# A Shallow Seafloor Reverberation Simulation Method Based on Generative Adversarial Networks
## 4 Conclusions  
This paper first introduces the importance of a reverberation characteristic study for the implementation of the reverberation suppression algorithms and presents one of the difficulties that needs to be overcome before the reverberation characteristic study: the acquisition of the reverberation signals. To address this difficulty, a reverberation simulation method based on the GAN is proposed for efficient reverberation signal study. First, the reverberation signal is simulated based on the traditional point-scattering model, and the reverberation characteristics are analyzed and summarized in various aspects. Then, the reverberation signal is simulated based on the GAN, and the reverberation characteristics of the simulated signal are also analyzed and summarized. The reverberation characteristics are also compared with those of the simulation signal obtained by the conventional point-scattering model. By comparing Figures 3 and 9, it can be seen that the reverberation simulation data based on the GAN have the same characteristics in the time domain as the reverberation simulation data obtained from the traditional point-scattering model, and they are all consistent with the time domain characteristics of the reverberation. By comparing Figures 4 and 14, it can be seen that the reverberation simulation data based on the GAN are similar to the reverberation simulation data obtained from the traditional point-scattering model in the frequency domain. The center frequencies are all 500 Hz, which satisfies the reverberation characteristics. By comparing Figures 5, 6 and 15, it can be seen that the reverberation simulation data based on the GAN and the traditional point-scattering  
Figure 17: Autocorrelation function of the GAN-generated reverberation signal. model are both close to the Gaussian distribution in the instantaneous amplitude distribution and close to the Rayleigh distribution in the envelope distribution, which satisfy the reverberation characteristics. By comparing Figures 7, 8 and 16, it can be seen that the reverberation simulation data based on the GAN and the traditional point-scattering model are similar in terms of correlation, and they both gradually weaken with time, which satisfy the reverberation characteristics. Therefore, it is theoretically feasible to use the GAN to generate reverberant signals. In terms of calculation cost, the main factors affecting the calculation cost of the point-scattering model are the emission signal period, ocean scatterer density, sampling frequency, etc. When any one of the above three factors is amplified, the overall calculation cost is often increased hundreds or thousands of times, so it is necessary to wait a long time for each reverberation simulation. Unlike the point-scattering model, the GAN-based reverberation simulation method requires a high computational effort only when the network is trained. Once the network is trained, the computational effort for each subsequent reverberation generation is negligible. Therefore, in comparison, the GAN-based reverberation simulation method is less computationally intensive. In terms of universality, the reverberation simulation method based on the point-scattering model is theoretically fixed and the parameters, such as the emission signal and the scatterer density, can only be slightly modified according to the different test conditions. However, the proposed GAN-based reverberation simulation method is different. As long as there is a small amount of the actual reverberation signal of the sea, the GAN can use these signals as samples to simulate the reverberation signals with a high degree of similarity. Therefore, in comparison, the proposed GAN-based reverberation simulation method has higher universality.