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Record W2793673453 · doi:10.1190/geo2017-0512.1

Estimation of δ and <i>C</i><sub>13</sub> of organic-rich shale from laser ultrasonic technique measurement

2018· article· en· W2793673453 on OpenAlexaff
Jianyong Xie, Bangrang Di, Douglas R. Schmitt, Jianxin Wei, Yangkang Chen

Bibliographic record

VenueGeophysics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
FundersChina Scholarship Council
KeywordsUltrasonic sensorAnisotropyOil shaleLookup tableAcousticsAttenuationComputer scienceFilter (signal processing)Synthetic dataNoise (video)AlgorithmMaterials scienceGeologyOpticsImage (mathematics)PhysicsArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

The laser ultrasonic technique (LUT) has many advantages in dealing with the anisotropy of organic-rich shale over the transducer ultrasonic contacting measurement. We have developed a systematic procedure to estimate anisotropic parameter [Formula: see text] and elastic parameter [Formula: see text] in organic-rich shale from LUT measurement. A novel filtering method called the structural-oriented space-varying median filter (SOSVMF) is proposed for removing the erratic noise from the recorded data. We apply the proposed algorithm onto one synthetic example and real recorded data in artificial bakelite and organic-rich shale by using LUT to show the successful performance. Compared with the original recorded data, the denoised waveform by SOSVMF will also help to obtain more accurate parameter estimations of the anisotropic parameter [Formula: see text] and elastic parameter [Formula: see text]. More importantly, the presented systematic procedure may extend the application of LUT to the study of attenuation anisotropy in organic-rich shale and scale physical modeling. In addition, the surface-modification method may be an effective method to enhance the laser-ultrasonic conversion efficiency.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.337

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 head0.011
GPT teacher head0.194
Teacher spread0.182 · 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 designBench or experimental
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

Citations14
Published2018
Admission routes1
Has abstractyes

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