Depth-progressive 3D seismic velocity model building via 2D generative diffusion models
Three-dimensional (3D) velocity model building (VMB) remains computationally challenging with conventional approaches and native 3D neural networks, largely due to the prohibitive memory and training cost of full 3D formulations. Therefore, we introduce 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. This allows the model to synthesize overlapping depth patches sequentially from shallow to deep while conditioning the prediction of the deeper patch on the shallower one, as well as on any available geophysica...
| Authors | Shijun Cheng, Randy Harsuko, Mohammad H. Taufik et al. |
|---|---|
| Primary category | physics.geo-ph |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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