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Clustering of seismic attributes for automatic seismic interpretation - first tests on synthetic geological models

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Interpretation of seismic data is still a process that involves manual work. By applying clustering algorithms to seismic attribute data, it is possible to automate interpretation to a certain degree. The first applications of clustering for seismic interpretation date from the early 1980s (Seber, 1984). In this work Seber applied K-means clustering, which remains one of the main clustering algorithms applied to seismic data. Sabeti and Javaherian (2009) applied K-means clustering to synthetic and real seismic data in an attempt to determine facies changes. Self-organizing maps (SOM) are another important clustering algorithm that has been applied to seismic data (Kohonen, 1990; 2001). Taner (2001) used SOM clustering to subdivide a seismic data set into four lithology classes. Strecker and Uden (2002) and Roy and Marfurt (2010) used SOM-based clustering to describe channel systems. Both Taner (2001) and Strecker (2002) emphasized the importance of well information for calibrating the results. Barnes and Laughlin (2002) applied both K-means and SOM clustering to seismic sections and to a 3D seismic data set. In their work they found a good correlation between the results of K-means and SOM clustering. Although almost no other clustering methods have been applied to seismic data, Paasche and Tronicke (2007) used Fuzzy K-means on 3D GPR data and assumed this method might also be applicable to 3D seismic data.

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