GEM Global Seismic Hazard Map v.2018.1
Bibliographic record
Abstract
The Global Earthquake Model (GEM) Seismic Hazard Map depicts the geographic distribution of the Peak Ground Acceleration (PGA) with a 10% probability of being exceeded in 50 years, equivalent to a return period of about 475 years, the internationally agreed reference for building safety regulation. The map was created by collating maps computed using national and regional probabilistic seismic hazard models developed by various institutions and projects, and by scientists working at the GEM Foundation. The OpenQuake engine, an open source seismic hazard and risk calculation software principally developed by the GEM Foundation, was used to calculate the hazard values. A smoothing methodology was applied to homogenise hazard values between adjacent models within buffer zones symmetrically distributed across the model borders. The map is based on a database of hazard models described using the OpenQuake engine and data format. Scientists working at the GEM Secretariat converted the models originally described using other formats. The models that were translated are: Alaska, Arabian Peninsula, Canada (translation completed by the Canada Geological Survey), China (translation completed by CEA in collaboration with GEM and the Swiss Seismological Service), Hawaii, India (translation completed by Nick Ackerley), Japan (translation completed by GEM in collaboration with NIED), New Zealand (translation completed by GNS Science), and United States of America. While translating these models various checks were performed to test the compatibility between the original results and the new results computed using the OpenQuake engine. Overall the differences between the original and translated model results are small, notwithstanding some diversity in modelling methodologies implemented in different hazard modelling software. The map and the underlying database of models are designed as a dynamic framework, capable to incorporate the most recent open models. The GEM Foundation plans to release future updates of this map on a regular basis as new information becomes available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.169 | 0.100 |
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".