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Physics-informed neural network for elastic wave-mode separation.

E A B Alves1, P D S de Lima1, D H G Duarte1

  • 1Universidade Federal do Rio Grande do Norte, Departamento de Física Teórica e Experimental, 59078-970 Natal-RN, Brazil.

Physical Review. E
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Summary
This summary is machine-generated.

Physics-informed neural networks (PINNs) effectively separate seismic P and S wave modes using a scalar Poisson equation. This method reduces computational cost and transverse wave leakage in elastic media analysis.

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Area of Science:

  • Geophysics
  • Computational Seismology
  • Machine Learning in Geosciences

Background:

  • Mode conversion in nonhomogeneous elastic media complicates accurate interpretation of physical properties.
  • Correct decomposition of seismic wave modes (P and S) is essential for various scientific applications.
  • Existing machine learning methods often rely on Helmholtz decomposition for mode separation.

Purpose of the Study:

  • To investigate the efficacy of a physics-informed neural network (PINN) for separating P and S wave modes.
  • To evaluate a novel scalar Poisson equation formulation for computational efficiency and scalability.
  • To demonstrate the method's performance in both homogeneous and nonhomogeneous elastic media.

Main Methods:

  • Implementation of a physics-informed neural network (PINN) to solve a scalar Poisson equation.
  • Application of the scalar formulation for dimensionally scalable reduction in computational cost compared to vector formulations.
  • Verification of the method using homogeneous and realistic nonhomogeneous elastic models.

Main Results:

  • The PINN successfully separated P and S wave modes with high accuracy.
  • The separated modes closely align with results from conventional numerical techniques.
  • The proposed scalar formulation demonstrated reduced transverse wave leakage.

Conclusions:

  • PINNs offer a computationally efficient and accurate approach for seismic wave mode separation.
  • The scalar Poisson equation formulation provides a scalable alternative to traditional vector methods.
  • This technique enhances the interpretation of physical properties in nonhomogeneous elastic media.