Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding

To overcome the interference of noise on the exploration effectiveness of the controlled-source electromagnetic method (CSEM), we improved the deep learning algorithm by combining the denoising convolutional neural network (DnCNN) with the residual network (ResNet), and propose a method based on the...

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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 15; no. 18; p. 4456
Main Authors:	Wang, Xin, Bai, Ximin, Li, Guang, Sun, Liwei, Ye, Hailong, Tong, Tao
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.09.2023
Subjects:	Algorithms Artificial neural networks China Coding controlled-source electromagnetic method (CSEM) Data mining Deep learning denoising denoising convolutional neural network (DnCNN) Dictionaries Electromagnetism Invariants Libraries Long short-term memory Machine learning Methods Neural networks Noise reduction Remote sensing residual network (ResNet) Seismic engineering shift-invariant sparse coding (SISC) Signal to noise ratio Square waves Wavelet transforms China
ISSN:	2072-4292, 2072-4292
Online Access:	Get full text
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