Meta-attributes and Artificial Networking : A New Tool for Seismic Interpretation

Omtale

Applying machine learning to the interpretation of seismic data 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. Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data. Volume highlights include: Historic evolution of seismic attributesOverview of meta-attributes and how to design themWorkflows for the computation of meta-attributes from seismic dataCase studies demonstrating the application of meta-attributesSets of exercises with solutions providedSample data sets available for hands-on exercises 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.