M. Ravasi, T. Selvan Pandurangan, N. Luiken

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Multi-Dimensional Deconvolution (MDD) is a versatile technique used in seismic processing and imaging to create ideal datasets deprived of overburden effects. Whilst, the forward problem is well defined for a single source, stable inversion of the MDD equations relies on the availability of a large number of sources, this being independent on the domain where the problem is solved, frequency or time. In this work, we reinterpret the cost function of time-domain MDD as a finite-sum functional, and solve the associated problem by means of stochastic gradient descent algorithms, where gradients at each step are computed using a small subset of randomly selected sources. Through synthetic and field data examples, we show that the proposed method converges more stably than the conventional approach based on full gradients. Therefore, it represents a novel, efficient, and robust approach to deconvolve seismic wavefields in a multi-dimensional fashion.

Original languageEnglish (US)
Title of host publication83rd EAGE Conference and Exhibition 2022
PublisherEuropean Association of Geoscientists and Engineers, EAGE
Number of pages5
ISBN (Electronic)9781713859314
StatePublished - 2022
Event83rd EAGE Conference and Exhibition 2022 - Madrid, Virtual, Spain
Duration: Jun 6 2022Jun 9 2022

Publication series

Name83rd EAGE Conference and Exhibition 2022


Conference83rd EAGE Conference and Exhibition 2022
CityMadrid, Virtual

Bibliographical note

Funding Information:
The authors thank KAUST for supporting this research. We are also grateful to Equinor and partners for releasing the Volve dataset.

Publisher Copyright:
Copyright© (2022) by the European Association of Geoscientists & Engineers (EAGE). All rights reserved.

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics


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