Local Slope Guided Seismic Signal Separation with Physics Informed Neural Networks
Bibliographic record
Abstract
Summary The local slope field of seismic data is an important seismic attribute that plays a pivotal role in several seismic processing tasks. However, the implementation of this concept often relied on finite-difference approximations, which required sequential solutions that are prone to numerical errors and were limited to handling a single slope. We propose a novel local slope guided seismic signal separation method built upon the Physics Informed Neural Networks (PINNs) framework. Two neural networks are jointly trained together to predict two separate components of the seismic data and the corresponding local slopes, which are forced to be of opposite signs. By doing so, events with conflicting dips can be successfully retrieved and separated. We obtain a good representation of the data on a proof of concept example, even in the case when the data are aliased. Our method is also able to accurately interpolate a synthetic shot-gather from the SEAM Phase 1 model including events with secondary conflicting dips originating from the reflections of a salt body.
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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 | 0.001 |
| 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".