Three dimensional Gravity Local Inversion Across the Area Struck by the 2016–2017 Seismic Events in Central Italy
Bibliographic record
Abstract
Abstract In this work, the crustal volume struck by the 2016–2017 seismic sequence in Central and Northern Apennines is investigated using constrained 3‐D inversion of the gravity anomalies. In order to focus on the area comprising the two mainshocks of the sequence, we perform a regional field removal on the data as a preprocessing step. This residual data set is then inverted into a 3‐D density contrast model. We perform a series of inversions to test different geological scenarios and parameters, with increasing complexity in the reference geometries. We first test a model comprising turbidites, carbonates and evaporites, and basement and then introduce a low‐density layer at the top of the basement. The geometries are obtained in agreement with the available geological and geophysical information in the area. We found that the 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 consisting of low‐grade metamorphic rocks (phyllites). Finally, we compare our modeling results to the spatial distribution at depth of major seismic events between August and November 2016. These events appear to be concentrated within the denser units at both shallow and deep locations, while the deeper events often occur in a region of major density contrast corresponding to the top of the basement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".