Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand
Unsupervised machine learning using an unsupervised vector quantization neural network
(UVQ-NN) integrated with meta-geometrical attributes as a novel computation process as …
(UVQ-NN) integrated with meta-geometrical attributes as a novel computation process as …
De-noising the image using DBST-LCM-CLAHE: A deep learning approach
Histogram Equalization (HE) is one of the most popular techniques for this purpose. Most
histogram equalization techniques, including Contrast Limited Adaptive Histogram …
histogram equalization techniques, including Contrast Limited Adaptive Histogram …
Random noise attenuation of seismic data via self-supervised Bayesian deep learning
Z Qiao, D Wang, L Zhang, N Liu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Random noise attenuation is a crucial task in seismic data processing, which can not only
improve the signal-to-noise ratio (SNR) of seismic data but also facilitate accurate geological …
improve the signal-to-noise ratio (SNR) of seismic data but also facilitate accurate geological …
Multiscale encoder–decoder network for DAS data Simultaneous denoising and reconstruction
Distributed acoustic sensing (DAS) has been considered a breakthrough technique in
seismic data collection owing to its advantages in acquisition cost and accuracy. However …
seismic data collection owing to its advantages in acquisition cost and accuracy. However …
A data-driven workflow based on multisource transfer machine learning for gas-bearing probability distribution prediction: A case study
J Yang, N Lin, K Zhang, R Ding, Z **, D Wang - Geophysics, 2023 - library.seg.org
Machine learning (ML) plays an important role in gas-bearing prediction based on
multicomponent seismic data because it can reveal the complex relationship between …
multicomponent seismic data because it can reveal the complex relationship between …
A potential solution to insufficient target-domain noise data: Transfer learning and noise modeling
Recently, a number of deep learning (DL) methods are developed to attenuate the noise in
seismic data. Most of them show good performance under a common precondition: the …
seismic data. Most of them show good performance under a common precondition: the …
Is attention all geosciences need? Advancing quantitative petrography with attention-based deep learning
Recent advances in deep learning have transformed data-driven geoscientific analysis. In
particular, the adoption of attention mechanism in deep learning has received considerable …
particular, the adoption of attention mechanism in deep learning has received considerable …
Seismic random noise attenuation based on M-ResUNet
J Gao, Z Li, M Zhang - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Suppressing random noise and improving the signal-to-noise ratio of seismic data are of
great significance for subsequent high-precision processing. As one of the most popular …
great significance for subsequent high-precision processing. As one of the most popular …
ASHFormer: axial and sliding window based attention with high-resolution transformer for automatic stratigraphic correlation
N Liu, Z Li, R Liu, H Zhang, J Gao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The stratigraphic correlation of well logs is crucial for characterizing subsurface reservoirs.
However, due to the complexity of well logs and the huge amount of well data, manual …
However, due to the complexity of well logs and the huge amount of well data, manual …
Seismic data denoising using a self-supervised deep learning network
Deep learning (DL) techniques have recently attracted considerable attention in the field of
seismic data denoising. However, most DL-based seismic denoising models require a …
seismic data denoising. However, most DL-based seismic denoising models require a …