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Record W4285134044 · doi:10.3997/2214-4609.202210525

Interferometric, Target-Enclosing Waveform Inversion: a Comparison of Approaches

2022· article· en· W4285134044 on OpenAlex
Polina Zheglova, Matteo Ravasi, Ivan Vasconcelos, Alison Malcolm

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 institutionsKootenay Association for Science & TechnologyMemorial University of Newfoundland
Fundersnot available
KeywordsInterferometryInversion (geology)Convolution (computer science)WaveformAlgorithmKinematicsComputer scienceInverse problemGeologyMathematicsOpticsArtificial intelligenceMathematical analysisPhysicsSeismologyTelecommunications

Abstract

fetched live from OpenAlex

Summary We present a new target-enclosing full waveform inversion (FWI) method based on a interferometric objective function, where the inversion is driven by the misfit between the wavefields reconstructed in a subdomain of interest via the convolution and correlation representation formulas. The method is fully local in the sense that it does not depend on the reconstruction of the physical properties outside the local domain, and only requires a kinematic velocity model estimate for redatuming. We compare the proposed method to another fully local FWI method based on the convolution representation formula and to full-model surface-data FWI. We demonstrate the potential of the proposed interferometric full waveform inversion method to achieve higher resolution images at a comparable or lower cost than the other methods.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.093
GPT teacher head0.267
Teacher spread0.174 · 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