Physics-constrained deep learning for ground roll attenuation

Abstract

We have developed a method to combine unsupervised and supervised deep-learning approaches for seismic ground roll attenuation. The method consists of three components that have physical meaning and motivation. The first component is a convolutional neural network (CNN) to separate a seismic record into ground roll and signal, while minimizing the residual between the sum of the generated signal and ground roll from two subnetworks and the input seismic record. The second component creates a maximum separation of signal and ground roll in the f-k domain, by training a supervised classifier. The third component is a CNN mapping signal to ground roll, which overcomes the problem of finding appropriate masks in traditional methods. Each component in our method is closely related to and motivated by the wave characteristics of the ground roll. Test results on field seismic records demonstrate the effectiveness of combining these components in preventing signal leakage and removing ground roll from seismic data.