Multi-physical Interpretable Deep Learning Network for Oil Spill Identification Based on SAR Images
J Fan, Z Sui, X Wang - IEEE Transactions on Geoscience and …, 2024 - ieeexplore.ieee.org
The application of deep learning algorithms to oil spill identification in synthetic aperture
radar (SAR) remote sensing images has enabled substantial progress. However, the end-to …
radar (SAR) remote sensing images has enabled substantial progress. However, the end-to …
Physics-Driven Neural Network for Interval Q Inversion
Y Wang, W Cao, W Geng, Z Jia… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Quality factor (Q) estimation is critical for the processing of nonstationary seismic data and is
an important indicator of oil and gas. Traditional methods for Q value estimation require the …
an important indicator of oil and gas. Traditional methods for Q value estimation require the …
Minimum Acceptance Criteria for Subsurface Scenario-based Uncertainty Models from Single Image Generative Adversarial Networks (SinGAN)
Evaluating and checking subsurface models is essential before their use to support optimum
subsurface development decision making. Conventional geostatistical modeling workflows …
subsurface development decision making. Conventional geostatistical modeling workflows …
Deep learning for spatial nonstationarity: evaluation, mitigation, and generation
L Liu - 2024 - repositories.lib.utexas.edu
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous
in many natural settings. While the advent of deep learning technologies has enabled new …
in many natural settings. While the advent of deep learning technologies has enabled new …