针对南海深远海海域基站覆盖较少，潮汐波浪影响水深测量,无法直接使用北斗卫星导航系统(BeiDou Navigation Satellite System,BDS)定位技术实时求解实时水深的问题,提出使用精密单点定位(Precise Point Positioning,PPP)和多波束(Multi-Beam System,MBS)测深作为样本实现不依赖基站获取实时精密水深数据。通过地球重力场模型(Earth Gravitational Model,EGM)以及MBS测深样本训练神经网络保存海底大地高拟合曲面,构建粒子群优化反向传播(Particle Swarm Optimization Back Propagation,PSO-BP)神经网络模型输出泥线大地高,对泥线和定位数据实时计算得到水深。尝试在南海某海域测试基于PSOBP神经网络方法的适应性,将拟合计算的水深和MBS测量的水深结果对比,表明应用PSOBP神经网络模型及PPP技术较EGM 2008方法显著降低了实时水深估计的误差。在 400 m 水深的测试点无验潮平均水深估计准确度可达±1 m,满足某海域内船舶的无测深设备的实时水深估计要求。研究成果可为未来南海及其他深远海域的航行安全及资源勘查提供重要支持。

 Due to the limited coverage of base stations in the deep-sea areas of the South China Sea,water depth estimation are affected by environmental factors such as tides and waves,making it impossible to directly use the BeiDou Navigation Satellite System(BDS) positioning technology for real-time water depth estimation.To overcome these challenges,this paper proposes a method that leverages Precise Point Positioning(PPP) function and multi-beam bathymetry data samples to achieve real-time precise water depth data acquisition without relying on base stations.The methodology involves training a neural network using the Earth Gravity Model(EGM) and multi-beam bathymetry data samples to preserve the seabed's geoid height fitting surface.A Particle Swarm Optimization Back Propagation(PSO-BP) network model is then developed to determine the geoid height at the mud line.Water depth is obtained by real-time calculation of the mud line and positioning data.To evaluate the adaptability of the proposed method,tests were conducted at a selected sea area of the South China Sea.By comparing the fitted water depth with the multi-beam measured water depth results,it shows that the application of the PSO-BP neural network fitting model and PPP technology significantly reduces the error in real-time water depth estimation compared to the EGM 2008 fitting method.Specifically,at a test site with a water depth of 400 meters,the accuracy of average water depth estimation without tidal measurements can achieve ±1 meter.This performance meets the realtime water depth estimation requirements for vessels operating in certain sea areas without depth measurement equipment.This study offers substantial support for navigation safety and exploration of resources in the South China Sea and other deep-sea areas.