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Genetic Algorithm inversion of geomagnetic vector data using a 2.5-dimensional magnetic structure model

机译:使用2.5维磁结构模型的地磁矢量数据遗传算法反演

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We propose a new inversion method for vector magnetic field data, which uses the Genetic Algorithm in a space domain calculation to determine the best-fitting 2.5-dimensional (2.5-D) structure. This 2.5-D model is composed of magnetic boundaries with arbitrary strike and magnetic intensity. Two numerical formulas combine to express this model. One of them is a two-dimensional magnetic structure expression for a realistically shaped magnetic layer, and the other is a magnetization contrast expression for magnetic boundaries of variable strike. We use a Genetic Algorithm as the computational technique that supports optimum solutions for magnetization, magnetic strike, and boundary location. In practice, calculations are more accurate in the space domain instead of the more conventional frequency domain because it better preserves the short wavelength components and the true geometry between magnetic sources and observation points even for uneven survey track lines. The above leads to high resolution in the inferred magnetization without the need of upward continuation, which is particularly useful for inverting near-bottom survey data. The code is designed to use smaller storage and less computational time. Its application to synthetic data illustrates the power of resolution and precision in interpreting the fine scale processes of mid-ocean ridge accretion.
机译:我们提出了一种新的矢量磁场数据反演方法,该方法在空间域计算中使用遗传算法来确定最适合的2.5维(2.5-D)结构。这个2.5维模型由具有任意冲击和磁强度的磁边界组成。两个数值公式结合起来表示该模型。其中一个是逼真的磁性层的二维磁性结构表达式,另一个是可变行程的磁边界的磁化对比度表达式。我们使用遗传算法作为计算技术,为磁化,磁冲击和边界位置提供最佳解决方案。实际上,在空间域中而不是在更常规的频率域中,计算更加准确,因为即使对于不平坦的测量轨迹线,它也可以更好地保留短波长分量以及磁源和观测点之间的真实几何形状。上面所述导致推断的磁化具有高分辨率,而无需向上连续,这对于反转近底测量数据特别有用。该代码旨在使用较小的存储空间和较少的计算时间。它在合成数据中的应用说明了在解释洋中脊隆积的精细尺度过程方面的分辨率和精度。

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