Earthquake Magnitudes from Dynamic Strain
Bibliographic record
Abstract
ABSTRACT Dynamic strains have never played a role in determining local earthquake magnitudes, which are routinely set by displacement waveforms from seismic instrumentation (e.g., ML). We present a magnitude scale for local earthquakes based on broadband dynamic strain waveforms. This scale is derived from the peak root-mean-squared strains (A) in 4589 records of dynamic strain associated with 365 crustal earthquakes and 77 borehole strainmeters along the Pacific-North American plate boundary on the west coast of the United States and Canada. In this data set, catalog moment magnitudes range from 3.5≤Mw≤7.2, and hypocentral distances range from 6≤R≤500 km. The 1D representation of geometrical spreading and attenuation of A common to all strain data is logA0(R)=−0.00072R−1.45log(R). After correcting for instrument gain, site terms, and event terms, the magnitude scale, MDS=logA−logA0(R)−log(3×10−9), scales as ≈0.92Mw with a residual standard deviation of 0.19. This close association with Mw holds for events east of the −124° meridian; west of this boundary, however, a constant correction of 0.41 is needed to adjust for additional along-path attenuation effects. As a check on the accuracy of this magnitude scale, we apply it to dynamic strain records from three strainmeters located in the near field of the 2019 M 6.4 and 7.1 Ridgecrest earthquakes. Results from these six records are in agreement to within 0.5 magnitude units, and five out of six records are in agreement to within 0.34 units.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".