Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator

TL;DR

Hankel-FNO is a physics-informed Fourier Neural Operator model that enables fast, accurate, and adaptable underwater acoustic charting, outperforming traditional solvers and existing data-driven methods in speed and accuracy.

Contribution

The paper introduces Hankel-FNO, a novel physics-encoded FNO model that improves underwater acoustic charting efficiency and adaptability over prior numerical and deep learning approaches.

Findings

Hankel-FNO outperforms traditional numerical solvers in speed.

The model achieves higher accuracy than existing data-driven methods.

It demonstrates robustness across diverse environments with minimal fine-tuning.

Abstract

Fast and accurate underwater acoustic charting is crucial for downstream tasks such as environment-aware sensor placement optimization and autonomous vehicle path planning. Conventional methods rely on computationally expensive while accurate numerical solvers, which are not scalable for large-scale or real-time applications. Although deep learning-based surrogate models can accelerate these computations, they often suffer from limitations such as fixed-resolution constraints or dependence on explicit partial differential equation formulations. These issues hinder their applicability and generalization across diverse environments. We propose Hankel-FNO, a Fourier Neural Operator (FNO)-based model for efficient and accurate acoustic charting. By incorporating sound propagation knowledge and bathymetry, our method has high accuracy while maintaining high computational speed. Results demonstrate that Hankel-FNO outperforms traditional solvers in speed and surpasses data-driven alternatives in accuracy, especially in long-range predictions. Experiments show the model's adaptability to diverse environments and sound source settings with minimal fine-tuning.