Machine learning for data driven sound propagation modelling

用于数据驱动的声音传播建模的机器学习

• 批准号: 2889679
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889679
• 关键词: Machine; learning; data; driven; sound

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.

声音对于跨越沟通，海洋生物学，防御和导航的水下活动至关重要。有效使用当前和未来的传感器系统需要深入了解动态海洋环境中的声音传播。当前的声音传播模型是有限的，缺乏洞察力，因此该项目旨在创建数据驱动的，物理上现实的模型，包括复杂的现象，例如内部波浪，湍流和散射。 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结构，海床，表面），电流，涡流和前部。很少研究与这些现象相关的机制，并且对数学和物理的观点保持不足；该领域的大多数研究本质上都是随机的，尽管这些模型提供了有用的预测能力，但本质上，它们无法对所涉及过程提供真正的物理见解。本项目将通过开发同时由数据驱动和身体上现实的模型来解决这些缺陷，以增强对复杂而批判的动态现象的理解，这些现象对声音的复杂和批判性动态现象具有很大的影响，这些现象会通过我们的海洋通过我们的海洋传播。为了有效而智能地估算任何声纳部署的声音传播。