Abstract
A numerical approach based on the hidden physics model to estimate the model parameters of elastic wave equations with the sparse and noisy data is presented in this paper. Through discretizing the time derivatives of elastic wave equations and placing the priors of the state variables as Gaussian process, the model parameters and structure of elastic wave equations are encoded in the kernel function of a multi-output Gaussian process. In the learning stage, a parameter bound constraint condition is incorporated to enforce the physical bound of the model parameters. The numerical results from several benchmark problems, including homogeneous media, layer media, anisotropic media, and homogeneous model with an inclusion, demonstrate the feasibility and performance of the hidden physics model.
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