Simultaneous source deblending using a deep residual network
Why this work is in the frame
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Bibliographic record
Abstract
The rapid development of deep learning has prompted researchers across disciplines to consider learning-based approaches as alternatives to conventional methods. In this study, we apply a deep residual convolutional neural network (CNN) to the problem of simultaneous-source deblending. Training data are produced by synthesizing a simultaneous-source acquisition from the Valhall OBC data set. The data are sorted into common-receiver gathers (CRG) where blended signal appears as erratic noise. Regular patches are extracted from the blended and unblended gathers to form training pairs. After training, the trained network is able to produce deblended receiver gathers that remove large parts of the erratic noise. Examination of the deblended shot gathers indicates that some signal is not recovered, suggesting the potential for further improvement.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it