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Record W2180440105 · doi:10.1190/geo2015-0001.1

A discontinuous collocated-grid implementation for high-order finite-difference modeling

2015· article· en· W2180440105 on OpenAlexaff
Na Fan, Lian‐Feng Zhao, Yingjie Gao, Zhenxing Yao

Bibliographic record

VenueGeophysics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsInstitute of Genetics
FundersNational Natural Science Foundation of China
KeywordsInterpolation (computer graphics)GridDiagonalRegular gridAlgorithmComputer scienceInteger (computer science)Finite differenceMathematicsGeometryApplied mathematicsMathematical optimizationMathematical analysisImage (mathematics)Computer vision

Abstract

fetched live from OpenAlex

ABSTRACT The discontinuous-grid method can greatly reduce the storage requirements and computational costs of finite-difference (FD) modeling for large-velocity-contrast models. However, traditional discontinuous-grid methods have to use interpolation when refining the wavefield in transition zones and would cause apparent artifacts. We have developed a new discontinuous collocated-grid scheme for high-order FD modeling. We refined the wavefield on a rotated coordinate system, where the interpolation is not required again. The horizontal and vertical spatial derivatives can be accurately converted into diagonal derivatives within the rotated coordinate system; thus, our scheme would be free of artifacts caused by improper interpolation. The ratio from coarse- to fine-grid spacing is restricted to 2n for our scheme, where n is a positive integer. Numerical experiments demonstrate that the proposed discontinuous collocated-grid scheme reduces the artificial reflections by about two orders of magnitude compared to the interpolation scheme and yields a wavefield that is almost identical to that of the uniform-grid simulation. The rotated FD operator with arbitrary even-order accuracy is applied in the transition zones; thus, it significantly improves the spatial accuracy while saving computational cost.

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 score0.906
Threshold uncertainty score0.995

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 head0.034
GPT teacher head0.256
Teacher spread0.223 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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