Evaluation of stochastic source models for the fault displacement hazard analyses using InSAR data: 2019 Ridgecrest earthquakes 
Bibliographic record
Abstract
The Ridgecrest earthquake sequence occurred in July 2019 in the Eastern California Mojave Desert. The sequence included two large events with moment magnitude (Mw) 6.4 and 7.1 and thousands of aftershocks above lower magnitude cutoff Mw 3.2. There was severe damage to critical infrastructures, such as major cracks and pavement failures along roads and highways, and disruption to utility service due to surface displacements. These infrastructures were important for post-disaster response and recovery operations. Fault displacement hazard analysis serves as an essential tool for characterizing the fault rupture hazard at sites of interest. Advancements in remote sensing approaches provide opportunities to study earthquake ground deformation hazard by modeling fault rupture process and surface displacements more reliably. In the present study, a stochastic source-based fault displacement hazard analysis is conducted. The methodology of the present study is based on statistical scaling relationships of source parameters (e.g., fault length, fault width, mean slip, and maximum slip), and heterogeneous earthquake slip distributions are synthesized to generate various stochastic source models. The method uses ground-truth and remotely sensed data, such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data to search for the source model with a satisfactory match with the available data. The present study differs from conventional fault displacement assessment practices in utilizing stochastic source modeling, instead of empirical predictive relationships. The methodology can be applied to all faulting mechanisms and consider multi-segment fault rupture. The use of Okada’s equations facilitates the calculation of three translational displacements and provides physically consistent fault displacement modeling at two locations for a given earthquake scenario, thereby allowing the estimation of the differential fault displacement at two sites.This study evaluates the effect of applying InSAR data to the stochastic source modeling approach for the 2019 Ridgecrest earthquakes which involved the complex interaction of multiple faults having different mechanisms. InSAR data provide useful information on the geometry and the extent of the rupture system and contributes to the hazard assessment efficiently. The capability of the method is evaluated in the framework of retrospective analyses by comparing the results with available data as well as existing studies and their associated model weighted errors. The performance of the models in earthquake source characterization is also analyzed considering InSAR data by the changes in model weighted errors for the cases of the surface displacement results with and without InSAR data. Based on the obtained results, InSAR data play an integral part in mainshock displacement analyses. Considering all the merits of applying ground truth and remote sensing data to the practice of stochastic source-based fault displacement hazard analysis, the obtained results characterize fault displacements more realistically and contribute to emergency management and disaster risk mitigation of critical facilities and infrastructure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".