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Extracting Induced and Remanent Magnetizations From Magnetic Data Modeling

机译:从磁数据建模中提取和再现磁化

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To investigate the crustal magnetic structure, it is important to assess the susceptibility and remanence properties of rocks and ores. In this paper, we propose a method to extract the contributions of induced and remanent magnetization from modeling of magnetic anomalies. We first estimate the direction of the total magnetization vector by studying the reduced-to-pole anomaly and its correlation with different magnitude magnetic transforms. Then we invert the magnetic data to obtain the volumetric distribution of the magnetization intensity. As the third step, based on a priori information about the Koenigsberger ratio derived from petrophysical measurements, we extract the distributions in the source volume of the induced and remanent magnetization intensities, based on a generalized relationship involving the total and remanent magnetizations, and the true susceptibility. In this way, we are able to produce separate maps of the anomaly fields attributed to the physical magnetic source parameters: remanent and induced magnetization. After validating the method with synthetic data, we analyze the data relative to the Mesozoic and Cenozoic igneous rocks in Yeshan region, eastern China. The analysis of the separated magnetization components reveals that the intrusion of dioritic and basaltic rocks occurred at different geological periods, and the basaltic rocks were magnetized by a reversed geomagnetic field. The uncertainty analysis shows that a larger Koenigsberger ratio is beneficial to extract more reliable remanence and susceptibility information.
机译:为了研究地壳磁性结构,重要的是评估岩石和矿石的易感性和剩磁特性。在本文中,我们提出了一种方法来提取磁异常建模诱导和再现磁化的贡献。我们首先通过研究减少对极异常及其与不同幅度磁变换的相关性来估计总磁化载体的方向。然后,我们反转磁数据以获得磁化强度的体积分布。作为第三步骤,基于关于来自岩石物理测量的Koenigsber符号的先验信息,我们基于涉及总和剩余磁化的广义关系,提取诱导和剩磁磁化强度的源量中的分布,以及真实的关系易感性。通过这种方式,我们能够生成归因于物理磁源参数的异常字段的单独映射:再现和诱导磁化。验证了合成数据的方法后,我们分析了中国东部yeshan地区中生代和新生代火岩的数据。分离的磁化组分的分析表明,在不同的地质时期发生了Dioritic和玄武岩岩石的侵入,并且玄武岩岩石被逆转的地磁场磁化。不确定性分析表明,较大的Koenigsberger比率有利于提取更可靠的遗弃和敏感性信息。

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