Deep Learning for Automated Salt Detection in Seismic Imaging for Safer Oil and Gas Exploration

Accurate detection of salt bodies from subsurface in seismic images is vital for the identification of hydrocarbon reservoirs in oil and gas exploration. The cconventional manual techniques are labour-intensive, time-consuming, and error-prone. These techniques may be effective but lack efficiency. We propose a deep learning method based with convolutional neural networks (CNNs) for automatic detection of salt bodies. Particularly, we utilize a U-Net model, which has been shown to achieve the best segmentation results in seismic imaging applications. The suggested model, learned from the TGS Salt Identification Challenge dataset, has an Intersection over Union (IoU) score of 0.57 and a test accuracy of 87.33%, which is significantly better than existing methods. The results demonstrate improved seismic interpretation efficiency, reducing processing time while maintaining high segmentation accuracy. Furthermore, our study explores the integration of cutting-edge techniques such as generative adversarial networks (GANs) and transfer learning to enhance model generalization. This work demonstrates the potential of deep learning to revolutionize oil and gas exploration, leading to industrial-scale application, despite computational complexity and data-related difficulties.