Optimizing GAN Training for 3D Seismic Microstructure Generation
Bibliographic record
Abstract
Summary In computational geophysics, generating precise 3D microstructures from seismic data is crucial for detailed subsurface analysis. Traditional methods often fall short in achieving the necessary resolution, but Generative Adversarial Networks (GANs), specifically SliceGAN, have proven effective. These networks allow for the generation of large volumes of statistically representative microstructures, improving the simulation of material properties based on their microstructural traits. However, the efficiency of GAN training is critical for both feasibility and accuracy. This study introduces an optimized GAN training method using a distributed data parallel (DDP) strategy within the PyTorch Lightning framework to leverage modern GPU computational power. Significant adaptations to the original SliceGAN code were made to incorporate PyTorch Lightning, allowing for DDP across multiple GPUs, significantly reducing training times and enhancing scalability. The method was tested on NVIDIA V100 and A100 GPUs, demonstrating near-linear scalability and a potential speedup of 48 times with eight A100 GPUs. This optimized training process notably improves the generation of complex, high-fidelity 3D microstructures essential for geophysical analysis, highlighting the advantages of PyTorch Lightning in scenarios requiring high scalability and rapid execution, thereby offering substantial benefits for geophysical research and exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".