A COHERENCY METRIC TO COMPARE OPTIMALLY CLUSTERED SEISMIC DATA

Clustering is a well-known solution to deal with complex database features as an unsupervised machine learning technique. One of its practical applications is the selection of non-similar earthquakes for consequent analysis of structural models. In the present work, appropriate clustering of seismic data is searched via optimization. Silhouette value is penalized and used to define the performance objective. A stochastic search algorithm is combined with a greedy search to solve the problem for distinct sets of near–field and far-field ground motion records. The concept of coherency is borrowed from optics to propose a coherency metric for earthquake signals before and after being filtered by structural models. It is then evaluated for various cases of structural response-to-record and response-to-response comparisons. According to the results the proposed coherency detection procedure performs well; confirmed by distinguished structural response spectra between different clusters.

Keywords:

Coherency Index , clustering , soft computing , optimization , nonlinear dynamic analysis