Diff-spaformer, a U-Net transformer with channel-wise sparse attention and a diffusion-based prior, improves seismic interpolation quality by about 1 dB SNR over leading diffusion baselines while cutting sampling to four steps.
Five-dimensional interpolation: Recovering from ac- quisition constraints,
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Efficient Seismic Data Interpolation via Sparse Attention Transformer and Diffusion Model
Diff-spaformer, a U-Net transformer with channel-wise sparse attention and a diffusion-based prior, improves seismic interpolation quality by about 1 dB SNR over leading diffusion baselines while cutting sampling to four steps.