AI-driven interpretation of mega seismic surveys for strike-slip faults and salt-related structures in Abu Dhabi

This paper presents an AI-driven workflow designed for the automatic interpretation of large seismic volumes, applied to a 15,000 km² offshore area in Abu Dhabi. The workflow begins with fault detection for low-magnitude, strike-slip faults, achieved through fine-tuning deep neural networks (DNNs). This fault detection process is enhanced by structure-preserving denoising to maintain critical features. Next, automated horizon interpretation and flattening reveal a variety of geological features that are otherwise difficult to identify. Finally, structure-enhancing denoising improves geological feature detection, using channel detection as an example. This workflow is set to be expanded to cover the entire offshore Abu Dhabi region, offering significant potential for large-scale geological analysis.