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Record W2121578779 · doi:10.1190/segam2014-0536.1

Recovering a thin dipping conductor with 3D electromagnetic inversion over the Caber deposit

2014· article· en· W2121578779 on OpenAlexaffabout
Michael S. McMillan, Christoph Schwarzbach, Douglas W. Oldenburg, Eldad Haber, Elliot Holtham, Alexander Prikhodko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInversion (geology)OverburdenGeologyConductorParametric statisticsConductivityMineralogySeismologyMining engineeringGeometryTectonicsPhysicsMathematics

Abstract

fetched live from OpenAlex

Summary Airborne time-domain electromagnetic (EM) data were collected in 2012 over the Caber volcanogenic massive sulfide (VMS) deposit in western Quebec. We inverted the data in three-dimensions (3D) to produce a conductivity inversion model that helped image the thin dipping conductor and surrounding geology. The 3D inversion method consisted of a two-step approach. The first step employed a parametric inversion to recover a best-fitting shape of the dipping conductor using only data exhibiting an anomalous response over the deposit. With the parametric result as an initial and reference model, the second step used a conventional 3D EM inversion with data locations over the entire survey area. The second stage allowed for fine tuning of the shape and conductivity of the central dipping anomaly, while filling in features, such as overburden, in the remaining areas of the domain. The shape of the central conductive anomaly in the 3D inversion compared well with the known outline of the Caber deposit, based on geologic knowledge from past drilling. The overburden layer in the inversion model also agreed with previous geologic mapping. Preliminary results from this two-stage process show that it possible to recover a thin, dipping conductor with sharp boundaries through 3D EM inversion, which has been a difficult challenge in recent years.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
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 head0.196
Teacher spread0.186 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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