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Score-based Seismic Inverse Problems
Sriram Ravula · Dimitri Voytan · Elad Liebman · Ram Tuvi · Yash Gandhi · Hamza Ghani · Alex Ardel · Mrinal Sen · Alex Dimakis

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in Machine Learning and the Physical Sciences »

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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Author Information

Sriram Ravula (The University of Texas at Austin)
Dimitri Voytan (University of Texas at Austin)
Elad Liebman (SparkCognition Research)
Ram Tuvi (SparkCognition)
Yash Gandhi (SparkCognition)
Hamza Ghani (SparkCognition)
Alex Ardel (SparkCognition)
Mrinal Sen (University of Texas at Austin)
Alex Dimakis (University of Texas, Austin)

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