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Record W2969428709 · doi:10.1190/geo2017-0707.1

Estimating changes in seismic wave velocity from a pneumatic source in an operational mine

2019· article· en· W2969428709 on OpenAlexaboutno aff
B.P. Salmon, Gareth Goldswain, Richard A. Lynch, Daryl Rebuli, J.C. Olivier, W. Kleynhans

Bibliographic record

VenueGeophysics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
FundersAustralian Research CouncilAustralian Government
KeywordsGeophoneGeologySeismic velocitySeismologyRock mass classificationSIGNAL (programming language)Seismic waveMicroseismSeismic noiseAcousticsMining engineeringGeotechnical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

ABSTRACT Rock mass characteristics such as applied stress, pore pressure, and fracture density are coupled to the seismic wave propagation velocity. Therefore, measuring small relative changes of seismic wave velocities in operational mines has the potential for tracking changes in these important rock mass properties. To this end, we have conducted an experiment with the aim of measuring seismic body wave velocities in situ at Williams mine, Hemlo, Canada, using a pneumatic source and multiple receivers. We determine that the signal-to-noise ratio is improved by deconvolving the source signal from the signal recorded at the remotely grouted geophones. We evaluate the estimated relative changes in traveltimes and assume that these are due to changes in the body wave velocity caused by mining-related activities. We are able to successfully detect the source response at remote receivers in an operational mine at a distance of more than 430 m.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.985

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)0.0010.000

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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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