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

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

Vrolijk, J. (TU Delft Applied Geophysics and Petrophysics)
Blacquière, G. (TU Delft Applied Geophysics and Petrophysics)

2021

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.

Aliasing
Artificial intelligence
Common receiver
Processing
Sampling

http://resolver.tudelft.nl/uuid:4016858b-a648-4a69-9c16-b8e2b9df57da

https://doi.org/10.1190/geo2020-0186.1

0016-8033

Geophysics, 86 (3), V185-V196

Accepted Author Manuscript

Institutional Repository

journal article

© 2021 J. Vrolijk, G. Blacquière