Improved Earthquake Clustering Using a Density-Adaptive DBSCAN Algorithm: An Example from Iran
Bibliographic record
Abstract
Abstract DBSCAN is a widely used unsupervised machine learning algorithm for clustering and spatial data analysis. However, the accuracy of the algorithm is highly dependent on the selection of its hyperparameters, minimum samples (Smin), the minimum number of points required to form a cluster, and ϵ, the maximum distance between points. In this study, we propose a modification to the DBSCAN algorithm by introducing an event density map replacing Smin and ε. Through this method, we decrease the number of hyperparameters from two to one, N which represents the number of cells in the event density map, simplifying, and speeding up optimization. As a result, the optimization of the algorithm will be improved as the sole factor to consider is the size of each cell. In addition, the utilization of dynamic Smin will provide more effective clustering because it is better suited to regions that have a variable earthquake density. We used the Iranian earthquake catalog for testing the algorithm, and we compared the outcomes to the Mirzaei et al. (1998) model as a standard for evaluation. Because this algorithm allows for density contrasts between clusters, it can be a good indicator when studying the zonation of a new area. The findings were more consistent than those of other methods and may offer additional insight into the seismotectonic of Iran. Other than earthquake studies, this algorithm can be applied in multiple fields of science and engineering for clustering datasets with variable-density clusters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".