基于DWT-AU-Net的叠前规则噪声压制方法OA

Severe regular noise is prevalent in seismic data acquired in the loess tableland area of the Ordos Ba-sin,and its characteristics,such as velocity and frequency,vary dramatically with surface relief.Conventional regular noise suppression methods have difficulty efficiently suppressing all regular noise across a large area and are prone to causing inconsistencies in the signal-to-noise ratio and spatial attributes.Therefore,a DWT-AU-Net-based method for prestack regular noise suppression is proposed.First,the distribution characteristics of regular noise in seismic data are investigated,and its effects on seismic data quality are clarified.Second,a DWT-AU-Net deep-learning architecture suitable for seismic data processing is designed.The discrete wavelet transform is used to transform seismic data into a multiscale domain,thus effectively improving network training efficiency.Meanwhile,an attention mechanism is introduced to enhance the network's ability to identify and focus on global noise patterns.The proposed network model is then systematically trained using an improved loss func-tion,enabling efficient suppression of regular noise in prestack seismic data and improving the signal-to-noise ra-tio and overall quality of the seismic data.Experimental results demonstrate that,compared with conventional methods,the proposed method better preserves useful signals while suppressing regular noise,improving the signal-to-noise ratio and the spatial consistency of seismic attributes.This study provides a new approach to regular noise suppression for large volumes of seismic data in the Ordos Basin and has important theoretical and practical value.