Three‐Dimensional Mapping of Mt. Ruapehu Volcano, New Zealand, From Aeromagnetic Data Inversion and Hyperspectral Imaging
Bibliographic record
Abstract
Abstract Combining 3‐D inversion of high‐resolution aeromagnetic data with airborne hyperspectral imaging creates a new method to map buried structure and hydrothermal alteration, applied to Mt. Ruapehu volcano, New Zealand. Hyperspectral imaging is sensitive to surface mineralogy including alteration minerals, while magnetic vector inversion reveals the volumetric distribution of magnetic susceptibility from which we interpret buried geology. Probability assessment from multiple model regularizations provides an important model uncertainty estimate. At Ruapehu, hyperspectral imaging highlights two main regions of surface alteration: the Pinnacle Ridge and the southeast flanks. The magnetic model of Pinnacle Ridge shows that alteration seen at surface continues to depth, but strongly magnetic, unaltered dikes form the core of the ridge. On the southeast flanks, the magnetic model also shows alteration imaged on the surface continues to depth; however, a previously unknown, magnetized sill intrudes part of the flank. Several smaller demagnetized regions are modeled, unlike at neighboring Mt. Tongariro where the hydrothermal system created a large demagnetized core. We propose that these differences relate to spatially focused (Ruapehu) vs distributed (Tongariro) eruption vents, the degree of faulting of the edifice and its glaciation history. Lava‐ice interaction produces fine‐grained lavas with measured magnetic susceptibilities similar to some moderately altered lavas, illustrating that care must be taken in the interpretation of magnetic data in the absence of geological information. The combination of hyperspectral imaging and aeromagnetic data inversion distinguishes shallow surface weathering from deeper‐seated hydrothermally altered rock masses, with implications for the magnitude and probability of collapse events.
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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 | 0.000 |
| 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".