S U M M A R YWe present a new numerical technique for elastic wave modelling in 3D heterogeneous media with surface topography, which is called the 3D grid method in this paper. This work is an extension of the 2D grid method that models P-SV wave propagation in 2D heterogeneous media. Similar to the finite-element method in the discretization of a numerical mesh, the proposed scheme is flexible in incorporating surface topography and curved interfaces; moreover it satisfies the free-surface boundary conditions of 3D topography naturally. The algorithm, developed from a parsimonious staggered-grid scheme, solves the problem using integral equilibrium around each node, instead of satisfying elastodynamic differential equations at each node as in the conventional finite-difference method. The computational cost and memory requirements for the proposed scheme are approximately the same as those used by the same order finite-difference method. In this paper, a mixed tetrahedral and parallelepiped grid method is presented; and the numerical dispersion and stability criteria on the tetrahedral grid method and parallelepiped grid method are discussed in detail. The proposed scheme is successfully tested against an analytical solution for the 3D Lamb problem and a solution of the boundary method for the diffraction of a hemispherical crater. Moreover, examples of surface-wave propagation in an elastic half-space with a semi-cylindrical trench on the surface and 3D plane-layered model are presented.
In this paper, the synchronization problem for a class of generalized neural networks with interval time-varying delays and reaction-diffusion terms is investigated under Dirichlet boundary conditions and Neumann boundary conditions, respectively. Based on Lyapunov stability theory, both delay-derivative-dependent and delay-range-dependent conditions are derived in terms of linear matrix inequalities (LMIs), whose solvability heavily depends on the information of reaction-diffusion terms. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. The obtained synchronization results are easy to check and improve upon the existing ones. In our results, the assumptions for the differentiability and monotonicity on the activation functions are removed. It is assumed that the state delay belongs to a given interval, which means that the lower bound of delay is not restricted to be zero. Finally, the feasibility and effectiveness of the proposed methods is shown by simulation examples.
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