CNN-Based Ringing Effect Attenuation of Vibroseis Data for First-Break Picking

Abstract

In the field of exploration geophysics, vibroseis system is one of the widely used seismic sources to acquire seismic data. “Ringing effect” is a common phenomenon in vibroseis data due to the limited frequency bandwidth of the vibroseis system, which degrades the performance of automatic first-break picking. In this letter, we proposed a deringing method for vibroseis data using a deep convolutional neural network (CNN). In this method, we use end-to-end network structure to obtain the deringed data directly and skip connections to improve model training performance and preserve the details of vibroseis data. For real vibroseis data processing, train data set is first generated from the data to be processed. We extract seismic wavelet and pseudoreflectivity series from real vibroseis data and use them to synthesize training data, which resembles real data. Pseudoreflectivity series with a broader frequency range is used as a training label. Experiments are conducted both on synthetic and real vibroseis data. The experiment results show that deep CNN-based method can attenuate the ringing effect effectively and expand the bandwidth of vibroseis data. The short-time average/long-time average ratio method for first-break picking also shows improvement on deringed vibroseis data.