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

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. In this work we test various clustering methods for their applicability to the segmentation of seismic data. The project is structured in two phases: firstly on synthetic data and secondly on real seismic. In the first phase we created synthetic structural models of reef bodies, salt structures, channels, karst features, fault zones, and volcanic structures. These models were then forward modelled to generate synthetic seismic data that were used as input for testing of clustering algorithms.