# A Shallow Seafloor Reverberation Simulation Method Based on Generative Adversarial Networks
###### Abstract
Subsequently, [PERSON] and [PERSON] directly derived the scattering functions of regular targets scattered on irregular surfaces and random undulated interfaces in free space based on [PERSON]'s theorem and established a unified model that included target scattering and interface reverberation in horizontal layered media [22]. Similar to the radiation problem, the scattered field can be regarded as the reradiation of a rough interface or an inhomogeneous medium. The form of the scattering field is described by the first-order approximation of the solution of the integral equation, which is called the Born approximation method. [PERSON] and [PERSON] used this method to solve the acoustic scattering problem of inhomogeneous media in the seafloor [23]. [PERSON] then presented a unified model, including the scattering of undulated interfaces and inhomogeneous media [24]. A monostatic backscattering model [25] proposed by [PERSON] and [PERSON] has nine input parameters and assumes no correlation between seafloor undulating-interface scattering and inhomogeneous-media scattering. [PERSON] et al. proposed a bistatic analytical model suitable for inclined seafloors [26]. The model considers that the seafloor reverberation comes from the omnidirectional three-dimensional sound-field scattering caused by the nonuniformity of the sound velocity and the density of the seafloor volume and interface, and then gives the analytical expression of the scattering field using the Born approximation. The reverberation simulation models mentioned above are mainly based on empirical scattering models or physical scattering models, in which the scattering functions of random scatters and irregular seabed surfaces are complex. The calculation cost of the mentioned models increases sharply as the simulation time increases and the factors considered increase, resulting in a low simulation efficiency. Furthermore, the high cost of sonar and oceanographic equipment makes the reverberation data collection more difficult, resulting in the scarcity of actual lake and sea trial reverberation data. Less data means that the researchers cannot fully study the reverberation characteristics, which causes data obstacles for the research of antireverberation technology and active sonar design. The GAN [27] was constructed by [PERSON]'s team, and the constructed model includes two subneural network models, namely, generative network and discriminative network models. The two subneural networks are trained to reach Nash equilibrium. The excellent generative ability of the GAN has attracted wide attention since its appearance,which has greatly promoted the development of unsupervised learning. As a hot research direction in the field of machine learning, the application of the GAN has been extended from the initial image generation [28] to various fields of computer vision [29; 30]. As the first adversarial neural network model widely used in real tasks, the superiority of the GAN has been demonstrated in several areas [31; 32]. The superiority of the GAN has inspired many researchers to carry out research on schemes combining with the GAN in their respective fields. Recently, GANs have shown progress from music generation to any short audio clip generation [33; 34]. The GANs have also been used to generate room impulse responses, which aims to augment high-quality RIRs using existing real RIRs [35]. In the field of underwater acoustic engineering, it should be theoretically feasible to use the GAN for active sonar reverberation data simulation to solve the problem of less reverberation data. Therefore, to solve the problem of less reverberation data, a GAN-based reverberation simulation method is proposed in this paper. The proposed method uses a small amount of actual reverberation data as reference samples to train the GAN, so as to obtain a larger amount of reverberation data.