A new approach to seismic pattern recognition is proposed. The method uses fuzzy logic and the concept of cluster partitioning to aid interpretation of seismic data for oil and gas exploration. Identifying seismic responses that correlate closely with a hydrocarbon signature is an important part of seismic interpretation. For example, it is known that velocity and frequency are critical parameters for identifying rock porosity. Therefore, for detection of porous rocks, it is important to target seismic samples with velocity and frequency values witpin acceptable ranges, white discoenting samples with velocity and frequency values outside these ranges. Cluster partitioning allows these samples to be examined more closely in relation to the reservoir of interest.


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