In this study, seismic events in the Edirne district (Turkey) and its vicinity have been investigated in order to discriminate earthquakes from quarry blasts. A total of 150 seismic events with Md ≤ 3.5 duration magnitude from a seismic activity catalog between 2009 and 2014 recorded by the Enez (ENEZ), Erikli (ERIK) and Gelibolu (GELI) broadband stations operated by Boğaziçi University, Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center were used in this study. The maximum S-wave and maximum P-wave amplitude ratio of vertical component velocity seismograms, power ratio (Complexity) and total signal duration of the waveform were calculated. Earthquakes and quarry blasts were discriminated using the linear discriminate function (LDF) and back propagation feed forward neural networks, an artificial neural network (ANN) learning algorithm, taking the determination coefficient and variance account values between these parameters into consideration. Eighty-one (54%) of the total 150 seismic events studied were determined to be earthquakes, and sixty-nine (46%) of them were determined to be quarry blasts. The LDF and ANNs methods were applied to the data in Edirne and its vicinity using a pair of parameters and were compared to each other for the first time. The accuracy of the methods are 95% and 99% for LDF and ANNs, respectively.

在这项研究中，对埃迪尔内地区（土耳其）及其附近地区的地震事件进行了调查，以区分采石场爆炸中的地震。由Boğaziçi大学，Kandilli天文台和地震研究中心运营的Enez（ENEZ），Erikli（ERIK）和Gelibolu（GELI）宽带站记录的2009年至2014年地震活动目录中的Md≤3.5持续时间震级的总共150次地震事件本研究使用了研究所地区地震海啸监测中心。计算了垂直分量速度地震图的最大S波和最大P波振幅比，功率比（复杂度）和波形的总信号持续时间。使用线性判别函数（LDF）和反向传播前馈神经网络来判别地震和采石场爆炸，一种人工神经网络（ANN）学习算法，其中考虑了这些参数之间的确定系数和方差值。在所研究的全部150个地震事件中，有81个（54％）被确定为地震，其中69个（46％）被确定为采石场爆炸。使用一对参数将LDF和ANNs方法应用于Edirne及其附近地区的数据，并首次进行了相互比较。对于LDF和ANN，该方法的准确性分别为95％和99％。其中69例（46％）被确定为石矿场爆炸。使用一对参数将LDF和ANNs方法应用于Edirne及其附近地区的数据，并首次进行了比较。对于LDF和ANN，该方法的准确性分别为95％和99％。其中69例（46％）被确定为石矿场爆炸。使用一对参数将LDF和ANNs方法应用于Edirne及其附近地区的数据，并首次进行了相互比较。对于LDF和ANN，该方法的准确性分别为95％和99％。