Abstract
This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data: (i) one in which we image the seismic data using a local Radon transform to obtain angle gathers, (ii) and another in which we start from the subsurface-offset gathers, based on correlations over the seismic data. Our results indicate that this single-step approach provides a faster alternative to the established pipeline while being convincingly accurate. We observe that adding the background model as input to the deep network optimization is essential in correctly categorizing litho-types. Also, starting from the angle gathers obtained by imaging in the Radon domain is more informative than using the subsurface offset gathers as input.
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The authors would like to thank the sponsoring companies in the Delphi Consortium for their support.
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The authors would like to thank the sponsoring companies in the Delphi Consortium for their support.
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Silvia L. Pintea and Siddharth Sharma contributed equally to this work.
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Pintea, S.L., Sharma, S., Vossepoel, F.C. et al. Seismic inversion with deep learning. Comput Geosci 26, 351–364 (2022). https://doi.org/10.1007/s10596-021-10118-2
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DOI: https://doi.org/10.1007/s10596-021-10118-2