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Numerical Simulation of the Multi-Level Air-Gun Array Based on Over/Under Source

2013· article· en· W1767382088 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy science and technology · 2013
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Launch and Propulsion Technology
Canadian institutionsnot available
Fundersnot available
KeywordsSuperposition principleBandwidth (computing)Computer sciencePhysicsAcousticsTelecommunications

Abstract

fetched live from OpenAlex

The conventional air-gun array is made of all guns at the same depth. This array is arranged in a simple way. It is easy to operate. But the ghost effect is obvious. And the ghost will limit the effective seismic bandwidth. Using the over/under source, we can attenuate the ghosts generated at the source side. Based on the over/under source, this paper proposes the multi-level source which consists sub-arrays placing at different depths and firing them in a sequence. This makes the notch frequencies of the sub-arrays different. After superposition, it can attenuate the source ghost. The sub-arrays in different depths are regarded as the over/under sources. Then we separate the up-going and down-going events. Data test proved that the algorithm can indeed attenuate the ghost included in the far-field wavelet, which extends the effective bandwidth and benefits low and high frequencies.

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: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.281

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 studies0.0000.001
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 head0.007
GPT teacher head0.199
Teacher spread0.193 · 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