CAUC: Combining Channel Attention U-Net and Convolution for Seismic Data Resolution Improvement

Seismic analysis and interpretation are sensitive to data resolution. High-density acquisition, deconvolution, and inverse Q filtering are traditional methods for resolution improvement. These methods have drawbacks such as strict assumptions and complex parameter solving processes. In this letter...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2023, Vol.20, p.1-5
Main Authors: Min, Fan, Tang, Jinyu, Pan, Shulin, Song, Guojie
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Channel attention
Coefficients
Computational modeling
Convolution
Data models
Iterative methods
Neural networks
Petroleum
Prediction algorithms
Reflection
resolution improvement
Seismic analysis
Seismic data
Seismological data
Spatial resolution
Thin films
Training
U-Net
Wave reflection
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Summary:Seismic analysis and interpretation are sensitive to data resolution. High-density acquisition, deconvolution, and inverse Q filtering are traditional methods for resolution improvement. These methods have drawbacks such as strict assumptions and complex parameter solving processes. In this letter, we propose a channel attention U-Net and physical convolution combination (CAUC) algorithm to overcome these limitations. First, we design a channel attention U-Net (CAU) to establish a sophisticated nonlinear relationship between low-frequency data and coarse reflection coefficients. Specifically, we use a channel attention block after each downsampling convolution block to extract important local features of seismic data. Second, we convolve the coarse reflection coefficients with the wavelets to obtain high-resolution data. This physical convolution operation has a solid theoretical foundation. Therefore, our algorithm benefits not only from the learning ability of the neural network but also from the explainability of the physical operation. Both synthetic and field data are employed to verify the validity of the new algorithm. The traditional fast iterative shrinkage-thresholding algorithm (FISTA) and deep convolutional neural network (CNN) are used for comparison. The results show the superiority of the proposed method, especially in enhancing details such as thin layers and continuity.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3322263