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 …
Seismic impedance inversion based on residual attention network
B Wu, Q Xie, B Wu - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
Deep learning (DL) has achieved promising results for impedance inversion via seismic
data. Generally, these networks, composed of convolution layers and residual blocks, tend …
data. Generally, these networks, composed of convolution layers and residual blocks, tend …
Lrdnet: lightweight lidar aided cascaded feature pools for free road space detection
Humans have long fantasized about self-driving vehicles for the sake of luxury, style, safety,
and ease. Free road space detection for collision avoidance and path planning is a vital part …
and ease. Free road space detection for collision avoidance and path planning is a vital part …
Self-supervised Multistep Seismic Data Deblending
X Chen, B Wang - Surveys in Geophysics, 2024 - Springer
The potential of blended seismic acquisition to improve acquisition efficiency and cut
acquisition costs is still open, particularly with efficient deblending algorithms to provide …
acquisition costs is still open, particularly with efficient deblending algorithms to provide …
MDA GAN: Adversarial-learning-based 3-D seismic data interpolation and reconstruction for complex missing
Y Dou, K Li, H Duan, T Li, L Dong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The interpolation and reconstruction of missing traces are crucial steps in seismic data
processing; moreover, it is also a highly ill-posed problem, especially for complex cases …
processing; moreover, it is also a highly ill-posed problem, especially for complex cases …
Swin Transformer for simultaneous denoising and interpolation of seismic data
L Gao, H Shen, F Min - Computers & Geosciences, 2024 - Elsevier
Seismic data are often characterized by low quality due to noise contamination or missing
traces. Convolutional neural networks are popular in dealing with denoising and …
traces. Convolutional neural networks are popular in dealing with denoising and …
Dual-attention-based wavelet integrated cnn constrained via stochastic structural similarity for seismic data reconstruction
W Cao, W Lu, Y Shi, Y Li, Y Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The field acquired seismic data are often irregular, which affects the accuracy of subsequent
processing algorithms. We develop a framework based on a dual-attention-based wavelet …
processing algorithms. We develop a framework based on a dual-attention-based wavelet …
Deep learning-based upgoing and downgoing wavefield separation for vertical seismic profile data
B Tao, Y Yang, H Zhou, Y Wang, F Lyu, W Li - Geophysics, 2023 - library.seg.org
The vertical seismic profile (VSP) considerably aids attenuation analysis and velocity
calibration, enabling high-resolution seismic exploration. However, the imaging and …
calibration, enabling high-resolution seismic exploration. However, the imaging and …
Coordinate Attention-Temporal Convolutional Network for Magnetotelluric Data Processing
J Li, H Cheng, J Wang, X Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Magnetotelluric (MT) has significant value in earthquake prediction, space weather
monitoring, mineral resources exploration, and deep Earth structure detection. However …
monitoring, mineral resources exploration, and deep Earth structure detection. However …
Generative interpolation via a diffusion probabilistic model
Seismic data interpolation is essential in a seismic data processing workflow, recovering
data from sparse sampling. Traditional and deep-learning-based methods have been widely …
data from sparse sampling. Traditional and deep-learning-based methods have been widely …