Multi-realization seismic data processing with Deep Variational Preconditioners

Matteo Ravasi*

*Corresponding author for this work

Research output: Contribution to conferencePosterpeer-review


Geophysical data processing is traditionally treated as a deterministic-centric discipline. Seismic data are usually processed through a chain of algorithms, where a given output becomes the input of a subsequent processing step. Geophysicists have recently started to recognize the importance of quantifying uncertainties within each processing step, such that they could be used as input to subsequent imaging and inversion processes. We present a two-steps approach where a Variational AutoEncoder (VAE) is first trained to generate realistic samples of the input seismic data at hand; parametric Variational Inference is then utilized to optimize the parameters of the VAE latent distribution for a given observed data and modelling operator of interest, and sample multiple realizations from the optimized parametric posterior distributions. Seismic deblending as well as joint interpolation and wavefield separation are used to showcase the proposed methodology.

Original languageEnglish (US)
Number of pages5
StatePublished - Dec 14 2023
Event3rd International Meeting for Applied Geoscience and Energy, IMAGE 2023 - Houston, United States
Duration: Aug 28 2023Sep 1 2023


Conference3rd International Meeting for Applied Geoscience and Energy, IMAGE 2023
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geophysics


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