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http://dx.doi.org/10.7582/GGE.2020.23.3.00168

Removal of Seabed Multiples in Seismic Reflection Data using Machine Learning  

Nam, Ho-Soo (KT Powertel, Strategic Product Planning Team)
Lim, Bo-Sung (Korea National Oil Corporation, Domestic Business Dept., Domestic Exploration Team)
Kweon, Il-Ryong (PODO Inc.)
Kim, Ji-Soo (Chungbuk National University, Dept. of Earth and Environment Sciences)
Publication Information
Geophysics and Geophysical Exploration / v.23, no.3, 2020 , pp. 168-177 More about this Journal
Abstract
Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.
Keywords
seabed seismic reflection data; multiples; normal-moveout correction; machine learning; U-Net model;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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