• Geophysics and Geophysical Exploration
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• v.21 no.1
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• pp.41-53
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• 2018

In the traditional seismic processing, multiple reflections are treated as noise and therefore they are eliminated during data processing. Recently, however, many studies have begun to consider multiples as signals rather than noise for seismic imaging. Multiple reflections can illuminate an area where primary reflections are not able to cover, thus it is allowed that a smaller number of shots and receivers are used for imaging large areas. In order to verify this, surface-related multiples were used for reverse-time migration (RTM), and then we compared the results with conventional RTM images which are generated from primary reflections. To utilize multiples, we separated multiples from whole seismic data using surface-related multiple elimination (SRME) method. Numerical examples confirmed that the migration using multiples can image wider area than the conventional migration, particularly in the shallow subsurface layers. In addition, the migration of multiples could eliminate the acquisition footprints.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

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.