2 - Partition d'une séquence d'images temps-échelle pour la séparation d'ondes dans un profil sismique
Bibliographic record
Abstract
This paper deals with the use of image processing techniques for tiling the time-frequency plane. This technique is applied on seismic wave separation. We consider data recorded by a linear array of sensors. For each recorded signal, the application of a time-frequency transform allows a two dimensional representation where the different seismic events are well localized and isolated. The segmentation by the watershed algorithm applied on each representation enables the definition of the time-frequency filters leading to the separation of the different waves. Then, in order to apply the separation algorithm to all the different recorded signals, we use the continuity from one signal to the other to perform the tracking of the different waves from one image to the next. After an initialisation step, this leads to an automatic algorithm. This algorithm is validated on a real data set and compared with a classical method. In comparison, the proposed method has the advantage to separate all the different waves simultaneously and without introducing artefact in the spatial domain. The limit of the algorithm is reached when the patterns associated to the different waves are not correctly separated in the time-frequency representation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".