A. Muhittin Albora

Separation of Bouguer anomaly map using cellular neural network

Abstract In this paper, a modern image-processing technique, the Cellular Neural Network (CNN) has been firstly applied to Bouguer anomaly map of synthetic examples and then to data from the Sivas–Divrigi Akdag region. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behaviour of the CNN is defined by two template matrices and a template vector. We have optimised the weight coefficients of these templates using the Recurrent Perceptron Learning Algorithm (RPLA). After testing CNN performance on synthetic examples, the CNN approach has been applied to the Bouguer anomaly of Sivas–Divrigi Akdag region and the results match drilling logs done by Mineral Research and Exploration (MTA).

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.

Separation of magnetic-field data using the differential Markov random-field (DMRF) approach

In this paper, the differential Markov random-field (DMRF) method is introduced and applied to the magnetic anomaly separation problem, in which residual anomalies are separated from a regional field. The DMRF method is an unsupervised statistical model-based learning approach that does not require prior knowledge. A data-adaptive program, based on the evaluation of noise and superimposed effects of various geologic structures, is presented by considering a statistical maximum a posteriori (MAP) criterion. The aim of our method is to capture the intrinsic properties of geologic structures and then to identify and hence understand the behavior of the observed magnetic-anomaly map. The magnetic-anomaly map is modeled using a 2D matrix. In the DMRF approach, each pixel of the matrix is evaluated considering neighboring pixels. In synthetic models, anomalies of magnetic dipoles are tested for different depths, orientation angles, and lengths. The DMRF method also is applied to the vertical magnetic-anomaly ma...

Investigation of fault lines in Iskenderun Gulf using Markov Random Fields

One of the important issue in Gravity and Magnetic prospecting is to separate regional and residual anomalies. Determination of this separation between them is also crucial. Markov Random Fields (MRF) method is used to distinguish these anomalies. The most important property of this method is to make use of stochastic characteristic in neighborhood and 2D view. MRF does not need any training before use. This method is first applied to magnetic anomaly map consisting of prisms. After application, MRF method gives satisfactory results. We use Iskenderun Gulf magnetic anomaly map to determined off-shore fault lines. According to our results, we reveal fault lines along Iskenderun Gulf.

Adaptive Exhaustive Search Algorithm (AESA) for gravity anomaly profile moddelling

In this paper, we introduce a new modelling technique called Adaptive Exhaustive Search Algorithm (AESA) to distinguish the density and to compute the accurate location in geophysical section respect to its gravity anomali. Proposed algorithm contains two step; the first one is Approximate Search Algorithm (ASA) which allows us to find the approximate location and the other one is Exhaustive Search Algorithm (ESA) that considers all probable model structures in the approximate region. ESA takes very long processing time. To shorten this time, here, we apply ASA, and thus, exhaustive search is more applicable. We use AESA algorithm for synthetic data, synthetic noisy data and real data from Siirt Bouguer anomali map in South-East Turkey. It is determined that the model obtained from the proposed algorithm is very close to the geological observations.