Abstract Ground roll in land seismic data is characterized by strong spatial coherence and long-range propagation, which pose persistent difficulties for effective signal preservation using conventional filtering techniques. Such noise components commonly exhibit large-scale structured patterns, requiring denoising methods capable of capturing extended spatial context. In this study, a Squeeze-and-Excitation Dilated UNet is employed for seismic ground roll attenuation. By incorporating dilated convolutions into the UNet architecture, the receptive field is effectively expanded without increasing the number of network parameters, enabling improved representation of long-range ground roll features. In addition, channel-wise attention is introduced to adaptively reweight feature responses, thereby enhancing the discrimination between effective reflections and different types of coherent noise. The performance of the adopted approach is evaluated on both synthetic and field seismic datasets and compared with the conventional UNet. The results demonstrate that the SE-Dilated UNet achieves superior ground roll attenuation while preserving effective seismic signals with reduced signal leakage.
Li et al. (Thu,) studied this question.