Seismic Hazard Analyses From Geologic and Geomorphic Data: Current and Future Challenges
Bibliographic record
Abstract
Abstract The loss of life and economic consequences caused by several recent earthquakes demonstrate the importance of developing seismically safe building codes. The quantification of seismic hazard, which describes the likelihood of earthquake‐induced ground shaking at a site for a specific time period, is a key component of a building code, as it helps ensure that structures are designed to withstand the ground shaking caused by a potential earthquake. Geologic or geomorphic data represent important inputs to the most common seismic hazard model (probabilistic seismic hazard analyses, or PSHAs), as they can characterize the magnitudes, locations, and types of earthquakes that occur over long intervals (thousands of years). However, several recent earthquakes and a growing body of work challenge many of our previous assumptions about the characteristics of active faults and their rupture behavior, and these complexities can be challenging to accurately represent in PSHA. Here, we discuss several of the outstanding challenges surrounding geologic and geomorphic data sets frequently used in PSHA. The topics we discuss include how to utilize paleoseismic records in fault slip rate estimates, understanding and modeling earthquake recurrence and fault complexity, the development and use of fault‐scaling relationships, and characterizing enigmatic faults using topography. Making headway in these areas will likely require advancements in our understanding of the fundamental science behind processes such as fault triggering, complex rupture, earthquake clustering, and fault scaling. Progress in these topics will be important if we wish to accurately capture earthquake behavior in a variety of settings using PSHA in the future.
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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 | 0.035 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".