Stochastic Source Modelling of the 2002 Denali Earthquake for Fault Displacement Hazard Assessment
Bibliographic record
Abstract
The Denali Fault earthquake was one of the largest strike-slip earthquakes with significant surface ruptures that occurred in 2002 in Alaska, United States. Probabilistic fault displacement hazard assessment (PFDHA) plays an important role in post-earthquake disaster management. This is because critical facilities and infrastructures in the vicinity of active faults are prone to major damage, leading to suspension of service due to fault displacement. Hence, in the present study, a PFDHA due to the Denali earthquake is conducted using a new methodology of stochastic source-based fault displacement hazard analysis. In this method, the surface rupture can be evaluated by applying Okada equations to simulated earthquake source models. The main differences between the methodology of the present study with conventional fault displacement assessment practices are to utilize the stochastic source models instead of the empirical predictive relationship of surface fault displacement and to calculate the distribution for surface fault displacement at a site of interest using the Okada equations. The new methodology is more versatile than the existing methods in several characteristics. First, it is applicable to all faulting mechanisms (e.g., strike-slip, normal, and reverse) by specifying different rake angles of the ruptured fault. Second, it has the ability to consider multi-segment fault rupture. Third, the calculation of three translational displacements by the Okada equations for a given location is available. Lastly, it provides physically consistent fault displacement modelling at two locations for a given earthquake scenario, allowing estimating of the differential fault displacement at two sites. Then the capability of the method is evaluated by applying it to the historical case of the 2002 Denali Fault earthquake. The satisfactory match of the modelled fault displacement and the observations, such as surface offset, Global Positioning System (GPS), and Interferometric Synthetic Aperture Radar (InSAR) data, is achieved based on calculating a performance metric. Therefore, more realistic ground deformation assessments can be carried out. Importantly, the obtained results significantly contribute to the hazard in earthquake-prone areas and reduce potential fatality and casualty risks as well as the post-earthquake damage repair costs of the built environment.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".