  Solving for the frequency-domain scattered wavefield via physics-informed
neural network (PINN) has great potential in seismic modeling and inversion.
However, when dealing with high-frequency wavefields, its accuracy and training
cost limits its applications. Thus, we propose a novel implementation of PINN
using frequency upscaling and neuron splitting, which allows the neural network
model to grow in size as we increase the frequency while leveraging the
information from the pre-trained model for lower-frequency wavefields,
resulting in fast convergence to high-accuracy solutions. Numerical results
show that, compared to the commonly used PINN with random initialization, the
proposed PINN exhibits notable superiority in terms of convergence and accuracy
and can achieve neuron based high-frequency wavefield solutions with a
two-hidden-layer model.
