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Found 9 papers, 2 papers with code
Evaluating the Stability of Deep Learning Latent Feature Spaces
This study introduces a novel workflow to evaluate the stability of these latent spaces, ensuring consistency and reliability in subsequent analyses.
Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation
Subsurface datasets inherently possess big data characteristics such as vast volume, diverse features, and high sampling speeds, further compounded by the curse of dimensionality from various physical, engineering, and geological inputs.
Mitigation of Spatial Nonstationarity with Vision Transformers
However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context.
Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting
Production forecast based on historical data provides essential value for developing hydrocarbon resources.
A recommender system for automatic picking of subsurface formation tops
Herein, we propose a method that does not use geophysical well logs for correlation, but rather uses already picked tops in multiple wells to recommend the depth to the remaining unpicked tops in the wells.
Machine learning-based porosity estimation from spectral decomposed seismic data
Moreover, the application is extended for a stress test by adding signal noise to the seismic data, and the workflow results show a robust estimation even with 5\% of noise.
Automatic Feature Highlighting in Noisy RES Data With CycleGAN
We implement a CycleGAN trained on these two domains to highlight layers in noisy images that can interpolate effectively without significant loss of structure or fidelity.
Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning
The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs).
Modeling nanoconfinement effects using active learning
At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.
