Journal Article10.1016/j.camwa.2022.01.012
Optimal staggered-grid finite-difference method for wave modeling based on artificial neural networks
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TL;DR: In this paper , a model-driven least square method (model-driven LSM) is proposed to obtain corresponding optimal difference coefficients, rather than a uniform set of coefficients applied to all models.
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Abstract: • We improve the objective function of the LSM and obtain the optimal SGFD coefficients for a flexible range of wave numbers. • A new optimal ESGFD and ISGFD scheme based on ANNs architecture is proposed. • The SGFD-ANNs method is applied to wave equation modeling, numerical experiments show the utility of the method. High-order staggered-grid finite-difference methods have been widely used in wave equation modeling. Imprecise difference coefficients and inappropriate difference order may result in numerical dispersion. We extend least square method (LSM) objective function to a flexible wave band to obtain its difference coefficients. Our method (model-driven LSM) is based on flexible model parameters to obtain corresponding optimal difference coefficients, rather than a uniform set of coefficients applied to all models. To reduce the computation cost in parameter estimation, we introduce the architecture, training and application of artificial neural networks (ANNs) to evaluate the staggered-grid finite-difference order and coefficients efficiently. In ANNs, double loss functions are adopted to ensure the stable and efficient convergence of training. Several numerical experiments for both two-dimensional and three-dimensional wave modeling are provided to verify the efficiency of the proposed scheme.
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Citations
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