Intelligent Spatial-Clustering of Seismicity in the Vicinity of the Hellenic Seismic Arc
Bibliographic record
Abstract
This research paper discusses possible seismic cluster formation and evolution in the vicinity of the Hellenic seismic arc and proposes a graphical user-interface monitoring and analysis tool based on various commercial and self-developed clustering algorithms for cluster discrimination, evolution and visualization. Self-developed algorithms enable the processing of both a) all recorder earthquakes and b) main seismic events alone, excluding foreshocks and aftershocks, by incorporating dynamic filters in space and time. The user can also import external formulae for the computation of the total earthquake preparation time, aftershocks duration and radius of the sphere of earthquake preparation region, and can also select specific regions of interest as well as the entire seismic map. The seismic imaging tool also addresses the concept of topical seismic cluster formation. Seismological maps indicate the presence of several seismic swarms forming within the region of the Hellenic arc, which appear to be either distinct or interacting together in groups of two or more. The identification of the number of possibly individual seismic clusters in a seismological area is a very challenging task by itself, which becomes even more complicated when investigating their outer boundaries especially in the case of multiple interacting clusters. The proposed imaging tool incorporates clustering algorithms that allow the user to apply various techniques for cluster identification, such as density based functions, gradient descent, centre of gravity, evolutionary allocation, and even import expert knowledge regarding the number of individual seismic clusters present.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".