Efficient localization and spectral estimation of an unknown number of ocean acoustic sources using a graphics processing unit
Bibliographic record
Abstract
This paper develops a matched-field approach to localization and spectral estimation of an unknown number of ocean acoustic sources employing massively parallel implementation on a graphics processing unit (GPU) for real-time efficiency. A Bayesian formulation is developed in which the locations and complex spectra of multiple sources and noise variances are considered unknown random variables, and the Bayesian information criterion is minimized to estimate these parameters, as well as the number of sources present. Optimization is carried out using simulated annealing and includes steps that attempt to add/delete sources to/from the model. Closed-form maximum-likelihood (ML) solutions for source spectra and noise variances in terms of the source locations allow these parameters to be sampled implicitly, substantially reducing the dimensionality of the inversion. Source sampling, addition, and deletion are based on joint conditional probability distributions for source range and depth, which incorporate the ML spectral estimates. Computing these conditionals requires solving a very large number of systems of equations, which is carried out in parallel on a GPU, improving efficiency by 2 orders of magnitude. Simulated examples illustrate localizations and spectral estimation for a large number of sources (up to eight), and investigate mitigation of environmental mismatch via efficient multiple-frequency inversion.
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 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 | 0.000 |
| 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 | 0.001 |
| 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".