Deep learning-based seismic data processing for attenuation of interference noise and deblending in the shot domain

Abstract

During seismic acquisition, reflected waves from the subsurface are recorded by sensors in seismic cables in the water column. The measured data are used to create images of the subsurface for the exploration of natural resources. To achieve good-quality images, unwanted energy needs to be separated, which is an essential task in seismic data processing. Historically, a tailored physics-based workflow consisting of multiple procedures would be implemented. This is usually costly in terms of manpower and use of computing resources.

To enhance processing automation and reduce costs, this thesis has studied deep learning to solve two fundamental problems in seismic data processing. The first is the removal of noise from other seismic surveys, termed seismic interference attenuation. The second is the so-called deblending, i.e., separating overlapping records caused by an increased shooting rate in our own survey to improve acquisition efficiency and/or data density. Also, some techniques to further develop the processing quality of the data-driven deep neural networks have been proposed. The latest iteration of our deep learning algorithms now delivers similar results to the best available conventional physics-based algorithms at a fraction of the compute time. This represents a significant step forward compared to state-of-the-art seismic data processing.