between noisy coefficients and effective signal coefficients. In addition, in order to achieve multi-scale and multi-direction accurate noise suppression, we construct a corresponding training dataset for each sub-band, so as to obtain a CNN-based coefficient selection model suitable for this sub-band. In this paper, we take shearlet transform as an example to verify the effectiveness of the proposed CNN-based threshold function. Synthetic and real examples demonstrate that our method can effectively suppress the desert low-frequency noise and completely recover the effective signals reflected by layers.
英文关键词	desert seismic data shearlet transform noise suppression convolutional neural network low signal-to-noise ratio spectrum overlapping
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000723481100004
WOS关键词	CONVOLUTIONAL NEURAL-NETWORK ; RANDOM-NOISE ATTENUATION ; MICROSEISMIC DATA ; BACKGROUND-NOISE ; DECOMPOSITION ; SUPPRESSION
WOS类目	Geochemistry & Geophysics
WOS研究方向	Geochemistry & Geophysics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373652
作者单位	[Dong, Xintong] Jilin Univ, Coll Instrumentat & Elect Engn, Jilin, Jilin, Peoples R China; [Li, Yue] Jilin Univ, Coll Commun Engn, Jilin, Jilin, Peoples R China; [Yang, Baojun] Jilin Univ, Coll Geoexplorat Sci & Technol, Jilin, Jilin, Peoples R China
推荐引用方式 GB/T 7714	Dong, Xintong,Li, Yue,Yang, Baojun. DESERT SEISMIC DATA DENOISING AND EFFECTIVE SIGNAL RECOVERY BY USING IMPROVED SHEARLET TRANSFORM BASED ON THE DEEP-LEARNING COEFFICIENT SELECTION[J],2021,30(5):455-479.
APA	Dong, Xintong,Li, Yue,&Yang, Baojun.(2021).DESERT SEISMIC DATA DENOISING AND EFFECTIVE SIGNAL RECOVERY BY USING IMPROVED SHEARLET TRANSFORM BASED ON THE DEEP-LEARNING COEFFICIENT SELECTION.JOURNAL OF SEISMIC EXPLORATION,30(5),455-479.
MLA	Dong, Xintong,et al."DESERT SEISMIC DATA DENOISING AND EFFECTIVE SIGNAL RECOVERY BY USING IMPROVED SHEARLET TRANSFORM BASED ON THE DEEP-LEARNING COEFFICIENT SELECTION".JOURNAL OF SEISMIC EXPLORATION 30.5(2021):455-479.