Source deghosting of coarsely sampled common-receiver data using a convolutional neural network

Jan Willem Vrolijk*, Gerrit Blacquière

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

It is well known that source deghosting can best be applied to common-receiver gathers, whereas receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in the common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, that is, the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we have trained a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver-ghost wavefield to obtain the training data. Due to reciprocity, these training data are a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine-learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach can remove the source-ghost wavefield from the coarsely sampled common-receiver gathers.
Original languageEnglish
Pages (from-to)V185-V196
Number of pages12
JournalGeophysics
Volume86
Issue number3
DOIs
Publication statusPublished - 2021

Bibliographical note

Accepted Author Manuscript

Keywords

  • Aliasing
  • Artificial intelligence
  • Common receiver
  • Processing
  • Sampling

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