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A Self-Supervised Denoising Method Based on Deep Noise Estimation
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DOI：10.1109/LGRS.2023.3239049.png)
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摘要
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Seismic random noise attenuation is an essential procedure when processing seismic data. Due to various acquisition environments and complex geological conditions, random noise in seismic field data exhibits spatiotemporal levels, significantly increasing the difficulty of extracting seismic signals. The deep learning (DL) methods have shown excellent performances for seismic denoising. However, most existing discriminative DL methods cannot fit field data with noise levels and signal structures that differ from the training set. To tackle the unmatching challenge, we propose a self-supervised deep denoising model named noise estimation-based convolution neural network (NE-CNN), which contains a multiscale denoising module as a pretrained model and a dual-path noise estimation module that estimates the noise level of each data patch with a generalized Gaussian distribution and gray-level co-occurrence matrix (GLCM). With Stein's unbiased risk estimate (SURE), we can fine-tune the pretrained denoising module according to the estimated noise levels in a self-supervised style solely on data to be processed, leading to a boost of robustness to nonstationary seismic noise. In synthetic and field data tests, NE-CNN performed satisfactorily compared with discriminative DL methods in denoising effects on seismic data with nonstationary noise.
Keyword:
Noise reduction
Noise level
Noise measurement
Convolution
Training
Feature extraction
Estimation
Denoising
noise estimation
seismic exploration
self-supervising learning
Stein's unbiased risk estimate (SURE)
期刊
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16.4
论文数:
1.0W
被引数:
5.1K
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引用论文
A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network一种基于快速灵活卷积神经网络的深度学习去噪方法
PREAI
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PREAI
分享收藏A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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