Multichannel Wavefield Reconstruction with Slope Assisted Physics Informed Neural Networks
Bibliographic record
Abstract
Summary We introduce a novel approach to interpolate multi-component seismic measurements using Physics Informed Neural Networks. This network is trained to predict both the pressure and horizontal particle acceleration measurements whilst being guided by the local plane wave partial differential equation. The local slope parameter of the plane wave differential equation is simultaneously estimated during the interpolation process using a smaller auxiliary network. The results of the new PINN multicomponent reconstruction algorithm, named MC PINNslope are compared on a synthetic receiver gather against a previous version of PINN-based interpolation that leverages only the pressure data, showing that the addition of measured gradient information in our new implementation allowed us to interpolate sparse acquisition scenarios that even the original PINN-based interpolation could not handle. In the second example, we benchmark the MC PINNslope against a state-of-the-art slope regularized sparsity promoting inversion. The two methods are compared on field data with progressively coarser subsampling, demonstrating that MC PINNslope can outperform the conventional method at sparser recordings. Finally, we showcase the grid-based training feature of MC PINNslope (and PINN-based interpolators in general) interpolating the field seismic data at a denser trace spacing of the original fully sampled field data without the need of retraining.
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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".