Predicting transmission loss in underwater acoustics using continual learning with range-dependent conditional convolutional neural networks
Bibliographic record
Abstract
Efficient and accurate prediction of underwater acoustic transmission loss (TL) is important for minimizing noise impacts on marine ecosystems and supporting naval operations. Traditional wave-based solvers are computationally expensive, especially for range-dependent bathymetry, rendering them unsuitable for real-time applications. Recent advances in data-driven models, particularly convolutional and recurrent neural networks, provide a more efficient alternative by substantially reducing the dimensionality of the data. However, these deep-learning models struggle with long-range wave forecasts as they often rely on auto-regressive predictions and lack far-field bathymetry information. This research aims to improve the accuracy of deep learning models for forecasting underwater radiated noise in far-field scenarios. We introduce a range-dependent conditional convolutional neural network that predicts TL fields in a single step by conditioning directly on input bathymetry. The model is trained using a replay-based continual learning strategy, which allows generalization across sequential bathymetric changes without retraining. We evaluate our model using multiple test cases and a benchmark scenario that involves predictions over the Dickins Seamount. Our architecture effectively captures transmission loss over range-dependent bathymetry profiles. The proposed framework provides an efficient deep learning model for digital twins of the ocean soundscape, enabling real-time decision-making for underwater radiated noise.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".