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Abstract.The results of the application of an unsupervised learning (neural network) approach comprising a Self Organizing Map (SOM), to distinguish micro-earthquakes from quarry blasts in the vicinity of Istanbul, Turkey, are presented and discussed. The SOM is constructed as a neural classifier and complementary reliability estimator to distinguish seismic events, and was employed for varying map sizes. Input parameters consisting of frequency and time domain data (complexity, spectral ratio, S/P wave amplitude peak ratio and origin time of events) extracted from the vertical components of digital seismograms were estimated as discriminants for 179 (1.8 < M d < 3.0) local events. The results show that complexity and amplitude peak ratio parameters of the observed velocity seismogram may suffice for a reliable discrimination, while origin time and spectral ratio were found to be fuzzy and misleading classifiers for this problem. The SOM discussed here achieved a discrimination reliability that could be employed routinely in observatory practice; however, about 6% of all events were classified as ambiguous cases. This approach was developed independently for this particular classification, but it could be applied to different earthquake regions.

Abstract. Two unsupervised pattern recognition algorithms, k-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 < M < 3.0). We have compared two algorithms with classical multivariate linear/quadratic discriminant analyses. The total accuracies of the models for GMM, k-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.

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Soil gas radon activity measurements were made around the western section of the North Anatolian Fault Zone. In the study, the variation of radon concentration at 12 different locations along the fault line was monitored by using LR-115 (solid-state nuclear track detectors) detectors for 12-monthly periods. Twelve radon stations were determined in the study region, and in each station, LR-115 films were installed in the borehole of ∼50 cm. The recorded radon concentration varies from 29 to 7059 Bqm-3 with an average value of 1930 Bqm-3. The influence of meteorological parameters such as temperature, pressure, total rainfall and humidity on soil radon concentrations in the study area was also investigated. The positive and poor correlation was observed between average value of 222Rn concentration and temperature. There is a reverse proportion between radon level with other meteorological factors (humidity, pressure and rainfall). The results show that the measured soil gas radon activity concentration shows seasonal variation in a highly permeable sandy-gravelly soil with definite seasons without obvious long transitional periods. The summer (from June 2013 to September 2013) is characterised by 1.8 times higher average soil gas radon activity concentration (median is 2.372 kBqm-3) than the winter (from December 2012 to March 2013) (median is 1.298 kBqm-3).