MétaCan
Menu
Back to cohort
Record W4387703899 · doi:10.4043/32741-ms

Marine Vibrator Milestone: A Pilot Seismic Survey

2023· article· en· W4387703899 on OpenAlexaff
R. Alfaro, S. Secker, E. Zamboni, Antoine Guitton, A. Cozzens, N. Henderson, V. Nechayuk, Mike Jenkerson, Graham Johnson, J. Karran

Bibliographic record

VenueOffshore Technology Conference Brasil · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsSeismic vibratorMarine engineeringSubmarine pipelineComputer scienceEngineeringGeologySeismologyOceanography

Abstract

fetched live from OpenAlex

Abstract The Marine Vibrator Joint Industry Project (MVJIP), sponsored by TotalEnergies, Shell and ExxonMobil, completed a successful pilot survey in Q3 2022 using two Integrated Project Node (IPN) Marine Vibrators in an offshore European open water setting. The IPNs were deployed in two configurations: one under tow, and the other stationary at each shot point. Various sweep types and duty cycles were also tested. An airgun source of equivalent energy size was also used for comparison. Throughout the pilot the IPNs demonstrated exemplary performance. There were no HSE incidents during the project, nor was there any technical downtime related to the IPNs. Moreover, real time quality control results were able to show the IPNs high fidelity with respect to the pilot sweep and excellent sweep-to-sweep repeatability. A fast-track processing sequence was completed, which showed that the IPNs have been able to image the subsurface very well including steeply dipping reflectors. This demonstrates the progress that has been achieved by the MVJIP over the last 10 years and gives a real impetus to continue the development of the marine vibrators into a commercial source. The next steps will be to complete a more comprehensive full processing sequence on all the lines to extract the best possible images.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.006

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 head0.053
GPT teacher head0.274
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueOffshore Technology Conference BrasilSame topicUnderwater Acoustics ResearchFrench-language works237,207