A Deep CNN Model for Ringing Effect Attenuation of Vibroseis Data
This addresses a domain-specific problem in geophysics for seismic data processing, with incremental improvements in deringing techniques.
The paper tackles the ringing effect in vibroseis data, which degrades first-break picking in exploration geophysics, by proposing a deep CNN model that effectively attenuates the effect and expands data bandwidth, with experiments showing improved first-break picking using the STA/LTA ratio method.
In the field of exploration geophysics, seismic vibrator is one of the widely used seismic sources to acquire seismic data, which is usually named vibroseis. "Ringing effect" is a common problem in vibroseis data processing due to the limited frequency bandwidth of the vibrator, which degrades the performance of first-break picking. In this paper, we proposed a novel deringing model for vibroseis data using deep convolutional neural network (CNN). In this model we use end-to-end training strategy to obtain the deringed data directly, and skip connections to improve model training process and preserve the details of vibroseis data. For real vibroseis deringing task we synthesize training data and corresponding labels from real vibroseis data and utilize them to train the deep CNN model. Experiments are conducted both on synthetic data and real vibroseis data. The experiment results show that deep CNN model can attenuate the ringing effect effectively and expand the bandwidth of vibroseis data. The STA/LTA ratio method for first-break picking also shows improvement on deringed vibroseis data using deep CNN model.