Deep Learning-Based Surface-Related Multiple Suppression in Shallow Arctic Seismic Data

Seismic data recorded in shallow water in the Arctic Ocean contain not only primary reflections but also surface-related multiples with strong amplitudes and short-period characteristics. These multiples generate false stratigraphic boundaries on stacked seismic sections, thereby reducing the accuracy of geological interpretation. Therefore, the attenuation of multiples is an essential step in seismic data processing for accurate geological interpretations. Recently, with the advancement of deep learning technology, research on suppressing surface-related multiples using deep learning networks (such as U-Net and stacked BiLSTM) has been actively proposed.

Firstly, surface-related multiple suppression algorithms using U-Net and stacked BiLSTM were applied to Arctic field data respectively. Each algorithm was designed to predict surface-related multiples by using input data that contained both primary reflections and surface-related multiples. Fractional Fourier transform (FrFT) and continuous wavelet transform (CWT), which represent time-series data in the time-frequency domain, were applied to synthetic data and used as input data feature for each network. Finally in order to suppress the surface-related multiples for seismic data in shallow depth Arctic Ocean, the proper methods (network architectures, input data feature) are suggested.

 

Acknowledgments

This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS-2023-00259633).