Seismic data analysis has been one of the most important tools for subsurface resource exploration and exploitation. However, the ever-growing demand for high-resolution imaging and interpretation has resulted in the significant explosion in seismic volume sizes. The consequent challenge ahead of us is the current capabilities and scalabilities of traditional data analysis approaches, and thus it is vital for exploring how newer algorithms and computational architectures can stand up to this challenge. The recent success of machine learning in audio, computer vision, image analysis, and a plethora of other applications inspires geoscientists with new alternative insights into connecting seismic data...
Introduction to special section: Machine learning in seismic data analysis
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Haibin Di, Tao Zhao, Vikram Jayaram, Xinming Wu, Lei Huang, Ghassan AlRegib, Jun Cao, Mauricio Araya-Polo, Satinder Chopra, Saleh Al-Dossary, Fangyu Li, Erwan Gloaguen, Youzuo Lin, Anne Solberg, Hongliu Zeng; Introduction to special section: Machine learning in seismic data analysis. Interpretation 2019;; 7 (3): SEi–SEii. doi: https://doi.org/10.1190/INT-2019-0609-SPSEINTRO.1
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