干旱区生态环境知识资源中心(Arid): Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation

Arid

DOI	10.1190/Geo2019-0815.1
	Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation
	Feng, Qiankun; Li, Yue; Wang, Hongzhou
通讯作者	Li, Y (corresponding author), Jilin Univ, Dept Informat Engn, Changchun 130026, Peoples R China.
来源期刊	GEOPHYSICS
ISSN	0016-8033
EISSN	1942-2156
出版年	2021
卷号	86 期号:1 页码:T19-T31
英文摘要	Deep-learning methods facilitate the development of seismic data processing methods; however, they also offer some challenges. The primary challenges are the lack of labeled samples for training, due to heterogeneity in seismic data, expensive acquisition apparatus, and data confidentiality. These problems limit the acquisition of high-quality training data. To solve this problem, we have developed variational autoencoding (VAE) to generate synthetic noise for data augmentation; however, the simplified Kullback-Leibler (KL) distance definition and parameter learning result in the outputs of the original VAE being blurry. To optimize VAE for simulating random desert noise and improve its simulation capability, here we have developed an improved VAE based on KL redefinition and learning parameter replacement. Specifically, we (1) build a training set containing desert random noise samples, (2) redefine the KL distance calculated between two Gaussian mixture densities (rather than two simple Gaussians) because the KL distance plays an important role in the learning accuracy of VAE, and (3) use log sigma(2) rather than sigma(2) to improve the learning efficiency. Statistical analysis indicates that the simulated random noise is statistically indistinguishable from real noise, indicating that our improved VAE is suitable for noise modeling. We also trained a denoising convolutional neural network (DnCNN) using the simulated noise. Data augmentation conducted using the simulated noise improved the effect of DnCNN, proving that our method contributes to data augmentation.
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000620734900002
WOS关键词	LOW-FREQUENCY NOISE ; AUTO-ENCODER ; CLASSIFICATION ; SUPPRESSION ; CNN
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/2XILL650/350426
作者单位	[Feng, Qiankun; Li, Yue; Wang, Hongzhou] Jilin Univ, Dept Informat Engn, Changchun 130026, Peoples R China
推荐引用方式 GB/T 7714	Feng, Qiankun,Li, Yue,Wang, Hongzhou. Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation[J],2021,86(1):T19-T31.
APA	Feng, Qiankun,Li, Yue,&Wang, Hongzhou.(2021).Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation.GEOPHYSICS,86(1),T19-T31.
MLA	Feng, Qiankun,et al."Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation".GEOPHYSICS 86.1(2021):T19-T31.