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The Seismic Acquisition Method Researching for the Complex Mountainous Terrain in YXL Area Qaidam Basin

2013· article· en· W2117578715 on OpenAlexvenueno aff
Qifeng Luo, An Peijun, Ning Hongxiao, Lijun Zhang, Ling‐Jun Yang

Bibliographic record

VenueAdvances in petroleum exploration and development · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyTerrainStack (abstract data type)SeismologyAttenuationStructural basinInterference (communication)Noise (video)SIGNAL (programming language)Feature (linguistics)Seismic arraySeismic explorationData acquisitionSichuan basinSignal-to-noise ratio (imaging)Remote sensingAcousticsGeomorphologyEngineeringComputer scienceTelecommunicationsCartographyOpticsGeographyGeochemistryImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

YXL area is the concentration area of exploration activity with classic complicate surface feature in Qaidam Basin. So, its interference wave is development and the seismic data is in low signal-to-noise ratio (SNR) in the area. Through multiple seismic exploration collecting means, Acquisition techniques has obtained great breakthrough, and array technique has showed great affection. The geological tasks and seismic exploration difficulties of target area is aimed in the paper. The remained problems in the past seismic exploration is dissected, studying the noise interference feature and the effects for the array noise attenuation. And the positive roles of the stack response for the noise attenuation is discussed and to supply the high quality and the high precision data for the seismic in this area. Key words : Shot-receiving array; Stack array response; Geometry; Direction effect; Array weighted average effect; Signal-to-noise ratio

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2013
Admission routes1
Has abstractyes

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