Crustal Movements and Deformations in Eastern Turkey in Connection with the Van Earthquake (October 23, 2011, Мw = 7.2): Study from GPS Data

Izvestiya, Physics of the Solid Earth - Abstract—A devastating earthquake with magnitude Mw = 7.2 occurred in the Van region, Eastern Turkey, on October 23, 2011. This study analyzes the...     

Climate Oscillations in Southern Bulgaria and Solar Activity: Analysis of Annual Rings of Needle Samples

Geomagnetism and Aeronomy - Abstract—The main subject of the study is an analysis of time series of the width of annual rings of spruce and pine (D) that were obtained during the...     

Application of the finite element method to gravity data case study: Western Turkey

Gravity anomalies always include the total effects (combination of the structures which have different densities and depths) of the study area and beyond. And the well-known non-uniqueness of potential field modelling may lead to very different interpretation results.The finite element method (FEM), which has been used in potential field interpretation for decades, makes complex problems to be solved easily and accurately. The first step of FEM is to identify the elements and then to decide on the boundary of the solution space. In this step, the solution space is divided into elements. After determination of the geometrical structure of the solution space, the most suitable elements should be chosen for this geometrical structure. The agreement between the geometry and the elements is quite important for the convergence to the best possible solution.In this work, the methods of trend analysis, filtering, analytical continuation and FEM were applied to a theoretical model and to gravity data from western Turkey to produce the regional and the residual anomalies. The results were compared and it was found that the FEM produced more accurate results than other methods did.     

Revealing the deformational anomalies based on GNSS data in relation to the preparation and stress release of large earthquakes

The results of Global Navigational Satellite System (GNSS) observations in the regions of large earthquakes are analyzed. The characteristics of the Earth’s surface deformations before, during, and after the earthquakes are considered. The obtained results demonstrate the presence of anomalous deformations close to the epicenters of the events. Statistical estimates of the anomalous strains and their relationship with measurement errors are obtained. Conclusions are drawn about the probable use of local GNSS networks to assess the risk of the occurrence of strong seismic events.     

GIS-Oriented Database for the System Analysis and Prediction of the Geodynamic Stability of the Nizhne-Kansky Massif

Izvestiya, Atmospheric and Oceanic Physics - In 2019, the construction of an underground research laboratory for justifying the geoecological safety of the disposal of highly active radioactive...     

Inversion of Self Potential Anomalies with Multilayer Perceptron Neural Networks

This study investigates the inverse solution on a buried and polarized sphere-shaped body using the self-potential method via multilayer perceptron neural networks (MLPNN). The polarization angle (α), depth to the centre of sphere (h), electrical dipole moment (K) and the zero distance from the origin (x ₀) were estimated. For testing the success of the MLPNN for sphere model, parameters were also estimated by the traditional Damped Least Squares (Levenberg–Marquardt) inversion technique (DLS). The MLPNN was first tested on a synthetic example. The performance of method was also tested for two S/N ratios (5 % and 10 %) by adding noise to the same synthetic data, the estimated model parameters with MLPNN and DLS method are satisfactory. The MLPNN also applied for the field data example in ?zmir, Urla district, Turkey, with two cross-section data evaluated by MLPNN and DLS, and the two methods showed good agreement.     

The Velocities of Modern Horizontal Movements of Earth Crust in the South Sector of Yenisei Ridge According to GNSS Observations

Doklady Earth Sciences - The paper presents the results of GNSS observations, provided by the authors in 2010-2019 years, in the zone of contact of tectonic structures of the Siberian platform,...     

Interpretation of magnetic anomalies using a genetic algorithm

A genetic algorithm (GA) is an artificial intelligence method used for optimization. We applied a GA to the inversion of magnetic anomalies over a thick dike. Inversion of nonlinear geophysical problems using a GA has advantages because it does not require model gradients or well-defined initial model parameters. The evolution process consists of selection, crossover, and mutation genetic operators that look for the best fit to the observed data and a solution consisting of plausible compact sources. The efficiency of a GA on both synthetic and real magnetic anomalies of dikes by estimating model parameters, such as depth to the top of the dike (H), the half-width of the dike (B), the distance from the origin to the reference point (D), the dip of the thick dike (δ), and the susceptibility contrast (k), has been shown. For the synthetic anomaly case, it has been considered for both noise-free and noisy magnetic data. In the real case, the vertical magnetic anomaly from the Pima copper mine in Arizona, USA, and the vertical magnetic anomaly in the Bayburt–Sar?han skarn zone in northeastern Turkey have been inverted and interpreted. We compared the estimated parameters with the results of conventional inversion methods used in previous studies. We can conclude that the GA method used in this study is a useful tool for evaluating magnetic anomalies for dike models.     

Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey)

Artificial neural networks (ANN) have been used in a variety of problems in the fields of science and engineering. Applications of ANN to the geophysical problems have increased within the last decade. In particular, it has been used to solve such inversion problems as seismic, electromagnetic, resistivity. There are also some other applications such as parameter estimation, prediction, and classification. In this study, multilayer perceptron neural networks (MLPNN) and radial basis function neural networks (RBFNN) were applied to synthetic gravity data and Seferihisar gravity data to investigate the applicability and performance of these networks for the method of gravity. Additionally performance of MLPNN and RBFNN were tested by adding random noise to the same synthetic test data. The structure parameters, such as the depths, the density contrasts, and the locations of the structures were obtained closely for different signal-to-noise ratios (S/N). Bouguer data of Seferihisar area were analyzed by MLPNN and RBFNN to estimate depth, density contrast, and location of the structure. The results of MLPNN, RBFNN, and classical inversion method were compared for real data obtained from Seferihisar Geothermal area and similar structure parameters were obtained. The experiments show that in general RBFNN not only increases the speed of the training stage enormously, but also provides slightly better performance.     

Processing of earthquake catalog data of Western Turkey with artificial neural networks and adaptive neuro<Emphasis>-</Emphasis>fuzzy inference system

Turkey is one of several countries frequently facing significant earthquakes because of its geological and tectonic position on earth. Especially, graben systems of Western Turkey occur as a result of seismically quite active tensional tectonics. The prediction of earthquakes has been one of the most important subjects concerning scientists for a long time. Although different methods have already been developed for this task, there is currently no reliable technique for finding the exact time and location of an earthquake epicenter. Recently artificial intelligence (AI) methods have been used for earthquake studies in addition to their successful application in a broad spectrum of data intensive applications from stock market prediction to process control. In this study, earthquake data from one part of Western Turkey (37–39.30° N latitude and 26°–29.30° E longitude) were obtained from 1975 to 2009 with a magnitude greater than M ≥ 3. To test the performance of AI in time series, the monthly earthquake frequencies of Western Turkey were calculated using catalog data from the region and then the obtained data set was evaluated with two neural networks namely as the multilayer perceptron neural networks (MLPNNs) and radial basis function neural networks (RBFNNs) and adaptive neuro-fuzzy inference system (ANFIS). The results show that for monthly earthquake frequency data prediction, the proposed RBFNN provides higher correlation coefficients with real data and smaller error values.