Different Tectonics, Same Approach: Estimation of source parameters using the Coda Calibration Tool (CCT).
Bibliographic record
Abstract
It is well known that the use of different methods (e.g., spectral fitting, empirical Green’s functions) for compiling catalogs of source parameters (e.g., seismic moment, stress drop) can results in significant inconsistencies (Baltay et al., 2022). In this study, we present the application of coda-wave source parameters estimation by the Coda Calibration Tool (CCT) to different tectonic settings for closer analysis of the regional variations.  CCT implements the empirical methodology outlined in Mayeda et al., (2003), which provides stable source spectra and source parameters even for events recorded by sparse local and regional seismic networks (e.g., Morasca et al., 2022). The main strength of the method is the use of narrowband coda waves measurements, which show low sensitivity to source and path heterogeneity. Additionally, we use independent ground-truth (GT) reference spectra for which apparent stresses are independently calculated through the coda spectral ratio (Mayeda et al., 2007), to break the path and site trade-off.  The use of GT spectra eliminates the need to assume source scaling for the region, reducing the impact of a-priori model assumptions on the interpretation of scaling laws of source parameters and their variability. The CCT is a freely available Java-based code (https://github.com/LLNL/coda-calibration-tool) that significantly reduces the coda calibration effort and provides calibration parameters for future use in the same region for routine processing.Recently, several studies applied CCT in very different tectonic contexts, including (1) earthquakes in tectonically active regions (e.g., central Italy, Puerto Rico, southern California, Utah); (2) induced earthquakes in southern Kansas and northern Oklahoma; and (3) moderate-sized earthquakes in stable continental regions such as in Eastern Canada and the United Kingdom. There is excellent agreement between coda-derived Mw in all regions and available Mw from waveform modelling. In some cases, such as central Italy and Ridgecrest, the validation process also involved the comparison with estimates from different empirical techniques, such as spectral decomposition approaches applied to data sets sharing common events with CCT. Overall, there is a general consistency in the scaling laws obtained for different source parameters (e.g., seismic moment, corner frequency, radiated energy and apparent stress), with earthquakes in the UK and Canada having similar and higher apparent stresses than Utah, central Italy, Puerto Rico and southern California, while the induced regions are characterized by the lowest values. In conclusion, the application of a consistent methodological framework and the robustness demonstrated by the results of the seismic coda analysis allow comparison of source scaling relationships for different tectonic settings over a wide range of magnitudes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".