Fractal Study of the 1997–2017 Italian Seismic Sequences: A Joint Analysis of Seismological Data and DInSAR Measurements
Bibliographic record
Abstract
During the last 20 years (1997 to 2017), four seismic sequences with Mw ≥ 5.5 mainshocks nucleated along the Central and Northern Apennines chain (Italy), causing casualties and damage: the 1997 Colfiorito, the 2009 L’Aquila, the 2012 Emilia, and the most recent 2016–2017 Central Italy seismic sequences. In this work, we perform a novel joint analysis of seismological and remote-sensing data to achieve new insights into the faulting process evolution during the considered seismic sequences. To this aim, we study these seismic sequences by exploiting the available seismological data and by applying fractals theory to them. In particular, we characterize the different behavior of compressional and extensional seismic sequences by examining the temporal evolution of the fractal dimension values. In addition, we compare the Differential Synthetic Aperture Radar Interferometry (DInSAR) displacement maps relevant to the considered seismic events (already published in our past papers) and the performed spatial and temporal seismological analyses, in order to emphasize some significant aspects of the different faulting processes active during these Italian seismic sequences. The analysis of the fractal dimension values shows that over time extensional seismic sequences are spatially distributed within a volume, whereas compressional ones are aligned along a preferential surface. These spatio-temporal patterns are confirmed by: (1) the spatial distribution of hypocenters for the events that occurred between the mainshock and the post-seismic synthetic aperture radar (SAR) acquisition; (2) the spatial extension of coseismic DInSAR ground-deformation patterns. The proposed seismic and ground-deformation analyses can thus typify different geodynamic contexts in Italy, providing a distinct image of articulated faulting processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".