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Abstract

Bayesian inversion based on normal modes analysis is an essential approach to understanding the internal structure of the Earth and other planets. While the traditional Markov Chain Monte Carlo (MCMC) method provides a feasible approach to obtain the posterior probability density distributions of model parameters, it demands a considerable amount of forward calculation and is slow in terms of computational efficiency. In order to overcome the limitations of the MCMC method, a new approach to Bayesian inversion using generative neural networks has been proposed in this study, which can deliver outcomes equivalent in accuracy to those of the MCMC method with less sample learning. The study proposed three types of Bayesian inversion neural network models based on popular generative neural networks, including GAN, flow model, and energy model, and evaluated their accuracy and discreteness of inversion results. By using the generative neural network method based on actual lunar normal mode observation data, this study has provided posterior probability density distribution of one-dimensional density and velocity structure parameters of the Moon, along with the optimal parameter values.The results demonstrate the potential of using generative neural networks in Bayesian inversion and provide a new direction for future research in this field.