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Record W2333501703 · doi:10.1190/segam2013-0822.1

Robust reduced-rank seismic denoising

2013· article· en· W2333501703 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

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicStatistical and numerical algorithms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSingular value decompositionMatrix decompositionHankel matrixGaussian noiseRank (graph theory)OutlierNoise (video)Noise reductionAlgorithmSingular valueLow-rank approximationFactorizationMatrix (chemical analysis)Computer scienceFilter (signal processing)MathematicsNoise measurementStatisticsArtificial intelligencePhysicsMathematical analysisEigenvalues and eigenvectorsCombinatorics

Abstract

fetched live from OpenAlex

Singular Spectrum Analysis (SSA) or Cadzow reduced-rank filtering is an efficient method for random noise attenuation when the data are contaminated by Gaussian noise. SSA starts by embedding the seismic data into a Hankel matrix. Rankreduction of this Hankel matrix followed by anti-diagonal averaging is utilized to estimate an enhanced seismic signal. The rank-reduction step in the SSA filter is often implemented via the truncated Singular Value Decomposition (TSVD). The TSVD is a non-robust matrix factorization that often leads to suboptimal results when the seismic data are contaminated by erratic noise. We propose to adopt a robust matrix factorization that permits to utilize the SSA filter in situations where the data are contaminated by noise bursts, outliers and/or isolated anomalous traces.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.670
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)0.0050.002

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 head0.071
GPT teacher head0.291
Teacher spread0.220 · 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

Quick stats

Citations15
Published2013
Admission routes1
Has abstractyes

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