Deciphering earthquake source observations to motivate questions for physics-based models of earthquake simulation
Bibliographic record
Abstract
Earthquake stress drop values estimated from ground-motion spectra commonly vary by several orders of magnitude, particularly for small earthquakes (~M < 3). Stress-drop values have been found to vary with faulting style, faulting type (intraplate, interplate), depth, and to exhibit differences between natural and induced earthquakes. Nevertheless, distinguishing uncertainties from real trends across data sets is challenging, in part due to the variation in methodological approaches and observational constraints. However, the proliferation of high-quality, dense seismic data in recent years has shown that at least some of the variability in stress drop values almost certainly reflects diversity in fault strength and geological conditions. Coupling well-constrained observations to a variety of modeling approaches will help uncover what controls earthquake rupture processes, but deconvolving observational constraints from real variation in rupture behavior is key.We present our stress drop estimates from data sets representing a wide range of fault loading conditions and geological environments, from interplate, intraslab and forearc subduction faults, to volcanic, intraplate, and human induced events. Stress-drop values range primarily between 1 – 100 MPa for events that meet the criteria for spectral-ratio analysis. We present correlations of low relative stress drop values in areas of high seismic attenuation indicative of lower rock strength, and a slight correlation with depth that corresponds to modeled deviatoric stress values. We also show one notable subset of induced events near active injection wells that exhibit stress drop values of ~0.1 MPa and have distinctive low-frequency content. Their spatial distribution, waveform, and source spectral characteristics suggest either slower rupture, lower stress drop values, or a combination of both, and may represent part of the transition between aseismic and seismic slip. We show using a Large-n array that while stress drop values are roughly constant (within 2 orders of magnitude), estimates can vary by roughly 25% when station coverage is limited to 15 stations or less with a maximum azimuthal gap of 90°. Our findings highlight the importance of using modeling approaches to explore relative influence of fault strength and methodological approaches in stress drop variation. In particular, models that incorporate both frictional and thermoelastic approaches may provide clues to the variability of conditions that can activate faults, both within stable sliding and seismic rupture conditions.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".