Acoustic representation theorem based time-reversal-extrapolation for microseismic event localization
Bibliographic record
Abstract
Summary Microseismic event localization is important for microseismic monitoring. Both traveltime-based and migration-based methods are used for microseismic event localization. Traveltime-based methods require P- and Swave first-arrival picking before further processing. This type of method does not work well for low quality data because of inaccurate arrival picking. Migration-based methods avoid picking first arrivals by back-propagating the recorded field to the source location using the velocity model. Time-reversal extrapolation is a typical migrationbased method. We use a representation-theorem-based extrapolation method instead of the more conventional adjoint method. Representation-theorem-based time-reversal extrapolation offers much promise for obtainingmicroseismic event locations without the need to first pick individual arrivals, in particular if both the pressure wavefield and its spatial gradients are available. The latter requires the combined use of both hydrophone and three-component particlevelocity sensors during microseismic acquisition.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".