Predicting transmission loss in underwater acoustics using convolutional recurrent autoencoder network
Why this work is in the frame
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Bibliographic record
Abstract
Underwater noise transmission in the ocean environment is a complex physical phenomenon involving not only widely varying physical parameters and dynamical scales but also uncertainties in the ocean parameters. It is challenging to construct generalized physical models that can predict transmission loss in a broad range of situations. In this regard, we propose a convolutional recurrent autoencoder network (CRAN) architecture, which is a data-driven deep learning model for learning far-field acoustic propagation. Being data-driven, the CRAN model relies only on the quality of the data and is agnostic to how the data are obtained. The CRAN model can learn a reduced-dimensional representation of physical data and can predict the far-field acoustic signal transmission loss distribution in the ocean environment. We demonstrate the ability of the CRAN model to learn far-field transmission loss distribution in a two-dimensional ocean domain with depth-dependent sources. Results show that the CRAN can learn the essential physical elements of acoustic signal transmission loss generated due to geometric spreading, refraction, and reflection from the ocean surface and bottom. Such ability of the CRAN to learn complex ocean acoustics transmission has the potential for real-time far-field underwater noise prediction for marine vessel decision-making and online control.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it