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Record W2318182044 · doi:10.1093/gji/ggs040

Inductive source induced polarization

2012· article· en· W2318182044 on OpenAlexaff
David Marchant, Eldad Haber, Douglas W. Oldenburg

Bibliographic record

VenueGeophysical Journal International · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInduced polarizationInversion (geology)Frequency domainGeodetic datumGeophysicsGeologyComputer scienceAlgorithmElectrical resistivity and conductivityMathematicsMathematical analysisGeodesySeismologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Induced polarization (IP) surveys are commonly performed to map the distribution of electrical chargeability that is a diagnostic physical property in mineral exploration and in many environmental problems. Although these surveys have been successful in the past, the galvanic sources required for traditional IP and magnetic IP (MIP) surveys prevent them from being applied in some geological settings. We develop a new methodology for processing frequency domain EM data to identify the presence of IP effects in observations of the magnetic fields arising from an inductive source. The method makes use of the asymptotic behaviour of the secondary magnetic fields at low frequency. A new quantity, referred to as the ISIP datum, is defined so that it equals zero at low frequencies for any frequency-independent (non-chargeable) conductivity distribution. Thus, any non-zero response in the ISIP data indicates the presence of chargeable material. Numerical simulations demonstrate that the method can be applied even in complicated geological situations. A 3-D inversion algorithm is developed to recover the chargeability from the ISIP data and the inversion is demonstrated on synthetic examples.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.265
Teacher spread0.242 · 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 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

Citations17
Published2012
Admission routes1
Has abstractyes

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