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Application of ik/i-means and Gaussian mixture model for classification of seismic activities in Istanbul

机译：k -均值和高斯混合模型在伊斯坦布尔地震活动分类中的应用

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Two unsupervised pattern recognition algorithms, ik/i-means, and Gaussian mixture model (GMM) analyses have been applied to classify seismic events in the vicinity of Istanbul. Earthquakes, which are occurring at different seismicity rates and extensions of the Thrace-Eskisehir Fault Zone and the North Anatolian Fault (NAF), Turkey, are being contaminated by quarries operated around Istanbul. We have used two time variant parameters, complexity, the ratio of integrated powers of the velocity seismogram, and S/P amplitude ratio as classifiers by using waveforms of 179 events (1.8 iM/i 3.0). We have compared two algorithms with classical multivariate linear/quadratic discriminant analyses. The total accuracies of the models for GMM, ik/i-means, linear discriminant function (LDF), and quadratic discriminant function (QDF) are 96.1%, 95.0%, 96.1%, 96.6%, respectively. The performances of models are discussed for earthquakes and quarry blasts separately. All methods clustered the seismic events acceptably where QDF slightly gave better improvements compared to others. We have found that unsupervised clustering algorithms, for which no a-prior target information is available, display a similar discriminatory power as supervised methods of discriminant analysis.
机译：两种无监督模式识别算法 k -means和高斯混合模型（GMM）分析已用于对伊斯坦布尔附近的地震事件进行分类。土耳其以色雷斯-埃斯基谢希尔断裂带和北安纳托利亚断裂（NAF）延伸的地震发生率不同，地震正在伊斯坦布尔附近开展的采石场污染中。通过使用179个事件的波形（1.8 i> M <3.0），我们使用了两个时变参数，复杂性，速度地震图的积分功率之比和S / P振幅比作为分类器。我们将两种算法与经典多元线性/二次判别分析进行了比较。 GMM， k 均值，线性判别函数（LDF）和二次判别函数（QDF）的模型的总准确度分别为96.1％，95.0％，96.1％，96.6％。分别讨论了地震和采石场爆炸的模型性能。所有方法都将地震事件合理地聚集在一起，其中QDF与其他方法相比略有改善。我们发现，无监督的聚类算法（没有先验的目标信息可用）显示出与判别分析的监督方法相似的区分能力。

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