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Record W4285156347 · doi:10.3997/2214-4609.202210838

Neural Estimation of Seismic Local Slopes

2022· article· en· W4285156347 on OpenAlex
Breno Bahia, Mauricio D. Sacchi

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

Venue83rd EAGE Annual Conference & Exhibition · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPlane waveSeismic waveDiscretizationPlane (geometry)Artificial neural networkFinite difference methodFinite differenceGeologyMathematical analysisGeometryMathematicsComputer scienceSeismologyPhysicsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

Summary The plane-wave assumption plays a crucial role in several classic seismic data processing techniques, such as plane-wave destructors. Plane waves can be described by the local plane-wave partial differential equation, where a seismic wavefield is characterized by its slope. Plane-wave destructors are constructed as finite-difference stencils for the plane-wave differential equation as a function of the unknown seismic slope, which can be estimated through a linear least-squares problem. This paper parametrizes the slope field of a seismic section by a neural network followed by the finite difference discretization of the model to obtain a physically meaningful learning process. The neural network, although task-specific, can estimate the unknown slope of the input seismic section.

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 categoriesInsufficient payload (model declined to judge)
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 score0.821
Threshold uncertainty score0.996

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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