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Record W1748649839

Acoustic emissions during experimental fragmentation of volcanic rocks

2010· article· en· W1748649839 on OpenAlexaff
Sandra Karl, Yan Lavallée, Bettina Scheu, Rosanna Smith, A. Flaws, M. A. Alatorre‐Ibargüengoitia, Philip Benson, Alexandra Arciniega, Donald B. Dingwell

Bibliographic record

VenueAGUFM · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsVolcanoFragmentation (computing)Explosive materialGeologyVolcanic rockExplosive eruptionVolcanologyMineralogyPyroclastic rockSeismologyPetrologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

Forecasting volcanic eruptions is a fundamental objective of volcanology. Given the complexity of magma ascent dynamics and that direct observations are impossible, laboratory experiments provide a promising approach to better understand the processes that lead to and feed explosive eruptions. Fragmentation of porous natural samples in a shock tube apparatus gives insights into the behavior of volcanic rocks during rapid decompression. In this preliminary study, we use acoustic emissions (AE) to monitor the generation of cracks and describe the signal that accompanies the fragmentation of natural volcanic rocks. Rapid decompression experiments were carried out in a fragmentation bomb at room temperature. We used sample sets from Merapi volcano (Indonesia) and Montserrat volcano (West Indies, UK) with open porosities ranging between 20% to 67%. Cylindrical samples were pressurized with Argon gas in an autoclave. To overcome the fragmentation threshold of the specific rocks, applied pressures varied from 5-20 MPa. Subsequent rapid decompression of the samples caused fragmentation. During fragmentation, acoustic emissions were monitored by a two 2- channel- AE system that allows sampling rates of 1-5 MHz/channel. Two piezzoelectric sensors (100-1000 kHz) were attached to the autoclave and simultaneously recorded the micro-seismic events. An enhanced setup with a waveguide connecting the sample to the AE sensors allowed a better quality of the recorded signals. Furthermore, to find the optimum sensor position, various settings were tried during decompression experiments. The recorded AE were analyzed with respect to their distribution, frequency, amplitude, and the energy released during fragmentation. The AE energy parameter takes into account both the number of hits and their sizes. Moreover, changes in size and characteristics of these acoustic emissions with explosion energy, magma state and energy partitioning will be discussed. The results of this study may contribute to better understand volcanic processes, and improve our ability to correctly evaluate the seismic nature of explosive eruptions, necessary to implement forecasting methods.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.991

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)0.0100.000

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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 designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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