First Arrival Time Auto-Picking Method Based on Multi-Time Windows Energy Ratio
Bibliographic record
Abstract
First arrival time auto-picking technique plays an important role in seismic exploration. It is widely used in shallow layer tomography and static correction. Conventional method that based on sliding time windows energy ratio is not stable. Here a new method based on multi-time windows energy ratio is proposed. Combining with automatic quality control and phase-domain first arrival estimation technique, our method performs perfectly on seismic records of normal S/N ratio. In the computational process of conventional sliding time windows energy ratio method, first arrivals are often determined by the maximum energy ratio of two adjacent sliding time windows. It is well known that for low S/N ratio data the conventional picking is not effective, and for high S/N ratio data weak reflections are hardly detected. The reason is that first arrival time does not correspond to the maximum energy ratio. Meanwhile conventional method sometime picks local secondary extreme of energy ratio. The new method of multi-time windows energy ratio method takes both maximum and local secondary extreme in consideration. Hence new method promotes the stability and accuracy of first arrival picking. Combined with automatic quality control and phase-domain first arrival estimation, the new method performs well in its application in the middle part of Dzungarian Basin(Northwest China).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".