To what extent do slip rates contribute to the seismic activity of faults?
Bibliographic record
Abstract
Crustal deformation comprises a combination of seismic energy release occurring by earthquakes and aseismic unloading through creeping or frictional sliding. Efficient segregation of the seismic component attributed to faults is critical in evaluating Seismic Activity Rate (SAR) or Magnitude Frequency Distribution (MFD) of earthquakes in fault-based hazard assessment. The MFDs are routinely calculated by utilizing fault geometry and slip rate, evaluated from geodetic (or geological) data. The slip rates as an integrated representation of elastic and anelastic loadings overestimate the MFDs since earthquakes release only the elastic strain. To work around this problem, the seismic/geodetic moment-rate ratio defined as Seismic Coupling Coefficient (SCC) is incorporated in this study to account for the seismic portion of the total moment rate to calculate MFDs. The parameter has been studied for different tectonic regions worldwide, including the USA, Canada, Iran, Greece, and Italy. We modify the Moment Budget (MB) algorithm introduced by Pace et al. (2016) to weight the total moment rate corresponding to the maximum magnitude (Mmax) generated by the modeled faults by incorporating the SCC. An updated mean recurrence time (Tmean) for the Mmax and its corresponding uncertainty is computed when SCC is incorporated in the calculation. Then, the seismic moment, the SCC weighted Tmean, and its uncertainty are utilized to compute MFDs by balancing the modeled seismic moment rates (by Doubly Truncated Gutenberg-Richter (DTGR) or Characteristic Gaussian (CHG)) and the SCC weighted moment rates. This process is implemented by the Activity Rates (AR) tool of FiSH codes. Fault data of 89 fault segments in Zagros, Iran, are introduced into the algorithm to compute the SCC incorporated MFDs. The acquired fault-based hazard maps are in harmony with the history of seismicity and tectonics of the region, while the total moment rates exaggerate the calculated hazard. Future work involves implementing the processing algorithm on hazard assessment in the Gulf of Guinea. This research contributes to the FCT-funded SHAZAM (Ref. PTDC/CTA-GEO/31475/2017), IDL (Ref. FCT/UIDB/50019/2020), and SIGHT (Ref. PTDC/CTA-GEF/30264/2017) projects. It also uses computational resources provided by C4G (Collaboratory for Geosciences) (Ref. PINFRA/22151/2016).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".