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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 OpenAlexaff
Polina Zheglova, Matteo Ravasi, Ivan Vasconcelos, Alison Malcolm

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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 routes1
Has abstractyes

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