Vertical resolution enhancement of seismic data with convolutional U-net

Abstract

Resolution of seismic data represents the ability to identify individual features or details in a given image and the temporal (vertical) resolution is a function of the frequency content of a signal. Thus, in order to improve thin-bed resolution, broadening of frequency spectrum is required and it has been one of the major objectives in seismic data processing. In this paper, we present a data-driven machine learning (deep learning) technique for spectral enhancement. We introduce the basic methodology of our new spectral broadening technique first and then demonstrate the promising features of this method through synthetic and field data examples as a means of enhancing thin bed resolution.