Focal-time estimation: A new method for stratigraphic depth control of induced seismicity
Bibliographic record
Abstract
In this presentation we describe a novel method for focal-depth determination of induced seismic events. Our approach involves joint interpretation of microseismic and induced-seismicity waveform observations along with multicomponent surface seismic data. The method operates using parallel workflows for processing induced-seismicity data and P-P and P-S data. The output is a set of calibrated P-P times for the microseismic events, which thereby enables the events to be co-rendered and visualized with the seismic data, thus providing stratigraphic control on source locations. The method requires Vp and Vs time-depth control from coincident multicomponent seismic data and is achieved by registration of P-P and P-S reflections from equivalent horizons. Hypocenter vertical locations are initially expressed as the zero-offset focal time (2-way P-P reflection time) and then converted to depth by leveraging methods available for time-depth conversion of the surface seismic data, as well as well ties using synthetic seismograms. Application of this method requires high-quality P- and S-wave picks for microseismic events, which are extrapolated to zero offset. This approach avoids the necessity to build and calibrate a 3-D velocity model for hypocenter location, nor determination of accurate absolute origin times. This method also implicitly accounts for factors that are often ill-constrained for most velocity models, e.g. velocity anisotropy, since these factors similarly affect both the induced seismicity and the 3-D seismic travel times. We apply our new method to an induced seismicity dataset with events up to ML3.6, recorded using a shallow-well monitoring array in Alberta, Canada. Reconciling the seismic processing datum with the microseismic datum was found to be a critical, but not insurmountable, challenge. The inferred focal depths place most induced events at, or above, the treatment depth.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".