Repeated inflations, deflations, dike injections and eruptions since 2023 in the Svartsengi volcanic system, Reykjanes Peninsula, Iceland
Bibliographic record
Abstract
The Svartsengi volcanic system, SW-Iceland, started to show unrest in early 2020 with a series of inflation-deflation cycles. In late October 2023, it started to inflate at unprecedented rate of ~8 mm/day until it produced a ~15 km long dike intrusion on the 10 November 2023. The inflation resumed soon after and has been continuous since, only interrupted by deflation periods concurrent to additional dike injections and associated eruptions at the Sundhnúkur crater row. Geodetic modelling, assuming a deformation source within a uniform elastic half-space, infers pressure changes between about 3-6 km depth, with inflow causing volume increase rates of 3-8 m3/s of a crustal volume inferred to be a magma domain (complex of liquid magma, crystal mush and hot rock). Displacements mapped by GNSS (Global Navigation Satellite System) geodesy are used to derive volume change estimates of the magma domain in near real-time. Additional geodetic inversions also use extensive interferometric analysis of synthetic aperture (InSAR) satellite images. These results have been used to map the locations and volumes of the intruded dikes and the concurrent contraction volume of the magma domain. Since 27 October 2023, we infer continuous inflow of magma from depth into the magma domain, which appears to continue even during outflow into dikes and the extrusion of lava flows. We analyze all the inflation-deflation cycles, to better understand the mechanisms controlling the activity. The relationship between volume loss of the magma domain during these events and subsequent volume recharged to the domain (before the next event is triggered) has allowed success in forecasting diking/eruption onset in the medium and short term.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".