干旱区生态环境知识资源中心(Arid): Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic

Arid

DOI	10.1093/gji/ggz363
	Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic
	Dong, X. T.1; Li, Y.1 ; Yang, B. J.2
通讯作者	Li, Y.
来源期刊	GEOPHYSICAL JOURNAL INTERNATIONAL
ISSN	0956-540X
EISSN	1365-246X
出版年	2019
卷号	219 期号:2 页码:1281-1299
英文摘要	The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernelpatch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.
英文关键词	Seismic attenuation Seismic noise Surface waves and free oscillations
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000491050200040
WOS关键词	EMPIRICAL MODE DECOMPOSITION ; SEISMIC DATA ; MICROSEISMIC DATA ; CNN ; ATTENUATION ; CLASSIFICATION ; ENHANCEMENT ; RECOGNITION ; REDUCTION ; ALGORITHM
WOS类目	Geochemistry & Geophysics
WOS研究方向	Geochemistry & Geophysics
EI主题词	2019-11-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310394
作者单位	1.Jilin Univ, Dept Informat Engn, Changchun 130026, Jilin, Peoples R China; 2.Jilin Univ, Dept Geophys, Changchun 130026, Jilin, Peoples R China
推荐引用方式 GB/T 7714	Dong, X. T.,Li, Y.,Yang, B. J.. Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic[J],2019,219(2):1281-1299.
APA	Dong, X. T.,Li, Y.,&Yang, B. J..(2019).Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic.GEOPHYSICAL JOURNAL INTERNATIONAL,219(2),1281-1299.
MLA	Dong, X. T.,et al."Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic".GEOPHYSICAL JOURNAL INTERNATIONAL 219.2(2019):1281-1299.