Source-Scaling Comparison and Validation for Ridgecrest, California: Radiated Energy, Apparent Stress, and Mw Using the Coda Calibration Tool (2.6 < Mw < 7.1)
Bibliographic record
Abstract
The determination of accurate apparent stress, radiated energy, corner frequency, and their scaling with magnitude remains one of the most difficult seismological endeavors because of complicated 3D Earth structure, complex rupture, and limited broadband recordings. This study focuses on a comparison of four separate state‐of‐the‐art methods that aim to compare and contrast common events using the well‐recorded 2019 Ridgecrest, California, sequence, which was motivated in large part by the U.S. Geological Survey (USGS)/Southern California Earthquake Center (SCEC) Community Stress‐Drop Validation Study group (Baltay et al., 2024). For this study, we calibrated the Ridgecrest and surrounding region using the Coda Calibration Tool (CCT) and compared them against recent generalized inversion technique (GIT) results of Bindi et al. (2021) () and two other state‐of‐the‐art methods for moderate‐sized events in the sequence (). We find excellent agreement between the GIT and coda‐derived results over a broad range of magnitudes, and for moderate‐size events, we find equally good agreement with source estimates from finite‐fault inversion method based on Dreger (1997) and a direct S‐wave spectral method by Ji et al. (2024). As found in a recent comparative study in central Italy by Morasca et al. (2022) (), we find that CCT and GIT results are in excellent agreement for events ranging between , and relative, weak‐motion site terms are also in agreement. Although both approaches observe a modest increase in apparent stress with depth, the overall trend in apparent stress increasing with magnitude is supported by our findings. Finally, upon comparison with other regions, we find that the absolute apparent stress values from Ridgecrest are comparable to central Italy but significantly lower than both eastern Canada and the United Kingdom.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".