Abstract: In order to get objective and accurate dominant occurrence of rock mass texture plane, a Self-organized Cluster algorithm is used. The dot belongs to its closed clustering center according to their minimal spherical distance. The unit vector of gravity center is used as the new clustering center. The maximum component standard deviation controls the division, and the minimum spherical distance of clustering center controls the combination. The algorithm is realized by the computer programming. The method is applied to analyze the joint occurrence of underground water-seal cavern in Zhanjiang. By means of three groups of parameters with increasing convergence accuracy, the objective and accurate dominant occurrence, clustering group number, and the dot number in each group are obtained. The result shows that the group cohesion increases and the spherical distances of clustering center decreases. This method is facilitated to discriminate the spatial development of texture plane, makes up for the deficiency of the traditional plots method that cannot give the accurate partition and the shortcoming of the previous method that keeps constant clustering group as well as the complex principle and process.

Key words:  Self-organized Cluster  , rock mass , dominant texture plane , occurrence ,  underground cavern