Seismic inversion with deep learning: A proposal for litho-type classification

Silvia L. Pintea*, Siddharth Sharma, Femke C. Vossepoel, Jan C. van Gemert, Marco Loog, Dirk J. Verschuur

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.
Original languageEnglish
Pages (from-to)351-364
Number of pages14
JournalComputational Geosciences
Volume26 (2022)
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • Deep Learning
  • Litho-type classification
  • Seismic inversion

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