Similarity-informed self-learning and its application on seismic image denoising
Seismic image denoising is essential to enhance the signal-to-noise ratio (SNR) of seismic
images and facilitate seismic processing and geological structure interpretation. With the …
images and facilitate seismic processing and geological structure interpretation. With the …
RMCHN: A residual modular cascaded heterogeneous network for noise suppression in DAS-VSP records
Distributed optical fiber acoustic sensing (DAS) is an emerging acquisition technology in
seismic exploration. However, DAS records are always affected by the complex background …
seismic exploration. However, DAS records are always affected by the complex background …
Trace-wise coherent noise suppression via a self-supervised blind-trace deep-learning scheme
Seismic data denoising via supervised deep learning is effective and popular but requires
noise-free labels, which are rarely available. Blind-spot networks circumvent this …
noise-free labels, which are rarely available. Blind-spot networks circumvent this …
Gabor-based learnable sparse representation for self-supervised denoising
Traditional supervised denoising networks learn network weights through “black box”(pixel-
oriented) training, which requires clean training labels. The inability of such denoising …
oriented) training, which requires clean training labels. The inability of such denoising …
Random noise attenuation of seismic data via self-supervised Bayesian deep learning
Z Qiao, D Wang, L Zhang, N Liu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Random noise attenuation is a crucial task in seismic data processing, which can not only
improve the signal-to-noise ratio (SNR) of seismic data but also facilitate accurate geological …
improve the signal-to-noise ratio (SNR) of seismic data but also facilitate accurate geological …
Coherent noise suppression via a self-supervised blind-trace deep learning scheme
Coherent noise regularly plagues seismic recordings, causing artefacts and uncertainties in
products derived from down-the-line processing and imaging tasks. The outstanding …
products derived from down-the-line processing and imaging tasks. The outstanding …
A self‐supervised scheme for ground roll suppression
In recent years, self‐supervised procedures have advanced the field of seismic noise
attenuation, due to not requiring a massive amount of clean labelled data in the training …
attenuation, due to not requiring a massive amount of clean labelled data in the training …
A Comprehensive Study on Self-Learning Methods and Implications to Autonomous Driving
J Xing, D Wei, S Zhou, T Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
As artificial intelligence (AI) has already seen numerous successful applications, the
upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning …
upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning …
Coherent noise suppression via a self-supervised deep learning scheme
Coherent noise attenuation is an essential step in seismic data processing to improve data
quality and signal-to-noise ratio. The use of deep learning based approaches for noise …
quality and signal-to-noise ratio. The use of deep learning based approaches for noise …
Seismic data reconstruction based on attention U-net and transfer learning
Y Zhu, J Cao, H Yin, J Zhao, K Gao - Journal of Applied Geophysics, 2023 - Elsevier
In field seismic exploration, missing seismic traces is inevitably encountered due to the
constraints of the exploration environment and equipment. Thus, seismic data reconstruction …
constraints of the exploration environment and equipment. Thus, seismic data reconstruction …