Seismic Events Discrimination Using a New ELVQ Clustering Model

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概要

• 論文の詳細を見る

• In this paper, the LVQ(Learning Vector Quantization)model and its variants are regarded as the clustering tools to discriminate the natural seismic events(earthquakes)from the artificial ones(nuclear explosions). The study is based on the six spectral features of the P-wave spectra computed from the short period teleseismic recordings. The conventional LVQ proposed by Kohenen[2]and also the Fuzzy LVQ(FLVQ)models proposed by Sakuraba[16]and Bezdek[2]are all tested on a set of 26 earthquakes and 24 nuclear explosions using the leave-one-out testing strategy. The primary experimental results have shown that the shapes, the number and also the overlaps of the clusters play an important role in seismic classification. The results also showed how an improper feature space partitioning would strongly weeaken both the clustering and recognition phases. To improve the numerical results, a new combined FLVQ algorithm is employed in this paper. The algorithm is composed of two nested sub-algorithms. The inner sub-algorithm tries to generate a well-defined fuzzy partitioning with the fuzzy reference vectors in the feature space. To achieve this goal, a cost function is defined as a function of the number, the shapes and also the overlaps of the fuzzy reference vectors. The update rule tries to minimize this cost function in a stepwise learning algorithm. On the other hand, the outer sub-algorithm tries to find an optimum value for the number of the clusters, in each step. For this optimization in the outer loop, we have used two different criteria. In the first criterion, the newly defined "fuzzy entropy" is used while in the second criterion, a performance index is employed by generalizing the fuzzy distance. The experimental results of the new model show promising improvement in the error rate, an acceptable convergence time, and also more flexibility in boundary decision making.

• 社団法人電子情報通信学会の論文
• 2000-07-25

著者

• NASSERY Payam

  E.E. Department, Amirkabir University of Tech.

• FAEZ Karim

  E.E. Department, Amirkabir University of Tech.

• Faez Karim

  E.e. Department Amirkabir Univ. Of Tech.

• Nassery Payam

  E.e. Department Amirkabir Univ. Of Tech.

関連論文

• Signature Pattern Recognition Using Moments Invariant and a New Fuzzy LVQ Model
• Seismic Events Discrimination Using a New ELVQ Clustering Model

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