Mapping Subsea Permafrost Distribution in the Canadian Beaufort Sea With Marine Seismic and Deep Learning

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Abstract
Subsea permafrost is widespread across the polar continental shelf, and mapping its distribution is crucial for assessing the possible impact of its degradation in response to global warming. While seismic techniques have been useful in determining the lateral extent of subsea permafrost in the Canadian Beaufort Sea, are limited when it comes to estimating its vertical extent. However, recent advances in deep learning inversion have enabled the generation of more reliable large‐scale, depth‐resolved velocity and attenuation models. In this study, we employ a multi‐input, multi‐output neural network (NN) to evaluate the distribution of subsea permafrost at a regional scale in the Canadian Beaufort Sea. Application of our method resulted in the velocity and attenuation estimation of 15 seismic lines collected on the outer continental shelf. Our results reveal a gradual reduction in ice content and bonding effects toward the shelf edge. Our models show for the first time the high spatial variability of subsea permafrost near the shelf edge, bringing new insights and constraints into the dynamics of ice formation. Our findings are confirmed by well logs tied to seismic inversion, and support previous studies on subsea permafrost distribution in the region. Our method resolves the thickness of the upper permafrost layer with greater accuracy than previous studies and provides a practical framework for regional‐scale detection of various permafrost conditions. Finally, our work opens the way to a larger‐scale automated reprocessing of existing Arctic multichannel seismic data that could help refine the distribution of subsea permafrost across the continental shelf.

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