Self-supervised transfer learning POCS-Net for Seismic Data Interpolation
Deep learning has been widely applied to seismic data interpolation. However, most
existing methods are based on supervised learning, suffering from limitations such as low …
existing methods are based on supervised learning, suffering from limitations such as low …
CO2Seg: Automatic CO2 Segmentation From 4D Seismic Image Using Convolutional Vision Transformer
To tackle the pressing issue of climate change stemming from carbon emissions, carbon
capture and storage (CCS) projects have emerged worldwide, which aim to store carbon …
capture and storage (CCS) projects have emerged worldwide, which aim to store carbon …
GNP-WGAN: Generative Non-Local A Priori Augmented Wasserstein Generative Adversarial Networks for Seismic Data Reconstruction
R Yao, K Li, Y Dou, Z Xu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Interpolation and reconstruction of seismic data are critical steps in geophysical exploration,
with results largely dependent on the performance of the interpolation techniques and the …
with results largely dependent on the performance of the interpolation techniques and the …
A self-supervised missing trace interpolation framework for seismic data reconstruction
M Li, X Yan, C Hu - Earth Science Informatics, 2024 - Springer
Reconstruction of missing seismic traces is one of the key steps in seismic data processing.
Deep neural network-based interpolation methods for seismic trace reconstruction have …
Deep neural network-based interpolation methods for seismic trace reconstruction have …
Multi‐view synergistic enhanced fault recording data for transmission line fault classification
M Jia, X Huang, F Han, D Yan, W Wang… - IET …, 2024 - Wiley Online Library
Fault recorded data has been proven to be effective for fault diagnosis of overhead
transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording …
transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording …
Unsupervised 3D seismic data reconstruction using a weighted-attentive deep-learning framework
The physical and cost limitations of seismic data acquisition often result in the spatial
undersampling of data, which has a detrimental impact on subsequent data processing …
undersampling of data, which has a detrimental impact on subsequent data processing …
A non-uniform interpolation method for seismic data based on a diffusion probabilistic model
C Yao, YU Siwei, LIN Rongzhi - Coal Geology & …, 2024 - cge.researchcommons.org
Objective The non-uniform interpolation of seismic data is identified as a prolonged
challenge in energy exploration. Since geophones cannot be precisely placed at positions …
challenge in energy exploration. Since geophones cannot be precisely placed at positions …
물리탐사분야에서의딥러닝기술: 현황, 도전과제및미래방향
유지윤， 오종찬， 공신혜， 이창훈， 임지원， 윤대웅 - 한국자원공학회지, 2024 - dbpia.co.kr
인공지능 기술의 급속한 발전과 함께, 물리탐사 분야 또한 기계학습 및 딥러닝 기술이 활발히
적용되고 있다. 이 논문에서는 탄성파, 중력, 자력, 전기 및 전자, 지표투과 레이다 탐사 그리고 …
적용되고 있다. 이 논문에서는 탄성파, 중력, 자력, 전기 및 전자, 지표투과 레이다 탐사 그리고 …