Focal-time analysis: A new method for stratigraphic depth control of microseismicity and induced seismic events
Bibliographic record
Abstract
ABSTRACT Focal-time analysis is a straightforward data-driven method to obtain robust stratigraphic depth control for microseismicity or induced seismic events. The method eliminates the necessity to build an explicit, calibrated velocity model for hypocenter depth estimation, although it requires multicomponent 3D seismic data that are colocated with surface or near-surface microseismic observations. Event focal depths are initially expressed in terms of zero-offset focal time (two-way P-P reflection time) to facilitate registration and visualization with 3D seismic data. Application of the focal-time method requires (1) high-quality P- and S-wave time picks, which are extrapolated to zero offset and (2) registration of correlative P-P and P-S reflections to provide VP and VS time-depth control. We determine the utility of this method by applying it to a microseismic and induced-seismicity data set recorded with a shallow-borehole monitoring array in Alberta, Canada, combined with high-quality multicomponent surface seismic data. The calculated depth distribution of events is in good agreement with hypocenter locations obtained independently using a nonlinear global-search method. Our results reveal that individual event clusters have distinct depth distributions that can provide important clues about the mechanisms of fault activation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".