A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Attention and hybrid loss guided deep learning for consecutively missing seismic data reconstruction
J Yu, B Wu - IEEE Transactions on Geoscience and Remote …, 2021 - ieeexplore.ieee.org
Missing trace reconstruction is an essential step in the seismic data processing. Various
interpolation methods have been proposed for handling this issue. In recent years, deep …
interpolation methods have been proposed for handling this issue. In recent years, deep …
Semi-supervised learning for seismic impedance inversion using generative adversarial networks
B Wu, D Meng, H Zhao - Remote Sensing, 2021 - mdpi.com
Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …
[HTML][HTML] The potential of self-supervised networks for random noise suppression in seismic data
Noise suppression is an essential step in many seismic processing workflows. A portion of
this noise, particularly in land datasets, presents itself as random noise. In recent years …
this noise, particularly in land datasets, presents itself as random noise. In recent years …
Unsupervised deep learning for ground roll and scattered noise attenuation
The attenuation of coherent noise in land seismic data, specifically ground roll and near-
surface scattered energy, remains a longstanding challenge. Although recent advances in …
surface scattered energy, remains a longstanding challenge. Although recent advances in …
Consecutively missing seismic data interpolation based on coordinate attention unet
X Li, B Wu, X Zhu, H Yang - IEEE geoscience and remote …, 2021 - ieeexplore.ieee.org
Missing traces interpolation is a basic step in the seismic data processing workflow.
Recently, many seismic data interpolation methods based on different neural networks have …
Recently, many seismic data interpolation methods based on different neural networks have …
Learning from noisy data: An unsupervised random denoising method for seismic data using model-based deep learning
For seismic random noise attenuation, deep learning has attracted much attention and
achieved promising performance. However, compared with conventional methods, the …
achieved promising performance. However, compared with conventional methods, the …
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 …
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 …
Ground truth-free 3-D seismic random noise attenuation via deep tensor convolutional neural networks in the time-frequency domain
The inherent challenge of 3-D seismic noise attenuation is determining how to uncover high-
dimensional concise structures that only exist in true signals to eliminate random noise. The …
dimensional concise structures that only exist in true signals to eliminate random noise. The …