3D gravity inversion across the area struck by the 2016-2017 seismic events in Central and Northern Apennines, Italy
Bibliographic record
Abstract
In this work, the crustal volume struck by the 2016-2017 seismic sequence in Central and Northern Apennines is investigated using constrained 3D inversion of the Bouguer anomaly. After a preliminary regional field removal the residual dataset is then inverted into a 3D density contrast model. With an increasing complexity in the reference geometries, we test different geological scenarios and software settings. Geometries used in the reference models were retrieved from the available geological and geophysical information in the area. Starting with a reference model encompassing turbidites, carbonates and evaporites, and basement we finally test the effects of a low-density layer at the top of the basement. The retrieved density distribution with depth is compatible with previous models. Moreover, results support the hypothesis based on borehole evidence, of a low-density upper basement across the entire area, possibly phyllitic in composition. Comparison of the resulting models with the spatial distribution at depth of M>3 seismic events between August and November 2016, allows to locate volumes with the higher concentration of seismic events. Both at shallow and deep locations, the majority of the events enucleated in volumes relatively denser while deeper events occur in a region of major density change corresponding to the top of the basement.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".