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Ucan Osman N. Albora A. Muhittin Hisarli Z. Mümtaz 《Marine Geophysical Researches》2001,22(4):251-264
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. 相似文献
2.
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. 相似文献
3.
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. 相似文献
4.
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. 相似文献
5.
Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications. 相似文献
6.
Davut?AydoganEmail author Ali?Elmas A.?Muhittin?Albora Osman?N.?Ucan 《Marine Geophysical Researches》2005,26(1):1-15
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. 相似文献
7.
Application of Wavelet Transform to Magnetic Data Due to Ruins of the Hittite,Civilization in Turkey
A.?Muhittin AlboraEmail author Z.?Mümtaz Hisarli Osman N.?Ucan 《Pure and Applied Geophysics》2004,161(4):907-930
— In the recent years, geophysical methods have been applied successfully in archaeological studies. In this article we have studied the application of wavelet transform to magnetic data in order to estimate boundaries of various synthetic examples and real data. Enhanced Horizontal Derivative (EHD) method is also applied as an alternative method for boundary estimation. The performance of wavelet transform and the EHD method are evaluated using magnetic data of the Sarissa-Kusakli archaeological site. All boundary results are mutually compared. Based on these comparisons, we conclude that the wavelet transform provides reasonable results.This work was supported by the TUBITAK under Project YDABCAG-100Y021.Received: 23 October 2001 相似文献
8.
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. 相似文献
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