Spontaneous dynamic rupture modeling of 2017 M 5.4 Pohang, South Korea, earthquake, using the slip-weakening friction law
Bibliographic record
Abstract
An M 5.4 earthquake occurred in the southeastern part of the Korean Peninsula in 2017. It is an oblique thrust event that occurred at a relatively shallow depth (~ 5 km) although it did not create coseismic surface rupture. A coseismic slip model was successfully obtained by inverting the ground displacement field extracted by the InSAR data (Song and Lee, 2019). In this study, we performed spontaneous dynamic rupture modeling using the slip weakening friction law. The static stress drop distribution obtained by the coseismic slip model was used as an input stress field. We adopted high performance computing (HPC) using the parallelized dynamic rupture modeling code (SORD, Support Operator Rupture Dynamics). Although our target event is moderate-sized one, we can successfully produce a spontaneous dynamic rupture model using a relatively small initial nucleation patch (radius ~ 1 km) with a relatively small slip weakening distance (~ 5 cm). Our preliminary results show that the rupture creates an asperity near the initial nucleation zone with approximately 4 MPa stress drop, then propagates obliquely upward both in the northeast and southwest directions. Although we assumed a single planar fault plane in our current rupture modeling, it seems worthwhile to dynamically model the rupture process, including complex fault geometry in following studies. Dynamic rupture modeling for a natural earthquake provides an opportunity to understand the dynamic rupture characteristics of the earthquake, including both stress drop and fracture energy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".