Seismic deghosting using convolutional neural networks

Author:

Almuteri Khalid1ORCID,Sava Paul2ORCID

Affiliation:

1. Colorado School of Mines, Center for Wave Phenomena, Department of Geophysics, Golden, Colorado, USA. (corresponding author)

2. Colorado School of Mines, Center for Wave Phenomena, Department of Geophysics, Golden, Colorado, USA.

Abstract

Ghost reflections deteriorate the quality of seismic data in towed-streamer acquisition, and various acquisition and processing solutions have been proposed to remove them from seismic data. A common issue with the proposed solutions is their limited ability to remove source-side ghosts because of the sparse source sampling. Satisfactory receiver-side deghosting solutions are facilitated by complementary measurements (e.g., particle motion data) for wavefield separation and also can be achieved using pressure data acquired at a single recording level only. We develop a solution based on convolutional neural networks (CNNs) to remove source- and receiver-side ghosts in the shot domain. The solution does not require complementary measurements, i.e., it can remove ghost reflections in conventional pressure data measured at a single recording level. Our method requires knowledge of the acquisition geometry to create training data that replicate the field acquisition geometry and require the ocean floor bathymetry to be known. A CNN learns to map ghost-contaminated gathers to corresponding ghost-free gathers through an iterative training process. We find that the CNN-based deghosting operator can remove ghost reflections from previously unseen data and demonstrate that the solution generalizes well when training is done on models unrelated to the actual field geology.

Funder

Saudi Aramco

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

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