7 citations · 10 across the 4 of their papers we have counts for
4 papers
A self-supervised scheme for ground roll suppression
Sixiu Liu, Claire Birnie, Andrey Bakulin +3
In recent years, self-supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labeled data in the training stage,…
Gabor-based learnable sparse representation for self-supervised denoising
Sixiu Liu, Shijun Cheng, Tariq Alkhalifah
Traditional supervised denoising networks learn network weights through "black box" (pixel-oriented) training, which requires clean training labels. The uninterpretability nature o…
Coherent noise suppression via a self-supervised blind-trace deep learning scheme
Sixiu Liu, Claire Birnie, Tariq Alkhalifah
Coherent noise regularly plagues seismic recordings, causing artefacts and uncertainties in products derived from down-the-line processing and imaging tasks. The outstanding capabi…
The potential of self-supervised networks for random noise suppression in seismic data
Claire Birnie, Matteo Ravasi, Tariq Alkhalifah +1
Noise suppression is an essential step in any seismic processing workflow. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years,…