Seismic inversion with deep learning
A proposal for litho-type classification
S. Pintea (TU Delft - Pattern Recognition and Bioinformatics)
S. Sharma (TU Delft - ImPhys/Computational Imaging)
Femke Vossepoel (TU Delft - Reservoir Engineering)
Jan van Gemert (TU Delft - Pattern Recognition and Bioinformatics)
Marco Loog (TU Delft - Pattern Recognition and Bioinformatics)
Eric Verschuur (TU Delft - ImPhys/Computational Imaging)
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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.