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Multi-directional derivation of self-potential/elevation gradient (Ce) maps-swirl procedure

机译:自向/高程梯度(Ce)映射-旋流过程的多方向推导

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摘要

The derivation of gradient maps in geophysics, particularly in the field of self-potential has the potential to improve our understanding on the source of a signal. Self-potential/elevation gradient maps are beneficial in significantly reducing the topographic effect. Manual calculation of the gradient for large data sets in two-dimensions is time consuming and highly dependent on the direction of the calculation. Automation of the calculation process has the potential to overcome the time and directional dependency problems. The derivation of gradient maps in the multi-direction improves the result and array based operators can perform the automatic calculations rapidly. Four different gradient calculation methods based on a new automatic array oriented procedure (swirl procedure) are discussed and tested with artificial and field data sets. These four methods can be simply defined by the number of data contributing to the calculation (full-swirl or limited-swirl procedures) and the mathematical operator (maximum value or mean value) used in the calculation. The mean value operator using the full-swirl procedure gave the most reliable result in terms of gradient range and accuracy. The swirl procedure can effectively perform the self-potential/elevation gradient calculations and it has a potential use in various applications.
机译:在地球物理学中,特别是在自电位领域中,梯度图的推导有可能增进我们对信号源的理解。自势/高程梯度图有利于显着降低地形影响。二维手动处理大型数据集的梯度非常耗时,并且高度依赖于计算的方向。计算过程的自动化具有克服时间和方向依赖性问题的潜力。在多方向上推导梯度图可以改善结果,基于数组的运算符可以快速执行自动计算。讨论了基于新的面向阵列的自动过程(漩涡过程)的四种不同的梯度计算方法,并使用人工和现场数据集进行了测试。可以通过有助于计算的数据数量(全旋流或受限旋流过程)和计算中使用的数学运算符(最大值或平均值)来简单定义这四种方法。使用全旋流程序的平均值算子在梯度范围和精度方面给出了最可靠的结果。旋流程序可以有效地执行自势/高程梯度计算,并且在各种应用中都有潜在的用途。

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