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Record W2084082172 · doi:10.1109/jstars.2013.2285383

Geophysical Signal Parameterization and Filtering Using the fractional Fourier Transform

2013· article· en· W2084082172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2013
Typearticle
Languageen
FieldMathematics
TopicMathematical Analysis and Transform Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFractional Fourier transformFourier transformFrequency domainComputer scienceSignal processingTime–frequency analysisShort-time Fourier transformNoise (video)AlgorithmSIGNAL (programming language)GeologyFilter (signal processing)MathematicsFourier analysisArtificial intelligenceMathematical analysisTelecommunicationsImage (mathematics)Computer visionRadar

Abstract

fetched live from OpenAlex

The fractional Fourier transform (FrFT) domain operations offer an alternative to the conventional Fourier Transform (FT) in signal processing, especially for seismic data. It is shown that subsurface reflection events in a two-dimensional (time-spatial) seismic data behave like linear chirps for each frequency in the frequency-spatial domain. Since the FrFT kernel is a set of linear chirps, frequency-spatial domain seismic data processing is better suited using the FrFT than the FT. An analytical relationship between linear seismic events and FrFT parameters is derived and illustrated with an example. Seismic data from a field survey is used to show that the FrFT filtering performs better in coherent noise attenuation than the FT.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.318

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)0.0000.000

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 head0.064
GPT teacher head0.295
Teacher spread0.231 · 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