Comments on the Gravity and Magnetic Anomalies of Saros Bay using Wavelet Approach

In this paper, residual and regional gravity and magnetic anomalies of Saros Bay are separated using wavelet method. Wavelet is one of the modern, stochastic image approaches processing technique in electronics. The vertical, horizontal and diagonal components of wavelet output are evaluated simultaneously and an underground model is obtained by suitable cross-sections. Thus the geological and tectonic properties of Saros Bay are extracted. Our proposed model is confirmed by deep and shallow seismic researches of Turkish Petroleum Cooperation (TPAO). The South-East region of Saros Graben is formed by strike slip Ganos Fault and Anafartalar reverse Fault that lies on the east of Ganos Fault. On the Northwest region, there is strike slip component fault resulting in the Enez Graben. Here we detected two new oval type geological structure, both of them starting from the Eastern direction of Canakkale Dardanelles and but one of them continuing through Gelibolu Peninsuls, while the other tending to the Enez Graben. We think that these structures are the ruins of a very old sea in this region. This revised version was published online in November 2006 with corrections to the Cover Date.     

A New Approach for Border Detection of the Dumluca (Turkey) Iron Ore Area: Wavelet Cellular Neural Networks

Anomaly analysis is used for various geophysics applications such as determination of geophysical structure's location and border detections. Besides the classical geophysical techniques, artificial intelligence based image processing algorithms have been found attractive for geophysical anomaly analysis. Recently, cellular neural networks (CNN) have been applied to geophysical data and satisfactory results are reported. CNN provides fast and parallel computational capability for geophysical image processing applications due to its filtering structure. The behavior of CNN is defined by two template matrices that are adjusted by a properly supervised learning algorithm. After training stage for geophysical data, Bouguer anomaly maps can be processed and analyzed sequentially. In this paper, CNN learning and processing capability have been improved, combining Wavelet functions and backpropagation learning algorithms. The new architecture is denoted as Wavelet-Cellular Neural networks (Wave-CNN) and it is employed to analyze Bouguer anomaly maps which are important to extract useful information in geophysics. At first, Wave-CNN performance is tested on synthetic geophysical data, which are created by a computer environment. Then, Bouguer anomaly maps of the Dumluca iron ore field have been analyzed and results are reported in comparison to real drilling results.     

Forward Modeling with Forced Neural Networks for Gravity Anomaly Profıle

In this paper, we introduce a new method called Forced Neural Network (FNN) to find the parameters of the object in geophysical section respect to gravity anomaly assuming the prismatic model. The aim of the geological modeling is to find the shape and location of underground structure, which cause the anomalies, in 2D cross section. At the first stage, we use one neuron to model the system and apply back propagation algorithm to find out the density difference. At the second level, quantization is applied to the density differences and mean square error of the system is computed. This process goes on until the mean square error of the system is small enough. First, we use FNN to two synthetic data, and then the Sivas–Gürün basin map in Turkey is chosen as a real data application. Anomaly values of the cross section, which is taken from the gravity anomaly map of Sivas–Gürün basin, are very close to those obtained from the proposed method.     

Evaluation of tectonic structure of İskenderun Basin (Turkey) using steerable filters

In this paper, we demonstrate the effectiveness of steerable filters as a method of delineating the boundaries of subsurface geological structures. Steerable filters, generally used for edge detection on 2-D images, have the properties of band pass filters with certain directions and are applied to many image processing problems. We first tested the method on synthetic data and then applied it to the aeromagnetic data of İskenderun Basin and adjacent areas.İskenderun Basin is located in the Northeastern Mediterranean where African–Arabian and Anatolian plates are actively interacting. The basin fill records a complex tectonic evolution since the Early Miocene, involving ophiolite emplacement, diachronous collision of Eurasian and Arabian plates and subsequent tectonic escape related structures and associated basin formation. Geophysical investigations of the tectonic framework of İskenderun Basin of Turkey provide important insights on the regional tectonics of the Eastern Mediterranean and Middle East. In this study we show geological structures, which are responsible for the magnetic anomalies in İskenderun Basin and enlighten the structural setting of the Northeastern Mediterranean triple junction using steerable filters. We obtained a magnetic anomaly map of the region from the General Directorate of Mineral Research and Exploration as raw data and then evaluated this by steerable filters. We determined the magnetic anomaly boundaries for İskenderun Basin by using various types of steerable filters and correlated these to drilling data and seismic profiles from the Turkish Petroleum Corporation. The result of the steerable filter analysis was a clarified aeromagnetic anomaly map of İskenderun Basin. The tectonic structure of İskenderun Basin is divided into regions by an N–S trending oblique-slip fault defined by the steerable filter outputs. We propose a new tectonic structure model of İskenderun Basin and modify the direction of the East Anatolian Fault Zone. In our model, East Anatolian Fault Zone cross-cuts the basin as a narrow fault zone and continues towards the Cyprus arc.     

