Localized Afterslip at Geometrical Complexities Revealed by InSAR After the 2016 Central Italy Seismic Sequence
Bibliographic record
Abstract
Abstract The Mw 6.5 Norcia earthquake occurred on 30 October 2016, along the Mt Vettore fault (Central Apennines, Italy), it was the largest earthquake of the 2016–2017 seismic sequence that started 2 months earlier with the Mw 6.0 Amatrice earthquake (24 August). To detect potential slow slip during the sequence, we produced Interferometric Synthetic Aperture Radar (InSAR) time series using 12‐ to 6‐day repeat cycles of Sentinel‐1A/1B images. Time series indicates that centimeter‐scale surface displacements took place during the 10 weeks following the Norcia earthquake. Two areas of subsidence are detected: one in the Castelluccio basin (hanging wall of the Mt Vettore fault) and one in the southern extent of the Norcia earthquake surface rupture, near an inherited thrust. Poroelastic and viscoelastic models are unable to explain these displacements. In the Castelluccio basin, the displacement reaches 13.2 ± 1.4 mm in the ascending line of sight on 6 January 2017. South of the Norcia earthquake surface rupture (a zone between the Norcia and Amatrice earthquakes), the postseismic surface displacements affect a smaller area but reach 35.5 ± 1.7 mm in ascending line of sight by January 2017 and follow a logarithmic temporal decay consistent with postseismic afterslip. Our analysis suggests that the structurally complex area located south of the Norcia rupture (30 October) is characterized by a conditionally stable frictional regime. This geometrical and frictional barrier likely halted rupture propagation during the Amatrice (24 August) and Norcia (30 October) earthquakes at shallow depth (<3–4 km).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".