Processed ground-motion records from induced earthquakes for use in engineering applications
Bibliographic record
Abstract
We compile and process an electronic database of ground motions recorded on accelerometers and broadband seismographic instruments for induced earthquakes of M ≥ 4 at distances <50 km in central and eastern North America. Most of the data are from Oklahoma, with some records from Alberta. Our focus is on the subset of available records that are of most interest for engineering analyses aimed at evaluation of the potential hazards from induced events, which is a pressing issue in western Canada and other regions experiencing induced seismicity. We considered all records to 50 km for events of M ≥ 4.5. For events of M 4 to 4.5, we select records at close distance (<10 km), having good signal strength (PGA > ∼3%g), to allow high-quality time histories to be obtained. These records have strong signal-to-noise ratio, making them suitable for engineering applications, such as dynamic analysis, after proper scaling. The selected records are windowed, filtered, and instrument-corrected to compile a set of records having acceptable acceleration, velocity, and displacement time histories. The records and their response spectra are provided as an electronic supplement at http://www.seismotoolbox.ca/IS_Strong_Motions/ . We note that the record set is not suitable as a response spectra database for development of ground-motion prediction equations, because for M < 4.5 the record selection is biased to records with higher amplitudes. Rather, the intended use of the records is as seed records, which can be readily scaled in the time domain to approximately represent induced-event target scenarios for engineering applications.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".