Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Abstract

Ground roll is usually considered as a common linear noise in land seismic data. The existence of the ground roll often masks the effective reflection information of underground media, resulting in the deterioration of seismic data quality. Therefore, ground roll suppression is one of the main tasks in seismic data processing. A large number of previous studies have proved that the time-frequency signal processing method based on mathematical transformation has shown excellent performance in ground roll attenuation and still has development potential. Meanwhile, a convolutional neural network, as one of the popular deep learning technologies, has also been widely used in the field of seismic signal processing. In this paper, we combine the convolutional neural network with the time-frequency signal processing method based on mathematical transformation, that is, spatial domain synchrosqueezing wavelet transform, and propose a complete ground roll suppression workflow of shot gathers in spatial wavenumber domain, realizing high-precision and automatic ground roll removal. Field data examples show that compared with bandpass filtering, FK filtering, time domain synchrosqueezing wavelet transform, spatial domain synchrosqueezing wavelet transform and the convolutional neural network, the spatial domain synchrosqueezing wavelet transform convolutional neural network has achieved satisfactory results in effectively attenuating ground roll and retaining valid information.