By Cornelius T. Leondes

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This procedure is repeated for every class Ck that has not been discriminated yet. The rule L k that possesses the minimal probability of misclassification is chosen as the discrimination rule for this level. This procedure is repeated with the rest of the unclassified example sets iteratively, until all classes are discriminated. We shall give an example to illustrate the application of the abovedescribed rule learning procedure. Four representative seismic image regions having different seismic texture are chosen by the interpreter and are shown in Figure 9a.

Pattern Analysis and Machine Intelligence 8, pp. 538-550 (1986). 62. I. N. Venetsanopoulos, "Towards a Knowledge-Based System for Automated Geophysical Interpretation of Seismic Data (AGIS)", Signal Processing 13, pp. 229-253 (1987). 63. I. Pitas, E. N. Venetsanopoulos, "A Minimum Entropy Approach to Rule Learning from Examples", IEEE Transactions on Systems, Man and Cybernetics SMC-22, pp. 621-635 (1992). 64. I. Pitas, and C. Kotropoulos, "Texture Analysis and Segmentation of Seismic Images", in Proc.

Three adjacent binary traces. Obviously, all ls belong to the same event. The classical and the generalized definitions of run give the results shown in Figures 8b, 8c respectively. The generalized run identifies the region of ls as a single entity having run length 3, whereas the classical definition fails to do so. The run following algorithm is based on the following simple idea: GEOPHYSICAL IMAGEINTERPRETATION 25 1. A generalized run has to be followed, when overlapping between the part of ls of the current trace and the part of ls of the subsequent trace occurs.