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 attributes
Overview of meta-attributes and how to design them
Workflows for the computation of meta-attributes from seismic data
Case studies demonstrating the application of meta-attributes
Sets of exercises with solutions provided
Sample data sets available for hands-on exercises
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