Deep-learning viscoelastic seismic inversion for mapping subsea permafrost

Marine seismic surveys can be used to map ice-bearing subsea permafrost on a large scale. However, present seismic processing technologies have limited capacity to image permafrost distribution at depth, mainly due to the low sensitivity of primary reflections and refractions to the velocity inversion found at the base of permafrost. Guided waves and multiples are more sensitive to the velocity variations below the top of permafrost, but they remain challenging to use in physics-based inversion approaches. A deep-learning-based seismic inversion has the potential to improve seismic imaging below the top of permafrost by automatically extracting information from all wave modes. We present a multi-input neural network to estimate seismic velocities from marine seismic data. The network is trained on synthetic data generated from representative distributions of the seismic properties of subsea permafrost. We show that our network can image large velocity contrasts and reversals in depth, typical of subsea permafrost. We use our network to estimate P- and S-wave velocity and Q-factor models from a seismic line in the Beaufort Sea. The neural network indicates highly discontinuous subsea permafrost with variable thickness in the area. Our work shows that deep-learning-based seismic inversion could become a cost-effective technology to map the distribution of subsea permafrost on a large scale and, more generally, high-velocity geological layers located in shallow waters.