Comparison Between Unsupervised and Supervise Fuzzy Clustering Method in Interactive Mode to Obtain the Best Result for Extract Subtle Patterns from Seismic Facies Maps

Pattern recognition on seismic data is a useful technique for generating seismic facies maps that capture changes in the geological depositional setting. Seismic facies analysis can be performed using the supervised and unsupervised pattern recognition methods. Each of these methods has its own advantages and disadvantages. In this paper, we compared and evaluated the capability of two unsupervised methods Fuzzy c-means (FCM) and Gustafson Kessel (GK) and one supervised method Adaptive Neuro-Fuzzy Inference Systems (ANFIS) at revealing the presence of a channel system. The process is performed in an interactive scheme in the SeisART software to obtain the best output. The seismic facies analysis was conducted on a 3D seismic data set acquired at North Sea block F3. Based on the results, the GK method outperformed the other two methods in delineating the channel pattern.

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