# Physics-informed simulation framework for realistic sonar image generation and statistical validation

arXiv: https://arxiv.org/abs/2605.19712 · HTML: https://mlsanity.com/paper/2605.19712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 3 of 3)

- [Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2401.14546/) (2024)
- [Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition](https://mlsanity.com/paper/2210.10267/) (2022)
- [S3Simulator: A benchmarking Side Scan Sonar Simulator dataset for Underwater Image Analysis](https://mlsanity.com/paper/2408.12833/) (2024)

## Built on by (most cited first, 1 of 1)

- [BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations](https://mlsanity.com/paper/2608.23215/) (2026)

## Abstract (arXiv)

Synthetic sonar datasets offer a scalable alternative to costly real-world acquisition, yet their utility remains limited by the absence of rigorous quantitative validation. We present ACOUSIM (ACOustic SIMulation and Validation Platform), a physics-informed framework that evaluates the statistical alignment between synthetic and real sonar imagery without relying on generative models. A Gazebo-based environment generates sonar-like images by explicitly controlling seabed texture, illumination-driven shadowing, platform altitude, and noise. Realism is quantified against two public sonar datasets, SeabedObjects-KLSG-II and Sonar Common Target Detection (SCTD), using global intensity and local texture (LBP) distributions assessed via Kullback-Leibler divergence, Jensen-Shannon divergence, and Earth Mover's Distance. Results show strong texture alignment (KL < 0.07) across all classes, with plane-class intensity alignment outperforming ship-class due to shadow geometry complexity. ACOUSIM establishes a reproducible, distribution-level baseline for sim-to-real sonar evaluation and directly supports reliable dataset validation for underwater image analysis.