Static Ground Displacement for an Induced Earthquake Recorded on Broadband Seismometers
Bibliographic record
Abstract
ABSTRACT Using geodetic methods, significant static ground deformation has been observed for many large natural earthquakes. Some of the largest earthquakes induced by hydraulic-fracturing operations have been observed in the Western Canada Sedimentary Basin; however, because of the size and depths of these events, the associated static ground deformations have not yet been observed using traditional geodetic techniques. A seismic processing technique, developed for small volcano-seismic events, has the potential to resolve micrometer-scale static displacements using broadband seismic data. In this study, we test this processing method using vertical component broadband recordings of an Mw 4.1 event acquired at four nearby broadband seismometers. Estimated static displacements at the four stations are compared with the theoretical surface displacement field for a dislocation on a finite rectangular source within a homogeneous, elastic half-space. The theoretical displacements have the same polarities as the measured displacements across the seismic network and have similar amplitudes for three of the four stations. However, one station yielded unstable results, which shows that care must be taken when using this method. These results suggest that this processing method has potential for obtaining surface deformation for small to moderate-sized earthquakes using broadband data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".