Enhancing seismic image resolution using Brownian diffusion bridge model
Bibliographic record
Abstract
Seismic images often lack the high resolution needed for proper identification of subsurface structures and potential hydrocarbon reservoirs, and that is due to factors such as limited data acquisition and the attenuation of seismic waves as they travel through the Earth’s subsurface layers. Most of the seismic image enhancement algorithms do not account for prior features of high resolution seismic data. Thus, we propose using a generative diffusion model to enhance seismic image resolution. Specifically, we employ the Brownian diffusion bridge model (BBDM) to translate samples from a lowresolution image distribution to one corresponding to a highresolution image distribution. To address the issue of training solely on synthetic data and improve the generality of the neural network, we adopted a robust training procedure using the know-distillation technique within a “teacher-student” framework. Field datasets demonstrated the robustness and good performance of the proposed method. Additionally, the intrinsic denoising feature of the diffusion model provides an added image-denoising capability for our methodology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".