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Record W1992297305 · doi:10.3997/2214-4609.20140599

Geological and Rock Physics Constraints in Anisotropic Tomography

2014· article· en· W1992297305 on OpenAlex
Marta Woodward, Y. Yang, Konstantin Osypov, Ran Bachrach, David M. Nichols, Olga Zdraveva, Y. Liu, A. Fournier

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsCanyonAnisotropyRegularization (linguistics)GeologyTomographyProperty (philosophy)Seismic tomographyStreet canyonSeismic anisotropyGeophysicsComputer sciencePhysicsGeomorphologyArtificial intelligenceOpticsMantle (geology)

Abstract

fetched live from OpenAlex

Because anisotropic models are unconstrained by surface-seismic data alone, we must learn to incorporate other non-seismic measurements and knowledge into the model building process. For this purpose, we demonstrate the regularization of anisotropic tomography with a preconditioning method which smooths updates along geological dip and constrains cross-property correlations to follow the predictions of rock-physics for compacting shales. The method is applied to the Green Canyon area in the Gulf of Mexico.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.227

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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