Multiple Events Detection in Seismic Structures Using A Novel U-Net Variant

Seismic data interpretation is a fundamental process in the pipeline of identifying hydrocarbon structural traps such as salt domes and faults. This process is highly demanding and challenging in terms of expert-knowledge, time, and efforts. The interpretation process becomes even more challenging when it comes to identifying multiple seismic events taking place simultaneously. In recent years, the technology trend has been directed towards the automation of seismic interpretation using advanced computational techniques and in particular deep learning (DL) networks. In this paper, we present our DL solution for concurrent salt domes and faults identification with very promising preliminary results obtained through applications to real world seismic data. The proposed workflow leads to excellent detection results even with small size training datasets. Furthermore, the resulting probability maps can be extended to even a larger number of structure types. Precisions of the order of more than 96% were obtained with real data when three types of seismic structures are present concurrently.