Method for Determination of Focal Mechanisms of Magnitude 2.5–4.0 Earthquakes Recorded by a Sparse Regional Seismic Network
Bibliographic record
Abstract
ABSTRACT Focal mechanisms of earthquakes with magnitudes Mw 4.0 and less recorded by a sparse seismic network are usually poorly constrained due to the lack of an appropriate method applicable to finding these parameters with a sparse set of observations. We present a new method that can accurately determine focal mechanisms of earthquakes with Mw (3.70–3.04) using data from a few regional seismic stations. We filter the observed seismograms as well as synthetic seismograms through a frequency band of 1.5–2.5 Hz, which has a good signal-to-noise ratio for small earthquakes of the magnitudes with which we are working. The waveforms are processed to their envelopes to make the waveforms relatively simple for modeling. To find the optimal focal mechanism for an event, a nonlinear moment tensor inversion in addition to a coarse grid search over the possible dip, rake, and strike angles at a fixed value of focal depth and a fixed value of scalar moment is performed. We tested the method on 18 aftershocks of Mw (3.70–2.60) of the 2011 Mw 5.7 Mineral, Virginia, earthquake and on five aftershocks of Mw (3.62–2.63) of the 2013 Mw 4.5 Ladysmith, Quebec, earthquake. Our method obtains accurate focal mechanisms for 16 out of the 21 events that have previously reported focal mechanisms. Tests of our method for different crustal models show that event focal mechanism determinations vary with an average Kagan angle of 30° with the different crustal models. This means that the event focal mechanism determinations are only somewhat sensitive to the uncertainties in the crustal models tested. This study confirms that our method of modeling envelopes of seismic waveforms can be used to extract accurate focal mechanisms of earthquakes with short-time functions (Mw<4.0) using at least three regional seismic network stations at epicentral distances of 60–350 km.
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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) | 0.000 | 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 | 0.001 |
| 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".