Correcting numerical dispersion artifacts from Finite Difference solvers is a well-identified challenge in computational wave physics, but existing approaches evaluate only a restricted family of CNN-based architectures and have been applied exclusively to the elastic wave equation. We instantiate the Deep Finite Difference framework on two-dimensional anisotropic acoustic wave propagation, pairing a fourth-order Finite Difference proxy with a Pseudo-Spectral reference over 27,000 heterogeneous velocity fields. We benchmark twelve correction architectures, from linear regression to Fourier Neural Operators, under a unified 10-fold cross-validation protocol.

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