[HTML][HTML] Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
Automated seismic fault interpretation has been an active area of research. Since 2018,
Deep learning (DL) based seismic fault interpretation methods have emerged and shown …
Deep learning (DL) based seismic fault interpretation methods have emerged and shown …
Similarity-informed self-learning and its application on seismic image denoising
Seismic image denoising is essential to enhance the signal-to-noise ratio (SNR) of seismic
images and facilitate seismic processing and geological structure interpretation. With the …
images and facilitate seismic processing and geological structure interpretation. With the …
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 …
Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities
Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
Automatic fault delineation in 3-D seismic images with deep learning: Data augmentation or ensemble learning?
S Li, N Liu, F Li, J Gao, J Ding - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Delineating seismic faults is one of the main steps in seismic structure interpretation.
Recently, deep learning (DL) models are used to automatic seismic fault interpretation. For …
Recently, deep learning (DL) models are used to automatic seismic fault interpretation. For …
Self-adaptive denoising net: Self-supervised learning for seismic migration artifacts and random noise attenuation
Seismic noise attenuation is essential for seismic interpretation and reservoir
characterization. Recently, many researchers have applied convolutional neural network …
characterization. Recently, many researchers have applied convolutional neural network …
Semi-supervised learning for seismic impedance inversion using generative adversarial networks
B Wu, D Meng, H Zhao - Remote Sensing, 2021 - mdpi.com
Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …
Quantum-enhanced deep learning-based lithology interpretation from well logs
N Liu, T Huang, J Gao, Z Xu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Lithology interpretation is important for understanding subsurface properties. Yet, the
common manual well log interpretation is usually with low efficiency and bad consistency …
common manual well log interpretation is usually with low efficiency and bad consistency …
Seismic data reconstruction based on multiscale attention deep learning
Seismic data reconstruction is always an essential step in the field of seismic data
processing. Effective reconstruction methods can obtain high-density information at low-cost …
processing. Effective reconstruction methods can obtain high-density information at low-cost …