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Record W4416719561 · doi:10.1093/gji/ggaf482

Muography for structural characterization of volcanoes: a case study at Mount Unzen, Japan

2025· article· en· W4416719561 on OpenAlexaff
L. Oláh, László Tercsi, D. Varga, Seiki Kubo, Junichi Akanuma, Masafumi Kaneko, Hisashi Watanabe

Bibliographic record

VenueGeophysical Journal International · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsFrontiers Foundation
FundersNemzeti Kutatási Fejlesztési és Innovációs HivatalMinistry of Land, Infrastructure, Transport and Tourism
KeywordsLavaVolcanoMountFlankDebrisPyroclastic rockPaleomagnetismFracture (geology)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.241
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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