Efficient Bayesian multi-source localization using a graphics processing unit
Bibliographic record
Abstract
This paper presents a highly-efficient approach to matched-field localization of an unknown number of ocean acoustic sources employing a graphics processing unit (GPU) for massively parallel computations. A Bayesian formulation is developed in which the number, locations, and complex spectra (amplitudes and phases) of multiple sources, as well as noise variance at each frequency, are considered unknown random variables constrained by acoustic data and prior information. The number of sources is determined during an initial burn-in stage by minimizing the Bayesian information criterion using an efficient birth/death scheme. Marginal posterior probability distributions for source locations are then computed using Gibbs sampling. Source and noise spectra are sampled implicitly by applying analytic maximum-likelihood solutions in terms of the source locations (explicit parameters). This greatly reduces the dimensionality of the inversion, but requires solving a very large number (order 10^5) of complex matrix inversions for each sample of the explicit parameters. These inversions can be solved in parallel on a GPU, increasing efficiency by a factor of ~100. Examples are given of localizing a large number of sources (up to 10) in near real time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".