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Record W2103194346 · doi:10.1190/1.1487074

Spectral analysis of a ghost

2001· article· en· W2103194346 on OpenAlexafffund
N. S. Hamarbitan, Gary F. Margravé

Bibliographic record

VenueGeophysics · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsCoherence (philosophical gambling strategy)DynamitePhysicsSingle shotBandwidth (computing)Resolution (logic)SIGNAL (programming language)Shot (pellet)Spectral analysisExplosive materialSpectral densityOpticsGeologyComputer scienceMaterials scienceTelecommunicationsChemistryArtificial intelligenceSpectroscopy

Abstract

fetched live from OpenAlex

Abstract A seismic line was shot such that the source-ghost effect from two different dynamite source patterns could be compared. Two 15-shot seismic datasets were created that were identical in all respects except that one used 4 kg of explosives in a single 18-m hole, while the other used 2 kg of explosives in each of two 9-m holes. After identical processing, the final stacked sections of the 18-m and 9-m datasets are dramatically different in character and temporal resolution. An f-x spectral analysis of the stacked sections reveals that the 18-m data shows a loss in power and phase coherence from 45 to 58 Hz, while the 9-m data shows a similar effect from 65 to 78 Hz. A spectral notch, centered near 55 Hz, due to a source ghost is suggested as the reason for the lower power in the 18-m dataset. The 9-m data is consistent with a spectral notch at a higher central frequency near 72 Hz. Above its spectral notch, 18-m data shows a reemergence of weak signal that persists to near 80 Hz; the 9 m dataset shows little signal above 65 Hz. Examination of raw shot records shows that these effects are very difficult to observe in field records. Without specialized deghosting, the 9-m dataset shows greater temporal resolution; however, the 18-m dataset has a broader signal bandwidth and is potentially superior.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 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

Citations15
Published2001
Admission routes2
Has abstractyes

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