Diffraction denoising using self-supervised learning

Diffraction wavefield contains valuable information on subsurface composition through velocity extraction and sometimes anisotropy estimation. It can also be used for the delineation of geological features, such as faults, fractures and mineral deposits. Diffraction recognition is, therefore, crucial for improved interpretation of seismic data. To date, many workflows for diffraction denoising, including deep-learning applications, have been provided, however, with a major focus on sedimentary settings or for ground-penetrating radar data. In this study, we have developed a workflow for a self-supervised learning technique, an autoencoder, for diffraction denoising on synthetic seismic, ground-penetrating radar and hardrock seismic datasets. The autoencoder provides promising results especially for the ground-penetrating radar data. Depending on the target of the studies, diffraction signals can be tackled using the autoencoder both as the signal and/or noise when, for example, a reflection is a target. The real hardrock seismic data required additional pre- and post-autoencoder image processing steps to improve automatic delineation of the diffraction. Here, we also coupled the autoencoder with Hough transform and pixel edge detection filters. Along inlines and crosslines, diffraction signals have sometimes a similar character as the reflection and may spatially be correlated making the denoising workflow unsuccessful. Coupled with additional image processing steps, we successfully isolated diffraction that is generated from a known volcanogenic massive sulphide deposit. These encouraging results suggest that the self-supervised learning techniques such as the autoencoder can be used also for seismic mineral exploration purposes and are worthy to be implemented as additional tools for data processing and target detections.