Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields

TL;DR

This paper introduces a Gabor-enhanced PINN framework for high-frequency wavefield simulation, improving accuracy and convergence speed by capturing oscillatory behaviors more effectively, validated on complex seismic models.

Contribution

The paper presents a simplified Gabor-PINN approach that maps input coordinates to a Gabor system, enhancing wavefield modeling without increasing training complexity.

Findings

Superior accuracy over traditional PINNs

Faster convergence in complex models

Enhanced robustness near boundaries

Abstract

Physics-Informed Neural Networks (PINNs) have gained increasing attention for solving partial differential equations, including the Helmholtz equation, due to their flexibility and mesh-free formulation. However, their low-frequency bias limits their accuracy and convergence speed for high-frequency wavefield simulations. To alleviate these problems, we propose a simplified PINN framework that incorporates Gabor functions, designed to capture the oscillatory and localized nature of wavefields more effectively. Unlike previous attempts that rely on auxiliary networks to learn Gabor parameters, we redefine the network's task to map input coordinates to a custom Gabor coordinate system, simplifying the training process without increasing the number of trainable parameters compared to a simple PINN. We validate the proposed method across multiple velocity models, including the complex Marmousi and Overthrust models, and demonstrate its superior accuracy, faster convergence, and better robustness features compared to both traditional PINNs and earlier Gabor-based PINNs. Additionally, we propose an efficient integration of a Perfectly Matched Layer (PML) to enhance wavefield behavior near the boundaries. These results suggest that our approach offers an efficient and accurate alternative for scattered wavefield modeling and lays the groundwork for future improvements in PINN-based seismic applications.