Gündüz Horasan

Lithospheric Structure of the Marmara and Aegean Regions, Western Turkey

We have simulated the waveforms of three aftershocks of the Izmit (17 August 1999) earthquake, with magnitudes greater than M 5.0, to determine the lithospheric structure of the Gulf of Izmit, Marmara region. Using the discrete wavenumber technique (Bouchon, 1981), different layered crustal models have been tested for the simulation of the waveforms, and the best crustal model was determined by a best-fit criterion between the observed and simulated seismograms. Our results indicate that the average thickness of the crust is 32 km and that Pn and S velocities are 8.0 and 4.60 km/sec, respectively, in the Gulf of Izmit, Marmara region. We also computed synthetic waveforms of the 21 April 2000 Denizli (Honaz) and the 9 July 1998 Izmir (Doganbey) earthquakes in the Aegean in order to compare the lithospheric structures of the Marmara and the Aegean regions. The Aegean region has an average crustal thickness of 33 km and Pn and S velocities of 7.85 and 4.53 km/sec, respectively. Although thicknesses of the crusts are comparable and suggest an approximately equal amount of E-W stretching in the Marmara and N-S stretching in the Aegean regions, a patchy midcrustal low-velocity zone exists in the Aegean. The upper-mantle Pn velocity variation between the Marmara and the Aegean regions is interpreted as the effect of a thinning continental lithosphere toward the active Aegean arc and the establishment of a consequent upper-mantle temperature gradient, increasing from the north to the south. Additionally, the Black Sea oceanic lithosphere that steeply dips southward beneath the Marmara region, as evidenced by the seismic tomographic results obtained by Gulen and Kuleli (1995), can contribute to the advective cooling of the upper mantle that causes relatively high Pn velocities in the Marmara region. Manuscript received 20 August 2000.

Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques

Abstract The purpose of this article is to demonstrate the use of feedforward neural networks (FFNNs), adaptive neural fuzzy inference systems (ANFIS), and probabilistic neural networks (PNNs) to discriminate between earthquakes and quarry blasts in Istanbul and vicinity (the Marmara region). The tectonically active Marmara region is affected by the Thrace-Eskisehir fault zone and especially the North Anatolian fault zone (NAFZ). Local MARNET stations, which were established in 1976 and are operated by the Kandilli Observatory and Earthquake Research Institute (KOERI), record not only earthquakes that occur in the region, but also quarry blasts. There are a few quarry-blasting areas in the Gaziosmanpasa, Catalca, Omerli, and Hereke regions. Analytical methods were applied to a set of 175 seismic events (2001–2004) recorded by the stations of the local seismic network (ISK, HRT, and CTT stations) operated by the KOERI National Earthquake Monitoring Center (NEMC). Out of a total of 175 records, 148 are related to quarry blasts and 27 to earthquakes. The data sets were divided into training and testing sets for each region. In all the models developed, the input vectors consist of the peak amplitude ratio (S/P ratio) and the complexity value, and the output is a determination of either earthquake or quarry blast. The success of the developed models on regional test data varies between 97.67% and 100%.

Clustering seismic activities using linear and nonlinear discriminant analysis

Identification and classification of different seismo-tectonic events with similar characteristics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude (Md) between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent parameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diaglinear and diagquadratic discriminant functions are investigated. Accuracy of methods with an additional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a various misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to complex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.

Classification of seismic events using linear discriminant function (LDF) in the Sakarya region, Turkey

The Sakarya prefecture is an interesting area with various seismicity types. This activity comes from earthquakes occurring at the North Anatolian Fault Zone and from a few quarry blast areas in the region. These quarry blast recordings produce errors in the determination of active faults and mapping of the microearthquake activity. Therefore, to recognize the tectonic activity in the region, we need to be able to discriminate between earthquakes and quarry blasts in the catalogues. In this study, a statistical analysis method (linear discriminant function) has been applied to classify seismic events occurring in the Sakarya region. We used 110 seismic events that were recorded by Sakarya University Seismic Station between 2012 and 2014. Time and frequency variant parameters, maximum S wave and maximum P wave amplitude ratio (S/P), the spectral ratio (Sr), maximum frequency (fmax), and total signal duration of the waveform were used for discrimination analyses. The maximum frequency (fmax) versus time duration of the seismic signal gives a higher classification percentage (94%) than the other discriminants. At the end of this study, 41 out of 110 events (44%) are determined as quarry blasts, and 62 (56%) are considered as earthquakes.