The field of geophysical signal processing is moving towards the development of more interpretable and reliable deep learning models. Researchers are focusing on creating models that can effectively separate signal from noise, and provide accurate reconstructions of subsurface structures. The use of disentangled representation learning and neural operators is becoming increasingly popular, as these approaches allow for more robust and interpretable results. Additionally, the integration of diffusion models and foundation models is being explored to improve the spectral representation of synthetic earthquake ground motion response. Overall, the field is trending towards the development of more sophisticated and accurate models that can handle complex geophysical data. Noteworthy papers include: Foundation Models For Seismic Data Processing, which critically examines the application of foundation models in seismic processing. Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response, which proposes an AI physics-based approach to generate synthetic ground motion.