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 …
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 …
Re‐visible blind block network: An unsupervised seismic data random noise attenuation method
J Wang, B Wu, H Yang, B Li - Geophysical Prospecting, 2024 - Wiley Online Library
Noise is inevitable when acquiring seismic data, and effective random noise attenuation is
crucial for seismic data processing and interpretation. Training and inferencing two‐stage …
crucial for seismic data processing and interpretation. Training and inferencing two‐stage …
[PDF][PDF] Recent advances of deep learning in geological hazard forecasting
Geological hazard is an adverse geological condition that can cause loss of life and
property. Accurate prediction and analysis of geological hazards is an important and …
property. Accurate prediction and analysis of geological hazards is an important and …
S2S-WTV: Seismic data noise attenuation using weighted total variation regularized self-supervised learning
Seismic data often undergo severe noise due to environmental factors, which seriously
affect subsequent applications. Traditional hand-crafted denoisers such as filters and …
affect subsequent applications. Traditional hand-crafted denoisers such as filters and …
Deep nonlocal regularizer: A self-supervised learning method for 3d seismic denoising
Noise suppression for seismic data can meliorate the quality of many subsequent
geophysical tasks. In this work, we propose a novel self-supervised learning method, the …
geophysical tasks. In this work, we propose a novel self-supervised learning method, the …
Irregularly sampled seismic data interpolation with self-supervised learning
W Fang, L Fu, M Wu, J Yue, H Li - Geophysics, 2023 - library.seg.org
Supervised convolutional neural networks (CNNs) are commonly used for seismic data
interpolation, in which a recovery network is trained over corrupted (input)/complete (label) …
interpolation, in which a recovery network is trained over corrupted (input)/complete (label) …
An unsupervised learning method to suppress seismic internal multiples based on adaptive virtual events and joint constraints of multiple deep neural networks
K Wang, T Hu, B Zhao, S Wang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In seismic data processing, the suppression of internal multiple is a challenging direction. To
suppress internal multiples, we propose an unsupervised deep neural network (DNN) …
suppress internal multiples, we propose an unsupervised deep neural network (DNN) …
Learning to decouple and generate seismic random noise via invertible neural network
Recovering the useful signal from seismic field data is critical in seismic data processing.
Seismic field data are usually coupled by a useful signal and field noise (random noise with …
Seismic field data are usually coupled by a useful signal and field noise (random noise with …
Total variation regularized self-supervised Bayesian deep learning for seismic random noise attenuation
D Wang, Z Qiao, L Zhang, N Liu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Random noise attenuation is an essential procedure of seismic data processing, which is
crucial to improve the signal-to-noise ratio (SNR) of seismic data. Recently, deep learning …
crucial to improve the signal-to-noise ratio (SNR) of seismic data. Recently, deep learning …