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Seismic Data Sparse Representation Using Swin Transformers
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AI
DOI：10.1109/LGRS.2024.3510685.png)
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Abstract
En 中文
Seismic data preprocessing significantly benefits from advanced sparse representation and domain transformation techniques to enhance denoising, wavefield separation, and data reconstruction. This study introduces a novel approach utilizing a deep learning framework for discrete sparse representation of seismic data. Our method utilizes a Swin Transformer-based encoding-decoding framework, which combines the hierarchical structures of CNNs with the self-attention mechanism of Transformers, to model both local and global information efficiently. This integration enables the precise characterization of seismic reflection events and the reconstruction of seismic records from a constructed sparse feature space. The proposed model has been rigorously tested on both simulated and field datasets, demonstrating its robustness, and potential provides superior decomposition of seismic data.
Keywords:
Transformers
Sparse approximation
Feature extraction
Space exploration
Reflection
Decoding
Data models
Synthetic data
Noise reduction
Merging
sparse representation
Swin Transformer
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K
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