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.
Original language | English |
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Pages (from-to) | 351-364 |
Number of pages | 14 |
Journal | Computational Geosciences |
Volume | 26 (2022) |
Issue number | 2 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Deep Learning
- Litho-type classification
- Seismic inversion