Abstract
Seismic signals discrimination is a multidimensional problem since recorded events may vary in terms of type, location, energy, etc. Recently, two discrimination features based on instantaneous frequency (IF) were proposed by the Authors. The first of these features is determined by distribution of the signals’ first Intrinsic Mode Function’s (IMF) IF. The second one is a particular simplification of the previous one as it gives information about the most frequently occurring instantaneous frequency in the considered first IMF. In order to exhibit features’ potential in distinguishing of seismic vibration signals, one has to use clustering algorithms. The features were already subjected to k-means algorithm. In this paper we show results of agglomerative hierarchical clustering (AHCA) and compare it with outcomes of k-means. In order to test optimal number of clusters, method based on average silhouette was accomplished. The results are illustrated by analysis of real seismic vibration signals from an underground copper ore mine.
Highlights
Clustering is one of the subtle problems in seismic signals analysis, since the analyzed signals may differ in terms of different source mechanisms, seismic source locations, seismic moment, slip direction, energy of the seismic event, different blasting techniques, etc. [1,2,3,4]
The second discrimination feature, which is a specific simplification of the previous one, represents each seismic signal segment by its dominant frequency of 1st Intrinsic Mode Function (IMF)’s instantaneous frequency (IF)
Seismic vibration signals analyzed in this paper are acquired using a sensor independent to the commercial seismic system installed in the underground copper ore mine
Summary
Clustering is one of the subtle problems in seismic signals analysis, since the analyzed signals may differ in terms of different source mechanisms, seismic source locations, seismic moment, slip direction, energy of the seismic event, different blasting techniques, etc. [1,2,3,4]. A good discrimination feature is characterized by: the ability to differentiate between two signals from different clusters (neighbors) and the ability to identify signals from the same cluster (cotenants) as similar. When a discrimination method is applied, one has to be sure that representatives of the data are well separated by the proposed feature This problem is relatively easy to solve if the considered data set is small and clustering into 2 groups is performed, since both the number of decisions to be made their difficulty are small. FEATURES BASED ON INSTANTANEOUS FREQUENCY FOR SEISMIC SIGNALS CLUSTERING. In this paper we utilize 2 different clustering algorithms: above-mentioned -means, and agglomerative hierarchical (AHCA) [18] Comparison of both methods’ results enable us to evaluate features’ usefulness.
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