Automating fault detection in seismic data: integrating image processing with deep learning

Abstract

Fault interpretation in seismic images is crucial for identifying fluid accommodation and flow migration pathways in the oil and gas industry. Several algorithms have been developed to calculate seismic attributes which help identify faults. Despite these advancements, challenges still remain in fault interpretation due to the complexity of fault networks, noise, and quality of seismic data. Hybrid seismic attributes extracted through artificial neural networks can enhance fault interpretation. In the case of neural network-based approaches used for geological feature extraction, picking precise samples for training neural networks is vital. In this study, an innovative method based on the Shi–Tomasi corner detection algorithm has been introduced to automatically pick fault samples on seismic data to be used as input to deep neural networks to predict faults. The method has been tested on two field seismic images that were acquired at different surveys. The field examples indicate that the trained neural networks could give a precise and clear estimation of faults with different azimuths. This proves the proposed sampling method can effectively provide a high-quality training data set for deep neural networks to automatically predict faults from seismic data.