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Record W2158546834 · doi:10.1190/1.2194513

A new formula to compute apparent resistivities from marine magnetometric resistivity data

2006· article· en· W2158546834 on OpenAlexafffund
Jiuping Chen, Douglas W. Oldenburg

Bibliographic record

VenueGeophysics · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsSchlumberger (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrical resistivity and conductivityTransmitterAzimuthGeologyMagnetic fieldField (mathematics)Range (aeronautics)GeophysicsMineralogyGeodesyGeometryMathematicsMaterials sciencePhysicsElectrical engineeringComputer scienceTelecommunicationsComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Magnetometric resistivity (MMR) is an electromagnetic (EM) exploration method that has been used successfully to investigate electrical-resistivity structures below the sea-floor. Apparent resistivity, derived from the observed azimuthal component of the magnetic field, often is used as an approximation to the resistivity of a layered earth. Two commonly used formulas to compute the apparent resistivity have their own limitations and are invalid for a deep-sea experiment. In this paper, we derive an apparent-resistivity formula based upon the magnetic field resulting from a semi-infinite electrode buried in a 1D layered earth. This new formula can be applied to both shallow and deep marine MMR surveys. In addition, we address the effects that arise from the transmitter-receiver (Tx-Rx) depth difference and the choice of the normalized range (the radial distance between transmitter and receiver, divided by the thickness of seawater) on data interpretation and survey design. The performance of the new formula is shown by processing synthetic and field data.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
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
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.027
GPT teacher head0.244
Teacher spread0.216 · 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 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
Published2006
Admission routes2
Has abstractyes

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