Discrimination of earthquakes and quarries in Kula District (Manisa, Turkey) and its vicinity by using linear discriminate function method and artificial neural networks

Abstract

In this study, seismic events in Kula district (Manisa, Turkey) and its vicinity have been investigated and then natural and artificial seismic activities are discriminated. Total of 77 digital vertical component velocity seismograms of seismic activities with M-L <= 3.5 magnitude from seismic activity catalogs between 2009 to 2014 recorded by Manisa Kula (KULA) broadband station operated by Bogazici University, Kandilli Observatory and Earthquake Resarch Institute Regional Earthquake-Tsunami Monitoring Center (RETMC) were used in this study. The maximum S-wave and maximum P-wave amplitude ratio (Ratio) of vertical component velocity seismograms and power ratio for (1 and 12 sec.) (Complexity-C) and total signal duration (Duration) of the waveform were calculated. The earthquakes and the quarry blasts have been discriminated using linear discriminant function (LDF) and Back Propagation-Feed Forward Neural Networks (BPNNs) that is one of the learning algorithms at the artificial neural networks (ANNs) methods taking correlation between these parameters into consideration. 39 (51%) of the 77 seismic activities were identified as quarry blasts and 38 (49%) of them as earthquakes LDF and ANNs methods have been applied together for the first time for Ratio-C, Ratio-logS and Ratio-duration parameter pairs with the data of Manisa and surroundings, and earthquakes and quarry blasts have been distinguished from each other. LDF and ANNs methods were compared for each pair of parameters. Both of two methods are successful but the ANNs method has higher accuracy percentage values than LDF method when there is sufficient number of data. The accuracy percentages are different for a pair of Ratio versus C, for a pair of Ratio versus logS and for a pair of Ratio versus duration, respectively.