Self-Supervised Ground-Roll Noise Attenuation Using Self-Labeling and Paired Data Synthesis

Abstract

Seismic exploration is a complex process that depends on different sources of information. An essential one is seismic imaging, and much of its interpretation performance relies on high-quality processing, which is currently still very dependent on prone-to-error human mediation. Automation of such processing steps is necessary to reduce the amount of time to treat seismic data - usually months - and improve the outcome overall quality by reducing the inherent subjectivity in the process. One of the most critical steps in seismic processing is noise suppression, and ground roll is one of the most challenging and everyday noises observed in seismic prestack data. In this article, we propose a self-supervised two-step approach to attenuate ground-roll noise in seismic prestack images. First, we detect ground-roll-affected area using convolutional neural networks, and then, we filter ground-roll noise in the detected area using conditional generative adversarial networks (cGANs). For each of these steps, we propose to build paired noisy/noise-free training sets with no supervision or reference data, hence creating a self-supervised pipeline for filtering ground-roll noise. Our two-stage approach enables noise suppression in the affected area while preserving the signal in unaffected areas. In addition, we propose to refactor conventional qualitative metrics in the industry into quantitative scores disregarding any reference data to evaluate ground-roll suppression for different geologies and report reliable results compared with expert filtering.