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首页> 外文期刊>Pure and Applied Geophysics >Modelling Potential Field Sources in the Gelibolu Peninsula (Western Turkey) Using a Markov Random Field Approach
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Modelling Potential Field Sources in the Gelibolu Peninsula (Western Turkey) Using a Markov Random Field Approach

机译：使用马尔可夫随机场方法对格利博卢半岛（土耳其西部）的潜在场源进行建模

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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 N 1 × N 2 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.
机译：在这项研究中，使用马尔可夫随机场（MRF）方法来定位源边界位置，这些位置很难从布格重力图和磁图中识别出来。作为马尔可夫链的一种广义形式，MRF方法是一种基于无监督统计模型的算法，适用于图像分析，特别是在视觉图案或纹理检测中。在这里，我们提出了一种基于MRF方法的动态编程，用于对各种地质结构产生的嘈杂和叠加的潜在异常进行边界检测。在MRF方法中，重力图和磁图被视为具有由N 1 ×N 2 个像素组成的矩阵的二维（2-D）图像。通过使用两个参数集，无需事先处理即可实时优化矩阵的每个像素值；平均转向向量（θ）和量化水平（M）。它们携带有关相邻像素的相关性及其连接位置的信息。我们选择MRF作为地球物理数据的处理方法，因为它是用于图像增强，边界检测和二维潜在异常分离的无监督，有效模型。 MRF的主要好处是，平均转向矢量和量化级别足以评估潜在的异常图。我们已经将MRF方法与噪声实现的合成势场异常进行了比较。在找到令人满意的结果后，该方法已应用于土耳其西部格利伯卢半岛的重力和磁异常图。在这里，我们观察到了Anafartalar逆冲断层和Anafartalar逆冲断层西北的另一个平行断层。我们已经对包括侧向断层的地质结构进行了建模，这导致了较高的磁化率和异常振幅增量。我们已经表明，MRF方法可以有效地检测出格里伯卢半岛的大规模地质构造，从而勾勒出格里伯卢半岛的复杂构造构造。

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