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Record W4311819641 · doi:10.1190/geo2022-0065.1

A phase transition in the O’Doherty-Anstey model

2022· article· en· W4311819641 on OpenAlexafffund
K. A. Innanen

Bibliographic record

VenueGeophysics · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScatteringSmoothnessLagWeightingStatistical physicsPhase transitionPartition (number theory)Function (biology)Phase (matter)MathematicsReflectivityPhysicsOpticsMathematical analysisCondensed matter physicsQuantum mechanicsComputer scienceAcousticsCombinatorics

Abstract

fetched live from OpenAlex

ABSTRACT In the O’Doherty-Anstey model, the spectrum of a seismic wave transiting and reverberating within a stack of interfaces is estimated in terms of its reflectivity. At the center of the model is a discrete combinatorical calculation, in which the contributing raypaths at each lag are weighted and counted. The model maps naturally into an analysis based on statistical mechanics, with raypaths playing the role of system configurations and lag playing the role of system energy. This leads to an expression for the probability of finding a contribution to the wave at a given lag, determined up to a partition function and a scattering parameter analogous to temperature. The partition function is estimated within the O’Doherty-Anstey model itself, and the scattering parameter can be adjusted to describe geologic media with increased or decreased scattering potential. The average contributing lag deriving from this analysis exhibits a continuous phase transition, separating two distinct scattering regimes, one in which the direct wave dominates and the other in which the scattering dominates. The weighting produced by the reflectivity appears to govern the smoothness of the transition.

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 score0.788
Threshold uncertainty score0.411

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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