Focal-time estimation: A new method for stratigraphic depth control of induced seismicity
Bibliographic record
Abstract
Focal-time estimation is introduced here as a novel method to obtain robust stratigraphic depth control for induced or natural earthquakes. The method requires VP and VS timedepth control from coincident multicomponent seismic data, which is achieved by registration of correlative P–P and P–S reflections. Event focal depths are initially expressed as the zero-offset focal time (2-way P–P reflection time) and then converted to depth by leveraging the abundance of data and methods available for time-depth conversion of seismic data. Application of this method requires high-quality P and S wave picks, which are extrapolated to zero offset. This approach avoids the necessity to build and calibrate a 3-D velocity model for hypocenter location, or the determination of accurate absolute origin times. This method also implicitly accounts for factors that are often ill constrained for most velocity models, such as transverse isotropy of the medium, since these factors similarly affect both the earthquake arrival times and the 3-D seismic data. We apply our new method to an induced seismicity dataset with events up to MW 3.2, recorded using a shallow borehole monitoring array in Alberta, Canada. Reconciling the seismic processing datum with the microseismic datum was found to be a critical, but not insurmountable, challenge. In contrast to many previous studies of induced seismicity where larger events typically occur in the basement, the inferred foci place induced events stratigraphically above the treatment level. Presentation Date: Tuesday, October 16, 2018 Start Time: 1:50:00 PM Location: 208A (Anaheim Convention Center) Presentation Type: Oral
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".