Mass transfer processes in a post eruption hydrothermal system: parameterisation of microgravity changes at Te Maari craters, New Zealand
Bibliographic record
Abstract
Abstract Fluid transfer and ground deformation at hydrothermal systems occur both as a precursor to, or as a result of, an eruption. Typically studies focus on pre-eruption changes to understand the likelihood of unrest leading to eruption; however, monitoring post-eruption changes is important for tracking the return of the system towards background activity. Here we describe processes occurring in a hydrothermal system following the 2012 eruption of Upper Te Maari crater on Mt Tongariro, New Zealand, from observations of microgravity change and deformation. Our aim is to assess the post-eruption recovery of the system, to provide a baseline for long-term monitoring. Residual microgravity anomalies of up to 92 ± 11 μGal per year are accompanied by up to 0.037 ± 0.01 m subsidence. We model microgravity changes using analytic solutions to determine the most likely geometry and source location. A multiobjective inversion tests whether the gravity change models are consistent with the observed deformation. We conclude that the source of subsidence is separate from the location of mass addition. From this unusual combination of observations, we develop a conceptual model of fluid transfer within a condensate layer, occurring in response to eruption-driven pressure changes. We find that depressurisation drives the evacuation of pore fluid, either exiting the system completely as vapour through newly created vents and fumaroles, or migrating to shallower levels where it accumulates in empty pore space, resulting in positive gravity changes. Evacuated pores then collapse, causing subsidence. In addition we find that significant mass addition occurs from influx of meteoric fluids through the fractured hydrothermal seal. Long-term combined microgravity and deformation monitoring will allow us to track the resealing and re-pressurisation of the hydrothermal system and assess what hazard it presents to thousands of hikers who annually traverse the volcano, within 2 km of the eruption site.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".