Refined Stochastic Source Modeling and Selection Method for Complex Fault Systems Considering Data Uncertainty: A Case Study of the 2019 Ridgecrest Earthquakes
Bibliographic record
Abstract
Abstract The geometrical complexity of earthquake ruptures underscores the importance of surface deformation hazard and risk assessments considering multisegment rupture scenarios. This study presents a stochastic source modeling applied to surface displacement hazard analysis of a complex fault system where multiple, overlapping segments interact. A methodology is developed to account for the multisegment rupture feature of a complex fault system and the uncertainty of surface deformation data. The method uses geological field observations and remotely sensed data, such as Global Positioning System and Interferometric Synthetic Aperture Radar data. The stochastic source modeling characterizes the fault displacement hazard based on statistical scaling relationships and analytical equations for calculating the elastic deformation due to a fault rupture. The new method overcomes the limitation of the current stochastic source modeling approach, which has been previously applied to simple scenarios, in terms of defining multiple asperity zones for the ruptured system based on the released seismic moment scenario. The method is applied to the 2019 Ridgecrest earthquake sequence of moment magnitude (Mw) 7.1 and 6.4, for which the earthquake rupture geometry is complex with near-perpendicular segments. The results of the stochastic source characterization of the 2019 Ridgecrest earthquakes indicate that considering different released seismic moments and different slip syntheses for individual segments significantly enhances the performance of the method and the match of the corresponding surface displacements to the observed data. Moreover, the number of qualified source models based on defined criteria increases due to the increasing uncertainties of data. This means that higher uncertainty of data leads to less restrictive constraints on source models.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".