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Record W2544721651 · doi:10.1190/geo2015-0575.1

Modeling anisotropic static elastic properties of soft mudrocks with different clay fractions

2016· article· en· W2544721651 on OpenAlexafffundabout
Biao Li, R.C.K. Wong

Bibliographic record

VenueGeophysics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsConcordia UniversityUniversity of Calgary
FundersUniversity of Calgary
KeywordsAnisotropyTransverse isotropyElastic modulusClay mineralsIsotropyPorosityMaterials scienceGeotechnical engineeringComposite materialGeologyDeformation (meteorology)Mineralogy

Abstract

fetched live from OpenAlex

ABSTRACT We have quantified the effects of clay fraction and fabric on the static elastic properties of soft mudrocks with emphasis on microlevel mechanisms. Soft mudrocks are treated as a mixture of nonclay minerals and clay-water composites. We have devised a simplified approach to estimate the fabric orientation distribution of soft mudrocks based on measured parameters such as clay fraction and porosity. A single parameter (fabric angle) that characterizes the fabric orientation distribution of soft mudrocks is related to the void ratio of clay-water composites. The static transversely isotropic (TI) elastic properties of soft mudrocks are modeled using an anisotropic differential effective medium approach. The effect of variation in fabric orientation distribution on the TI elastic parameters of clay-water composites is studied by applying the Voigt approximation. With an increase of clay fraction, soft mudrocks have decreasing trends in the deformation moduli because some nonclay minerals are replaced by clay-water composites. However, the deformation moduli of clay-water composites could increase when there is more anisotropy in the fabric due to an increase in the clay fraction. Thus, the correlations between anisotropic elastic moduli and volume fraction of clay-water composites will display some fluctuations. Such nonlinear relationships are validated against published experimental data on Colorado shale samples from the Western Canadian Sedimentary Basin.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.210

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 head0.016
GPT teacher head0.191
Teacher spread0.175 · 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 designSimulation or modeling
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

Citations10
Published2016
Admission routes3
Has abstractyes

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