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A depth-progressive framework that achieves 3D VMB using 2D generative diffusion models through a depth-as-channel formulation, in which the depth dimension of each 3D velocity patch is mapped to the channel dimension of a 2D U-Net, which effectively propagates shallow velocity priors downward and produces high-quality...
Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology...
A conditional diffusion-based wavefield propagator that advances seismic wavefields recursively from one time step to the next, conditioned by a short history of recent wavefield time steps, the velocity model, and the wavefield time step index is introduced.
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