Some preliminary results of a worldwide seismicity estimation: a case study of seismic hazard evaluation in South America
Bibliographic record
Abstract
Global data have been widely used for seismicity and seismic hazard assessment by seismologists. In the present study we evaluate worldwide seismicity in terms of maps of maximum observed magnitude (Mmax), seismic moment (M 0 ) and seismic moment rate (M 0S). The data set used consists of a complete and homogeneous global catalogue of shallow (h £ 60 km) earthquakes of magnitude MS ³ 5.5 for the time period 1894-1992. In order to construct maps of seismicity and seismic hazard the parameters a and b derived from the magnitude-frequency relationship were estimated by both: a) the least squares, and b) the maximum likelihood, methods. The values of a and b were determined considering circles centered at each grid point 1° (of a mesh 1° ´1°) and of varying radius, which starts from 30 km and moves with a step of 10 km. Only a and b values which fulfill some predefined conditions were considered in the further procedure for evaluating the seismic hazard maps. The obtained worldwide M max distribution in general delineates the contours of the plate boundaries. The highest values of M max observed are along the circum-Pacific belt and in the Himalayan area. The subduction plate boundaries are characterized by the largest amount of M 0 , while areas of continental collision are next. The highest values of seismic moment rate (per 1 year and per equal area of 10 000 km 2) are found in the Southern Himalayas. The western coasts of U.S.A., Northwestern Canada and Alaska, the Indian Ocean and the eastern rift of Africa are characterized by high values of M 0 , while most of the Pacific subduction zones have lower values of seismic moment rate. Finally we analyzed the seismic hazard in South America comparing the predicted by the NUVEL1 model convergence slip rate between Nazca and South America plates with the average slip rate due to earthquakes. This consideration allows for distinguishing between zones of high and low coupling along the studied convergence plate boundary.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".