<b>Applying machine learning to the interpretation of seismic data</b> <p>Seismic data gathered on the surface can be used to generate numerous seismic attributes that enable better understanding of subsurface geological structures and stratigraphic features. With an ever-increasing volume of seismic data available, machine learning augments faster data processing and interpretation of complex subsurface geology. <p><i>Meta-attributes and Artificial Networking: A New Tool for Seismic Interpretation</i> explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data. <p>Volume highlights include: <ul> <li>Historic evolution of seismic attributes</li> <li>Overview of meta-attributes and how to design them</li> <li>Workflows for the computation of meta-attributes from seismic data</li> <li>Case studies demonstrating the application of meta-attributes</li> <li>Sets of exercises with solutions provided</li> <li>Sample data sets available for hands-on exercises</li> </ul> <p>The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
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