Surface wave suppression through deformable convolutional wavelet transform network with residual dense blocks.

Surface wave suppression plays a vital role in enhancing the quality of reflection seismic exploration. Convolutional neural networks (CNNs) can adaptively learn the characteristics of effective signals and surface waves. However, CNN has limited receptive fields and cannot effectively reuse features. When surface waves and effective signals overlap heavily, CNN struggles to preserve effective signals effectively. In this paper, we propose a deformable convolutional wavelet transform network (DCWTN) with residual dense blocks to suppress surface waves. DCWTN contains three types of modules: (1) The deformable convolution module (DCM) is designed to expand the receptive field and enhance seismic events continuity. (2) The wavelet transform enhancement module (WTEM) combines a wavelet transform and a residual dense block to suppress surface waves. It performs multi-scale feature extraction on surface waves according to their time–frequency characteristics to recover detailed information on overlapping parts. (3) The residual dense convolution module (RDCM) is designed for feature enhancement and further refinement of the acquired features. Experimental results show that DCWTN retains more effective signals than four popular methods. [ABSTRACT FROM AUTHOR]