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Record W2595499855 · doi:10.3997/2214-4609.201600713

Julia Application in Calculating Reflectivity from High Order Anisotropic Interface

2016· article· en· W2595499855 on OpenAlexaff
R. Malehmir, Douglas R. Schmitt

Bibliographic record

Venue78th EAGE Conference and Exhibition 2016 · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIsotropyAnisotropyAmplitudePolarization (electrochemistry)Seismic waveSlownessPlane waveInversion (geology)Reflection (computer programming)OpticsAlgorithmComputer sciencePhysicsGeologyGeophysics

Abstract

fetched live from OpenAlex

Summary Elastic anisotropy in the Earth causes many artifacts in an isotropic seismic processing and interpretation. To date, these issues have been ameliorated by using approximations to the full solutions for wave propagation and reflectivity for special material symmetries. This algorithm has been written to extend these capabilities to the general case of reflectivity from the interface between two anisotropic slabs of arbitrary symmetry and orientation. To achieve this, the algorithm solves for polarization, amplitude and slowness of all the wave modes generated by a plane wave incident to the interface. In the first step, the plane-wave velocities and polarizations of all three orthogonal wave modes are calculated for a given incidence angle. Second, the algorithm determines the reflection and transmission angles of all of the possible scattered modes followed by their respective velocities and polarization vectors. With this information, the algorithm solves system of equations incorporating the imposed boundary conditions to arrive at the scattered wave amplitudes. We have tested this algorithm in some of the most computationally difficult models to ensure there is no energy leakage in the system of calculations. This algorithm could be used in seismic processing, migration, and inversion of seismic data in anisotropic media.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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.

Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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