Image-based geologic interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geologic structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve near-human-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geologic interpretation in petroleum exploration and production. For example, a fault picking method applied to 3D seismic volume data using deep...
Introduction to special section: Machine learning for image-based geologic interpretation
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Jiajun Jiang, Tao Lin, Chaoshun Hu, Bulbul Ahmmed; Introduction to special section: Machine learning for image-based geologic interpretation. Interpretation 2022;; 10 (3): SEi–SEii. doi: https://doi.org/10.1190/INT-2022-0613-SPSEINTRO.1
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