关键词: 海洋测量, 卷积神经网络, 双向长短期记忆神经网络, 随机噪声, 多波束

Abstract: [Purposes] Multi-beam bathymetry system has the advantages of high efficiency and high resolution in ocean depth measurement and terrain acquisition,but due to changes in sea conditions,sound velocity profile errors and equipment noise and other factors,bathymetry data often contains a large number of random noise and outlier values,which seriously restricts the quality of water depth data and the accuracy of subsequent terrain modeling.In view of the insufficient adaptability of traditional filtering and regular discrimination methods to complex noise,this paper proposes an intelligent denoising method applied to multi-beam bathymetry data. [Methods] This method is based on convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM)to model multi-beam water depth time series data,by learning the time series characteristics and spatial continuity of water depth changes,to achieve accurate prediction of normal water depth trends,and to achieve accurate prediction of normal water depth trends.The predicted residual is used as the implicit basis for noise discrimination and suppression,so as to complete the automatic removal of depth noise. [Findings] Experimental results show that compared with traditional denoising methods,the proposed method has better robustness and generalization capabilities in complex seafloor terrain and high-noise environments,can effectively reduce water depth measurement errors,improve the overall quality of multi-beam bathymetry data,and provides a new intelligent data processing idea for high-precision seabed topography mapping. [Conclusions] The CNN-BiLSTM model proposed in this article makes up for the limitations of a single filter.Compared with traditional threshold filtering and statistical filtering methods,this method does not require manual setting of complex parameters,can effectively suppress random noise while retaining real terrain details,and is suitable for large-scale multi-beam bathymetry data processing in complex underwater environments．

Key words: ocean survey, convolutional neural network, bidirectional long short-term memory, random noise, multi-beam