Seismic data reconstruction via wavelet-based residual deep learning
Seismic data reconstruction is one of the essential steps in the seismic data processing.
Recently, the deep learning (DL) models have attracted huge attention in seismic …
Recently, the deep learning (DL) models have attracted huge attention in seismic …
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 …
StorSeismic: A new paradigm in deep learning for seismic processing
R Harsuko, TA Alkhalifah - IEEE Transactions on Geoscience …, 2022 - ieeexplore.ieee.org
Machine learned tasks on seismic data are often trained sequentially and separately, even
though they utilize the same features (ie, geometrical) of the data. We present StorSeismic …
though they utilize the same features (ie, geometrical) of the data. We present StorSeismic …
Unsupervised deep learning for 3D interpolation of highly incomplete data
We propose to denoise and reconstruct the 3D seismic data simultaneously using an
unsupervised deep learning (DL) framework, which does not require any prior information …
unsupervised deep learning (DL) framework, which does not require any prior information …
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 …
Deblending and recovery of incomplete blended data via MultiResUnet
B Wang, J Li, D Han, J Song - Surveys in Geophysics, 2022 - Springer
Blended acquisition is still open to improve the efficiency of seismic data acquisition.
Deblending is an essential procedure to provide separated gathers for subsequent …
Deblending is an essential procedure to provide separated gathers for subsequent …
Joint use of multiseismic information for lithofacies prediction via supervised convolutional neural networks
Lithology prediction from seismic data is of great significance for sweet-spot detection,
reservoir structure delineation, and geologic model building, hence it is important in …
reservoir structure delineation, and geologic model building, hence it is important in …
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 …
Can deep learning compensate for sparse shots in the imaging domain? A potential alternative for reducing the acquisition cost of seismic data
Dense shots can improve the fold of subsurface imaging points, which is essential for the
resolution of imaging results. However, dense shots significantly increase the cost of data …
resolution of imaging results. However, dense shots significantly increase the cost of data …