Muography for structural characterization of volcanoes: a case study at Mount Unzen, Japan
Bibliographic record
Abstract
SUMMARY Destabilization of volcanic edifices can generate debris avalanches with catastrophic impacts on their environment. We present the first high-resolution muography of Mount Unzen, Japan, conducted to characterize the structure of lava lobes formed on the volcano’s summit and flank during the 1990–1995 eruption. A multi-wire-proportional-chamber-based muon tracking system was operated for 203 d. The obtained high-resolution muographic image shows the internal density structure of Mount Unzen with a spatial resolution of 12 m. Mean densities were respectively measured as 2470 and 2290 kg m−3 for the base rock and a fracture zone, and both were consistent with the results of prior drilling and sampling experiments. The mean density of lava lobes was measured significantly lower value of 1570 kg m−3, indicating post-eruptive structural weakening. A comparison between the time-series of muographically measured density-lengths and daily precipitation records suggest that rainfall-induced gravitational destabilization did not occur during the observational period. This work demonstrates that long-term (multi-year) muon monitoring of the lava lobes can provide valuable complementary information for volcanic stability assessments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".