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This study addresses dispersion in the convolutional perfectly matched layer (CPML) for low-frequency electromagnetic (EM) modeling. Researchers found dispersion relates to real stretch (κ) and proposed strategies to suppress it for better EM simulations.

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Area of Science:

  • Geophysics
  • Computational Electromagnetics
  • Numerical Modeling

Background:

  • Accurate forward modeling is crucial for interpreting low-frequency crosswell electromagnetic (EM) data.
  • The finite-difference time-domain (FDTD) method is widely used for EM simulations.
  • Perfectly matched layers (PML) are essential for absorbing outgoing waves in FDTD simulations, but can introduce dispersion.

Purpose of the Study:

  • To investigate and mitigate dispersion issues in the convolutional perfectly matched layer (CPML) implementation for low-frequency crosswell EM forward modeling.
  • To analyze the relationship between CPML parameters and wave propagation characteristics.
  • To propose an optimal parameter strategy for CPML to enhance absorption and reduce dispersion.

Main Methods:

  • Application of the finite-difference time-domain (FDTD) method.
  • Implementation of impulse sources and convolutional perfectly matched layer (CPML).
  • Derivation of the analytical solution for magneto-dipole impulse source radiation fields.
  • Numerical simulations using high- and low-frequency pulses to analyze dispersion and absorption.

Main Results:

  • Dispersion in CPML was observed and found to be positively related to the real stretch parameter (κ).
  • Dispersion was minimally affected by grid interval.
  • Analytical solutions and numerical simulations qualitatively and quantitatively elucidated dispersion laws.
  • An optimal parameter strategy for CPML was suggested based on established criteria for low-frequency pulses.

Conclusions:

  • The CPML method, by warping space-time, shows promise for ideal wave absorption in EM modeling.
  • While effective, completely eliminating dispersion in CPML remains challenging.
  • The findings provide insights for optimizing CPML parameters in low-frequency crosswell EM forward modeling.