Affiliation:
1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
2. Exploration and Development Research Institute, Liaohe Oilfield of CNPC, Panjin 124010, China
Abstract
Convolutional neural network- (CNN-) based deep learning (DL) architectures have achieved great success in many fields such as remote sensing, medical image processing, and computer vision. Recently, CNN-based models have also been attempted to solve geophysical problems. This paper presents a noise attenuation method of seismic data via a novel deep learning (DL) architecture, namely, deep multiscale fusion network (MSFN). Firstly, we integrate multiscale fusion (MSF) block to adaptively exploit local signal features at different scales from seismic data. And then, a series of stacked MSF blocks are formed into MSFN, which can restore the noisy seismic data effectively and preserve more useful signal information. Furthermore, a comparative study of our method and other leading edge ones is conducted by using synthetic seismic records and the SEG/EAGE salt and overthrust models. The results qualitatively and quantitatively show the capability of our method of achieving higher peak signal-to-noise ratios (PSNRs) while preserving much more useful information, comparing with other methods. Finally, our method is utilized in the real seismic data processing, obtaining satisfactory results.
Funder
Liaoning Technical University
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Cited by
1 articles.
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