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Record W3212866550 · doi:10.1093/gji/ggab404

An experimental study of volcanic tremor driven by magma wagging

2021· article· en· W3212866550 on OpenAlexfundno aff
vahid dehghanniri, M. Jellinek

Bibliographic record

VenueGeophysical Journal International · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsGeologyVolcanoMagmaAnnulus (botany)AmplitudeMechanicsGeophysicsSeismologyMagma chamberPetrologyPhysics

Abstract

fetched live from OpenAlex

SUMMARY Protracted episodes of 0.5–7 Hz pre-eruptive volcanic tremor (PVT) are common at active stratovolcanoes. Reliable links to processes related to magma movement consequently enable a potential to use properties of PVT as diagnostic eruptive precursors. A challenging feature of PVT is that generic spectral and amplitude properties of the signal evolve similarly, independent of widely varying volcano structures and conduit geometries on which most physical models rely. The ‘magma wagging’ model introduced in Jellinek & Bercovici (2011) and extended by Bercovici et al. (2013), Liao et al. and Liao & Bercovici (2018) makes progress because it depends on magma dynamics that are only weakly sensitive to volcano architecture: The flow of gas through a permeable foamy annulus of gas bubbles excites, modulates and maintains a wagging oscillation of a central magma column rising in an erupting conduit. ‘Magma wagging’ and resulting PVT are driven through an energy transfer from a ‘Bernoulli mode’ related to azimuthal variations in annular gas flow speeds. Consistent with observations, spectral and amplitude properties of PVT are predicted to evolve before an eruption as the width of the annulus decreases with increased gas fluxes. To confirm this critical Bernoulli-to-wagging energy transfer we use extensive experiments and restricted numerical simulations on wagging oscillations excited on analogue viscoelastic columns by annular air flows. We also explore sensitivities of the spatial and temporal characters of wagging to asymmetric annular air flows that are intractable in the existing magma wagging model and expected to occur in nature with spatial variations in annulus permeability. From high-resolution time-series of linear and orbital displacements of analogue column tops and time-series of axial deflections and accelerations of the column centre line, we characterize the excitation, evolution, and steady-state oscillations in unprecedented detail over a broad range of conditions. We show that the Bernoulli mode corresponds to the timescale for the buildup of axial elastic bending stresses in response to pressure variations related to air flows over the heights of columns. We identify three distinct wagging modes: (i) rotational (cf. Liao et al. 2018); (ii) mixed-mode and (iii) chaotic. Rotational modes are favoured for symmetric, high intensity forcing and a maximal delivery of mechanical energy to the fundamental magma wagging mode. Mixed-mode oscillations regimes are favoured for a symmetric, intermediate intensity forcing. Chaotic modes, involving the least efficient delivery of energy to the fundamental mode, occur for asymmetric forcing and where the intensity of imposed airflow is low. Numerical simulations also show that where forcing frequencies are comparable to a natural mode of free oscillation, power delivered by peripheral air flows is concentrated at the lowest frequency fundamental mode generally and spread among higher frequency natural modes where air pressure and column elastic forces are comparable. Our combined experimental and numerical results make qualitative predictions for the evolution of the character of volcanic tremor and its expression in seismic or infrasound arrays during natural events that is testable in field-based studies of PVT and syn-eruptive volcanic tremor.

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 score0.252
Threshold uncertainty score0.997

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)0.0040.000

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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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