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Record W3196791222 · doi:10.1190/segam2021-3594961.1

Least-squares DAS to geophone transform

2021· article· en· W3196791222 on OpenAlexaffabout
Jorge Monsegny, Kevin Hall, Daniel Trad, Don C. Lawton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeophoneDistributed acoustic sensingCompendiumDisplacement (psychology)GeologyStrain gaugeComputer scienceGeodesySeismologyOptical fiberTelecommunicationsEngineeringFiber optic sensorGeographyElectrical engineeringArchaeology

Abstract

fetched live from OpenAlex

Distributed acoustic sensing uses optical fibre to measure strain or strain rate along to the fibre direction. The strain rate estimated by distributed acoustic sensing is related to the total displacement of a section of the fibre called gauge length. By using this link between strain rate and displacement we propose a least squares inversion scheme to obtain the particle velocity along the fibre from strain rate in a distributed acoustic sensing system. We test this least squares transformation with data from the Containment and Monitoring Institute Field Research Station in Alberta, Canada. We found that the transformed traces are very similar to a filtered version of the corresponding geophone ones, in particular at early times.

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 score0.837
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)0.0310.002

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 head0.010
GPT teacher head0.223
Teacher spread0.214 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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