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Record W2891822602 · doi:10.1190/segam2018-2998300.1

Estimating overburden thickness in resistive areas from two-component airborne EM data

2018· article· en· W2891822602 on OpenAlexaff
Thomas Bagley, Richard S. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsLaurentian University
Fundersnot available
KeywordsOverburdenResistive touchscreenGeologyComponent (thermodynamics)Remote sensingGeotechnical engineeringPhysicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

An overburden with variable thickness can obscure the response of underlying geophysical features. For example, the gravity response of an increased thickness of low-density overburden might not be distinguishable from a deeper sandstone hydrothermally altered to clay. When the overburden is conductive, it’s thickness can be determined from the rate of decay of the off-time airborne electromagnetic data. However, the off-time decay of a thin or resistive overburden is small and difficult to measure. Previous studies have used the on-time resistive-limit response of a single component to successfully map apparent ground conductance in resistive areas. Quantitative resistive-limit models exist for thin-sheet, half-space, thin-sheet over half-space, and thick-sheet over half-space models. This study uses horizontal and vertical component data to estimate the thickness (and conductivities) of a two layered model across the survey profile. Presentation Date: Thursday, October 18, 2018 Start Time: 8:30:00 AM Location: 213B (Anaheim Convention Center) Presentation Type: Oral

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations3
Published2018
Admission routes1
Has abstractyes

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