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Workshop: Machine Learning and the Physical Sciences

Score-based Seismic Inverse Problems

Sriram Ravula · Dimitri Voytan · Elad Liebman · Ram Tuvi · Yash Gandhi · Hamza Ghani · Alex Ardel · Mrinal Sen · Alex Dimakis


We present a new family of score-based models designed specifically for seismic migration. We define a sequence of corruptions obtained by migration artifacts created by reverse time migration (RTM) as the number of measurements changes. Our network is conditioned on the number of source locations and refines the reconstructed image over an annealed sequence of steps. Experiments on synthetic seismic data show that we can reconstruct geological details using a very small number of sources. Our method produces significantly higher-quality images compared to posterior sampling using standard score-based generative models and supervised seismic migration baselines.

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