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Record W4415567831 · doi:10.1190/geo-2024-0958

3D velocity modeling for mineral exploration via ambient noise tomography

2025· article· en· W4415567831 on OpenAlexafffundabout
Hema Sharma, Adebayo Oluwaseun Ojo, Sheri Molnar, David Good, Nicholas Arndt, John H. McBride, Chanelle Boucher, Charles D. Beard, Dan Hollis

Bibliographic record

VenueGeophysics · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsOntario Power GenerationGolder Associates (Canada)Barrick Gold (Canada)Western University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmbient noise levelGeophoneRayleigh waveMineral explorationSeismic noiseNoise (video)TomographyDispersion (optics)

Abstract

fetched live from OpenAlex

ABSTRACT An application of ambient noise tomography (ANT) for mineral exploration near the town of Marathon, Ontario, Canada, is documented. The study area consists of host rocks for platinum group metals and copper. ANT was performed to generate a 3D shear-wave velocity (VS) model for the exploration of Marathon deposits. The ambient noise recordings used were collected using 90 vertical geophones for 26 days. The recordings were used to compute the noise correlation functions and estimate Rayleigh wave phase and group velocity dispersion curves. Then, these dispersion measurements were used for 2D tomography to create 2D velocity maps that were inverted to obtain a 3D VS model. The independently produced VS model shows good agreement with the measured VS from downhole VS logging. Furthermore, the developed 3D velocity model aids in interpretation of the study area that can be helpful for future mineral exploration and for improving understanding of the mineral generation model.

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: none
Teacher disagreement score0.928
Threshold uncertainty score0.325

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)0.0000.000

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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