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Record W2059186208 · doi:10.1190/tle33090986.1

Enhanced imaging with high-resolution full-waveform inversion and reverse time migration: A North Sea OBC case study

2014· article· en· W2059186208 on OpenAlexaff
A. Ratcliffe, Antonio Privitera, Graham Conroy, Vetle Vinje, Alexandre Bertrand, B. Lyngnes

Bibliographic record

VenueThe Leading Edge · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsSeismic migrationGeologyOverburdenInversion (geology)Regional geologyEnvironmental geologyWaveformEconomic geologyNorth seaHigh resolutionGeophysical imagingSeismologyComputer scienceRemote sensingTelecommunicationsTelmatologyTectonicsOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract In a case study from the Tommeliten Alpha area of the Norwegian North Sea, imaging problems were caused by the presence of gas in the overburden. In particular, a large part of the reservoir is in a seismically obscured area (SOA) caused by the gas. Full-waveform inversion (FWI) and reverse time migration (RTM) dramatically improve the imaging from ocean-bottom cable (OBC) acquisition over the region. The FWI algorithm is pushed to 22 Hz to generate an extremely high-resolution velocity model, and RTM then becomes required to honor the complexity in the resultant velocity model. Consequently, migration is done with the FWI model to generate a high-frequency RTM image to 80 Hz. This image is approximately double the maximum frequency commonly used for RTM in the North Sea and matches that of equivalent Kirchhoff products, but with all the benefits in imaging that RTM brings, providing a subsequent impact on interpretation of the area.

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 score0.937
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 communication0.0000.000
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 head0.011
GPT teacher head0.203
Teacher spread0.193 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

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