AUV-Based 3D Laser Inspection for Structural Integrity Management in Deepwater Fields
Bibliographic record
Abstract
Abstract Lockheed Martin has developed the capability to conduct AUV-based structural survey and post-hurricane platform inspection using 3D mapping and change detection with a 3D sonar. Successful validation trials for this technology were conducted with the Marlin® AUV during the summer of 2011 and the first commercial operations were conducted in the summer of 2012. Lockheed Martin Corporation, in partnership with underwater laser developer 3D at Depth LLC, and supported by funding from the Research Partnership to Secure Energy for America (RPSEA), is now extending its revolutionary 3D modeling and change detection to employ a 3D laser sensor, thereby improving model resolution and accuracy by an order of magnitude, from centimeter scale to millimeter scale. AUVs outfitted with 3D Laser imaging systems will provide new, high accuracy tools for subsea integrity management that are currently used extensively in terrestrial applications, including High Definition Scanning (HDS) for close-in inspection of problem areas, and underwater LIDAR (LIght Detection And Ranging) for 3D mapping and inspection of flowlines, risers, and other subsea infrastructure. The scope of this project is to develop and demonstrate the technology required to conduct AUV-based 3D-laser imaging utilizing 3D mapping and change detection. Objectives include demonstration of close-in, high resolution underwater structural inspection, generation of high resolution 3D models of subsea structures, and performance of change detection of flaws or damage against a priori structural models. This paper will detail the results achieved to-date and will highlight the 3D Laser performance improvements over current platform inspection methods, including significant improvements in operating efficiencies and the development of accurate 3D models for use in structural integrity management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".