Revisiting Paleoearthquakes with Numerical Modeling: A Case Study of the 1679 Sanhe–Pinggu Earthquake
Bibliographic record
Abstract
Abstract Investigating a paleoearthquake in a region can be used to study the seismicity of fault zones, and provides guidance for earthquake prevention and disaster reduction in nearby cities. However, the short of reliable records brings challenges to the assessment of the paleoearthquake disasters. With the development of computational seismology, we can study paleoearthquakes using numerical modeling based on limited data, to provide a reference for understanding the physical laws of historical earthquakes and earthquake relief in present society. Taking the 1679 M 8.0 Sanhe–Pinggu earthquake as an example, we built a dynamic model with good consistency between the surface slip and historical records, calculated the strong ground motion based on it, and obtained the intensity distribution that was consistent with the previous investigation. We found that the heterogeneous dip-slip distribution caused by the fault geometry change may be the reason that the fault scarp only remains about 10 km. In addition, the intensity of Tongzhou area in this earthquake may be as high as XI. In the future, it may be necessary to pay attention to strengthening earthquake prevention and disaster reduction in this area. Then, we estimated the number of deaths in the study area at that time, and the mathematical expectation was of about 74,968. During the systematic retrospective study of paleoearthquakes, as shown in this article, we can gain new understandings of the rupture process of paleoearthquakes and evaluate earthquake disasters more accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".