Machine learning for data driven sound propagation modelling

声音对于横跨通信、海洋生物、国防和导航的水下活动至关重要。要有效利用当前和未来的传感器系统，需要对动态海洋环境中的声音传播有深入的了解。当前的声音传播模型有限且缺乏洞察力，因此该项目寻求创建包含内波、湍流和散射等复杂现象的数据驱动的物理逼真模型。该项目旨在提高在海洋环境中运行的所有实体的声纳系统效率。该项目将开发一系列高保真数字双胞胎，能够封装一些影响声波在海洋环境中传播的关键动态现象，包括内波、多尺度结构热和时间变化和涨落、非光滑界面和边界(如半淹没结构、海床、表面)、海流、涡流和锋面的散射。与这些现象相关的机制很少被研究，了解也很少，特别是从数学和物理的角度；这一领域的大多数研究本质上是随机的，尽管这些模型提供了有用的预测能力，但就其本质而言，它们不能提供对所涉过程的真正物理洞察。本项目将通过开发同时由数据驱动和物理现实的模型来解决这些不足之处，以加强对复杂和关键的动态现象的了解，这些动态现象对声音在海洋中的传播产生重大影响。该项目的重点将是机器学习模型，这些模型可以成功地描述动态海洋环境中的声音传播，存在在物理模型中在计算或数学上难以表示的多尺度过程，以便高效和智能地估计任何声纳部署的声音传播。

Sound is crucial for underwater activities spanning communication, marine biology, defence, and navigation. Effective use of current and future sensor systems requires a deep understanding of sound propagation in dynamic ocean environments. Current sound propagation models are limited and lack insight, so this project seeks to create data-driven, physically realistic models encompassing complex phenomena like internal waves, turbulence, and scattering. The project aims to improve sonar system effectiveness for all entities operating in ocean environments.This project will develop a series of high-fidelity digital twins capable of encapsulating a number of critical dynamic phenomena, which affect the propagation of sound waves through ocean environments, including internal waves, multi-scale structural thermal and temporal variations and fluctuations, scattering by non-smooth interfaces and boundaries (e.g. semi-submerged structures, sea bed, surface), currents, eddies, and fronts. The mechanisms associated with these phenomena are rarely studied and remain poorly understood, particularly from a mathematical and physical perspective; the majority of studies in this area are stochastic in nature and, although these models provide useful predictive capability, by their very nature, they cannot offer real physical insight into the processes involved. The present project will address these deficiencies by developing models that are simultaneously data-driven and physically realistic in order to enhance the understanding of complex and critical dynamic phenomena that have substantial impact on the propagation of sound through our oceans.The focus of the project will be on machine learning models that can successfully characterise sound propagation in dynamic ocean environments, in the presence of multi-scale processes which are computationally or mathematically difficult to represent in physical models, in order to efficiently and intelligently estimate sound propagation for any sonar deployment.