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<p>In the present paper, we implement Physics-Informed Neural Networks (PINNs) to study the dispersion of SH-waves in an initially stressed sandy half-space. The traditional numerical methods for simulation studies of seismic wave propagation are computationally expensive. We thus propose the application of PINNs to efficiently solve the governing equations representing SH-wave propagation. In the PINN framework, the governing equation along with the initial and boundary conditions are embedded into the neural network’s loss function which is then minimized using Adam’s technique to optimize the network. The optimal architecture of the PINN framework is obtained by varying the number of hidden layers and neurons. The loss function values are also calculated at various epochs and presented graphically to analyze the convergence of the method. Further, the PINN framework is then utilized to explore how anisotropy, initial stresses, and the presence of sandiness influence the displacement and velocity of SH-waves and are illustrated graphically. Additionally, a three-dimensional graph is generated to illustrate the displacement of the wave as a function of spatial coordinates <i>x</i> and <i>z</i>, as well as time <i>t</i>. The framework is further applied to simulate SH-wave propagation in anisotropic sedimentary basin. The results demonstrate the PINN’s ability to capture anisotropy-induced changes in wave displacement and velocity.</p>

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Physics-informed neural networks for dispersion studies of SH-waves in an initially stressed sandy half-space

  • 期刊论文

  • 发表日期：

    2025年9月13日

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  • Tanishqa Shivaji Veer,
  • Vijay Kumar Kalyani,
  • A. Akilbasha,
  • Prashant Malavadkar

摘要

In the present paper, we implement Physics-Informed Neural Networks (PINNs) to study the dispersion of SH-waves in an initially stressed sandy half-space. The traditional numerical methods for simulation studies of seismic wave propagation are computationally expensive. We thus propose the application of PINNs to efficiently solve the governing equations representing SH-wave propagation. In the PINN framework, the governing equation along with the initial and boundary conditions are embedded into the neural network’s loss function which is then minimized using Adam’s technique to optimize the network. The optimal architecture of the PINN framework is obtained by varying the number of hidden layers and neurons. The loss function values are also calculated at various epochs and presented graphically to analyze the convergence of the method. Further, the PINN framework is then utilized to explore how anisotropy, initial stresses, and the presence of sandiness influence the displacement and velocity of SH-waves and are illustrated graphically. Additionally, a three-dimensional graph is generated to illustrate the displacement of the wave as a function of spatial coordinates x and z, as well as time t. The framework is further applied to simulate SH-wave propagation in anisotropic sedimentary basin. The results demonstrate the PINN’s ability to capture anisotropy-induced changes in wave displacement and velocity.