4D imaging of the volcano feeding system beneath the urban area of the Campi Flegrei caldera
Bibliographic record
Abstract
This paper describes an approach to analyze ground deformation data collected by InSAR (Interferometric Synthetic Aperture Radar) imaging the volcano feeding system (VFS) beneath a caldera. The approach is applied to the Campi Flegrei caldera in southern Italy, a densely populated area at high risk for volcanic eruption . The method is a 4D tomographic inversion that considers a combination of 3D pressure sources and dislocations (strike-slip, dip-slip and tensile) acting simultaneously. This is in contrast to traditional methods that assume a priori geometries and type for the volcanic source. Another novelty is that we carry out a time-series analysis of multifrequency InSAR displacement data. The analysis of these multiplatform and multifrequency InSAR data from 2011 to 2022 reveals an inflating source at a depth of 3–4 km that is interpreted as a pressurized magmatic intrusion. The source broadens and migrates laterally over time, with a possible new magmatic pulse arriving in 2018–2020. The model also identifies a shallow region (at 400 m depth) that may be feeding fumaroles in the area. The analysis also reveals a zone of weakness (dip-slip) that could influence the path of rising magma . This method provides a more detailed dynamic 4 - dimensional image of the VFS than previously possible and could be used to improve hazard assessments in active volcanic areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".