# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 1 Introduction  
The ocean contains abundant natural resources, and all countries are intensifying their development of the ocean and enhancing the maintenance of maritime rights and interests in those territorial waters. Future naval warfare will have a high degree of in-formatization. Obtaining information superiority on the battlefield is the basis for winning modern wars and defending maritime rights and interests. Since the Germans published their first paper on underwater acoustics in 1919 [1], people have gradually established a theoretical system of underwater acoustic modeling and constructed a classic underwater acoustic transmission loss model, mainly including the ray model (RM), normal model (NM), fast field program (FFP) model, parabolic equation (PE) model, and some hybrid algorithms [1; 2; 3]. Each hydroacoustic model algorithm has limitations in the applicable situations. For example, the NM and FFP models are suitable for distance-independent sound field calculations; the RM and PE model are suitable for distance-dependent sound field calculation. In addition, the RM is suitable for high-frequency calculations, and it is more difficult to handle low-frequency and caustic calculations; the PE model is suitable for low-frequency and narrow-angle calculations, and the calculation efficiency is not very high for high-frequency and deep-sea environments. In different marine environments,regional physical parameters and signal frequency ranges may change. It is necessary to select an adapted hydroacoustic model to calculate the most accurate hydroacoustic propagation loss prediction value. This is an urgent problem in the research of marine underwater acoustics. [PERSON] (1982, 1984) proposed an underwater acoustic transmission loss model matching scheme that classified five acoustic models according to the depth (deep and shallow), frequency (high and low), and the relationship between ocean environment and distance (related and unrelated) adaptation [3]. This method has no restrictions on frequency and water depth, has few judged features, and relies on a large amount of prior experience and expert knowledge for feature extraction. Based on the ray model and the parabolic equation model, [PERSON] and others [4] studied the energy propagation mode of ocean fronts and vortices and made more accurate predictions for four different ocean sound fields. [PERSON] and others [5] designed a system with the Kraken normal model as the core including the Bellhop ray model, the Scooter finite element model, and the Sparc time-domain stepping model to calculate several typical sound field transmission losses. The above forecasting method still relies on expert experience to realize it. However, the large dynamic changes of the marine environment make the hydrological conditions have complex time-space-frequency characteristics, and the underwater acoustic field also presents complicated time-space random fluctuations, the environment is uncertain, and the parameters are uncertain. In a dynamically changing environment, it is difficult for traditional methods that rely on expert experience to accurately extract features so that the acoustic model can effectively and adaptively match the changing marine environment in real time. Therefore, when predicting the underwater acoustic transmission loss, the adaptive matching of the underwater acoustic transmission loss model needs to find a new method. In recent years, in the context of big data and deep learning, deep learning has been successfully applied to multiple research fields such as speech recognition, image processing, wireless communication, medical treatment, and social decision-making [6; 7; 8], bringing new ideas in solving traditional underwater acoustic transmission loss prediction problems. The deep belief net (DBN) proposed by [PERSON] is a neural network containing multiple hidden layers [9]. Compared with shallow models, it can effectively avoid problems such as overfitting and insufficient classification capabilities [10; 11; 12].