Multiattribute analysis

With several attributes generated for a seismic project, it becomes desirable to integrate the information provided by these attributes so that they can be interpreted in a convenient and meaningful way.  For doing this, some kind of decision making will first need to be done to separate the wheat from the chaff, i.e. the useful features in the seismic attributes are picked up from the background information, for display or further computation.  Such decision making could be done either by a human interpreter in an interactive fashion or could be automated with the use of computing machines.  Fortunately, a broad range of tools exist that can be used for integrating the information provided by the seismic attributes.  These tools can be subdivided based on the mechanism that is adopted for decision making (figure below). Interactive decision making can be divided into visual and numerical techniques while machine learning techniques can be divided into supervised and unsupervised techniques.

Subdivision of multiattribute analysis tools based on decision analysis in terms of interpreter-driven attribute analysis or machine-learning attribute analysis.