Equivalent Point-Source Modeling of Moderate-to-Large Magnitude Earthquakes and Associated Ground-Motion Saturation Effects
Bibliographic record
Abstract
We modeled the source and attenuation attributes of well-recorded M6� earthquakes based on the equivalent point-source approach, with the goal of determin- ing how to treat ground-motion saturation effects within this context. We consider ground motions as originating from an equivalent point source and mimic finite-fault effects by treating the motion as emanating from a virtual point (not a real point on the fault rupture), such that ground motions are correctly predicted at close distances. This is achieved by using an effective distance metric R �� D 2 � h 2 � 0:5 , in which Drup is the closest distance to the rupture and h is a pseudodepth term that accounts for sat- uration effects. We found that the distance-saturation effect is magnitude dependent, extending to further distances with increasing magnitude. For earthquakes of M ≥6, we model the saturation term as logh �� −1:72 � 0:43 M with a standard deviation of 0.19 in log10 units, based on the values obtained from the study earthquakes. The apparent source spectra of most M6� earthquakes can be modeled using a simple Brune point-source model. For a few of the M6� earthquakes, notably those in California, we observed a spectral sag at intermediate frequencies. For such earth- quakes, a two-corner point-source model provides a better match than the Brune model. We conclude that an equivalent point-source model based on the effective distance concept can successfully predict the average ground motions from M6� earthquakes over a wide distance range, including close distances (<20 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".