Efficient estimation of range-dependent seabed properties from large data volumes of a towed source and receiver-array system
Bibliographic record
Abstract
Knowledge of geophysical seabed properties is important for shallow-water sonar applications, including detection and classification of unexploded ordnance. However, state-of-the-art surveying methods such as seismic profiling, coring, or acoustic inversion are of limited use when surveying large areas with high spatial sampling density. We consider a new acoustic survey method based on a towed source and receiver array which produces large volumes of seabed reflectivity data that contain unprecedented and detailed seabed information. These data can be analyzed by inversion, but require efficient computation of reflection coefficients, efficient inversion algorithms and efficient use of computer resources. This work applies a particle filter to quantify information content of multiple data sets by considering results from previous data along the survey track to inform the importance sampling at the current point. Challenges arise from rapid environmental changes along the track where the complexity of sediment layers and their properties change. This is addressed by including trans-dimensional steps in the filter which allow the layering complexity to change along a track. Efficiency is improved by tempering the likelihood function of particle subsets and including exchange moves (parallel tempering). The filter is implemented on a hybrid computer that combines central processing units (CPUs) and graphics processing units (GPUs). The algorithm exploits three levels of parallelism: (1) parallel computation of spherical reflection coefficients with a GPU implementation of Levin integration; (2) updating particles by concurrent CPU processes which exchange information using automatic load balancing; (3) overlapping CPU-GPU communication (a major bottleneck) with GPU computation by staggering CPU access to the multiple GPUs (via multi process service). The algorithm is applied to simulated spherical reflection coefficients for 170 data sets along a 7-km track. We demonstrate substantial efficiency gains over previous methods, providing uncertainty quantification for 100+ data sets per 24 hours.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".