Modelling Potential Field Sources in the Gelibolu Peninsula (Western Turkey) Using a Markov Random Field Approach

In this study, a Markov Random Field (MRF) approach is used to locate source boundary positions which are difficult to identify from Bouguer gravity and magnetic maps. As a generalized form of Markov Chains, the MRF approach is an unsupervised statistical model based algorithm and is applied to the analysis of images, particularly in the detection of visual patterns or textures. Here, we present a dynamic programming based on the MRF approach for boundary detection of noisy and super-positioned potential anomalies, which are produced by various geological structures. In the MRF method, gravity and magnetic maps are considered as two-dimensional (2-D) images with a matrix composed of N₁ × N₂ pixels. Each pixel value of the matrix is optimized in real time with no a priori processing by using two parameter sets; average steering vector (θ) and quantization level (M). They carry information about the correlation of neighboring pixels and the locality of their connections. We have chosen MRF as a processing approach for geophysical data since it is an unsupervised, efficient model for image enhancement, border detection and separation of 2-D potential anomalies. The main benefit of MRF is that an average steering vector and a quantization level are enough in evaluation of the potential anomaly maps. We have compared the MRF method to noise implemented synthetic potential field anomalies. After satisfactory results were found, the method has been applied to gravity and magnetic anomaly maps of Gelibolu Peninsula in Western Turkey. Here, we have observed Anafartalar thrust fault and another parallel fault northwest of Anafartalar thrust fault. We have modeled a geological structure including a lateral fault, which results in a higher susceptibility and anomaly amplitude increment. We have shown that the MRF method is effective to detect the broad-scale geological structures in the Gelibolu Peninsula, and thus to delineate the complex tectonic structure of Gelibolu Peninsula.     

A New Approach to Gravity Anomaly Separation: Differential Markov Random Field (DMRF)

In this paper the authors introduce a novel approach to stochastic image processing, denoted as Differential Markov Random Field (DMRF), which has been applied to gravity anomaly separation problems. The advantages of the method are that it introduces only little distortion into the shape of the original image and that it is not affected significantly by factors such as the overlap power spectra of regional and residual fields. Testing of the proposed meth using synthetic examples gave excellent results.     

A new approach to the structural features of the Aegean Sea: Cellular neural network

In this study, structural features in the Aegean Sea were investigated by application of Cellular Neural Network (CNN) and Cross-Correlation methods to the gravity anomaly map. CNN is a stochastic image processing technique, which is based on template optimization using neighbourhood relationships of pixels, and probabilistic properties of two-Dimensional (2-D) input data. The performance of CNN can be evaluated by various interesting real applications in geophysics such as edge detection, data enhancement and separation of regional/residual potential anomaly maps. In this study, CNN is used in edge detection of geological bodies closer to the surface, which are masked by other structures with various depths and dimensions. CNN was first tested for (prismatic) synthetic examples and satisfactory results were obtained. Subsequently, CNN/Cross-Correlation maps and bathymetric features were evaluated together to obtain a new structural map for most of the Aegean Sea. In our structural map, the locations of the faults and basins are generally in accordance with the previous maps from restricted areas based on seismic data. In the southern and southeastern parts of the Aegean Sea, E–W trending faults cut NE–SW trending basins and faults, similar to on-shore Western Anatolia. Also, in the western, central and northern parts of the Aegean Sea, all of these structures are truncated by NE-trending faults.     

Lava stones from Neapolitan volcanic districts in the architecture of Campania region,Italy

Results of a research carried out on the lavas from Campi Flegrei and Somma-Vesuvius volcanic districts are reported here. The lavas have been widely employed, since Roman age, in several important monumental buildings of the Campania region, mainly in the town of Naples and in its province. They are classified as trachytes (Campi Flegrei products), tephri-phonolites and phono-tephrites (Somma-Vesuvius complex) from a petrographical point of view. Sampling was carried out from well-known exploitation districts. A substantial chemical difference between the products of the two sectors was confirmed, while petrophysical characterization evidenced similarity among the two different materials, although some differences were recorded even in samples coming from the same exploitation site.     

A view of tectonic structure and gravity anomalies of Hatay Region Southern Turkey using wavelet analysis

In this study, we demonstrate wavelet analysis as a method of delineating the boundaries of subsurface geological structures. We applied wavelet transform to Bouguer anomaly data of the Cilicia Region and adjacent areas. The residual anomalies are the results of various depth and other properties of buried structures on the gravity anomaly map. The superposition of anomaly of the underground structures with different properties such as depth, density and size, increases the complexity of the overall system. Thus the extraction of the desired properties from the anomaly map becomes difficult. Different separation techniques are used to solve this problem. Wavelet transform is one of these modern approaches. Here, we tested various types of wavelet transform modeling on synthetic examples and then applied them to the gravity anomaly map of the Cilicia Region in Turkey. We have detected the borders of the Hatay Region in southern Turkey and proposed the tectonic model of this real complex structure using the wavelet transformation. The most important result we have found is the presence of a triple junction near the Hatay region.     

Residual Separation of Magnetic Fields Using a Cellular Neural Network Approach

—?In this paper, a Cellular Neural Network (CNN) has been applied to a magnetic regional/residual anomaly separation problem. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behavior of the CNN is defined by the template matrices A, B and the template vector I. We have optimized weight coefficients of these templates using Recurrent Perceptron Learning Algorithm (RPLA). The advantages of CNN as a real-time stochastic method are that it introduces little distortion to the shape of the original image and that it is not effected significantly by factors such as the overlap of power spectra of residual fields. The proposed method is tested using synthetic examples and the average depth of the buried objects has been estimated by power spectrum analysis. Next the CNN approach is applied to magnetic data over the Golalan chromite mine in Elazig which lies East of Turkey. This area is among the largest and richest chromite masses of the world. We compared the performance of CNN to classical derivative approaches.