Near real-time simulations of global CMT earthquakes
Bibliographic record
Abstract
We have developed a near real-time system for the simulation of global earthquakes.Prompted by a trigger from the Global Centroid Moment Tensor (CMT) Project, the system automatically calculates normal-mode synthetic seismograms for the Preliminary Reference Earth Model, and spectral-element synthetic seismograms for 3-D mantle model S362ANI in combination with crustal model Crust2.0.The 1-D and 3-D synthetics for more than 1800 seismographic stations operated by members of the international Federation of Digital Seismograph Networks are made available via the internet (global.shakemovie.princeton.edu) and the Incorporated Research Institutions for Seismology Data Management Center (IRIS; iris.edu).The record length of the synthetics is 100 min for CMT events with magnitudes less than 7.5, capturing R1 and G1 at all epicentral distances, and 200 min for CMT events with magnitudes equal to or greater than 7.5, capturing R2 and G2.The mode simulations are accurate at periods of 8 s and longer, whereas the spectral-element simulations are accurate between periods from 17 to 500 s.The spectral-element software incorporates a number of recent improvements, for example, the mesh honours the Moho as a first-order discontinuity underneath the oceans and continents, and the performance of the solver is enhanced by reducing processor cache misses and optimizing matrix-matrix multiplication.In addition to synthetic seismograms, the system produces a number of earthquake animations, as well as various record sections comparing simulated and observed seismograms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".