Mapping shallow water acoustic measurements to deep-water equivalents via model-based calibration
Bibliographic record
Abstract
Accurate measurement of underwater radiated noise (URN) from ships in shallow water is critical for assessing compliance with marine environmental standards. However, shallow environments introduce significant distortion due to seabed and surface interactions, challenging the validity of conventional URN measurements. This study presents a methodology to calibrate and model shallow water acoustic ranges using in situ propagation loss (PL) measurements. Calibrated low-frequency acoustic projectors (43–1200 Hz) were deployed to emulate vessel noise sources and characterize transmission conditions. Measured PL was validated using physics-based models (KRAKEN/KRAKENC and BELLHOP), showing strong agreement in most frequency bands. Dynamic ship ranging trials were then performed, enabling transformation of shallow water measurements into deep-water equivalent source levels. A custom Python-based data tracking and visualization framework was developed to ensure precise positional analysis and efficient validation of acoustic trials. The resulting methodology provides a framework for evaluating and certifying candidate acoustic ranges in coastal areas, supporting broader access to URN assessments for maritime operators. These contributions advance the development of standardized shallow water ranging procedures and lay the groundwork for a future national standard. Ongoing work includes expansion to additional vessel types and environmental conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".