Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines

Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub-surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U-net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state-of-the-art techniques, our model demonstrates superior performance, achieving a stable and validated $96\%$ intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining $90\%$ underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.