[HTML][HTML] Machine learning in microseismic monitoring
The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
A multidirectional deep neural network for self-supervised reconstruction of seismic data
Seismic studies exhibit gaps in the recorded data due to surface obstacles. To fill in the gaps
with self-supervised deep learning, the network learns to predict different events from the …
with self-supervised deep learning, the network learns to predict different events from the …
Seismic data reconstruction based on a multicascade self-guided network
Due to inherent limitations in data acquisition, seismic data reconstruction is an important
procedure to recover missing data or improve observation density. Many conventional …
procedure to recover missing data or improve observation density. Many conventional …
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 …
Deep learning vertical resolution enhancement considering features of seismic data
Y Gao, D Zhao, T Li, G Li, S Guo - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The resolution of seismic data determines the ability to characterize individual geological
structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for …
structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for …
An unsupervised deep neural network approach based on ensemble learning to suppress seismic surface-related multiples
K Wang, T Hu, B Zhao - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
Surface-related multiples are generally removed as noise. To suppress surface-related
multiples, we propose an unsupervised deep neural network approach based on ensemble …
multiples, we propose an unsupervised deep neural network approach based on ensemble …
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) …
A projection-onto-convex-sets network for 3D seismic data interpolation
Seismic data interpolation is an essential procedure in seismic data processing. However,
conventional interpolation methods may generate inaccurate results due to the simplicity of …
conventional interpolation methods may generate inaccurate results due to the simplicity of …
Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data
Subsurface reflectivity imaging is one of the most important geophysical characterization
methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity …
methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity …
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) …