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Quantitative Interpretation of Self-Potential Anomalies of Some Simple Geometric Bodies

机译：一些简单几何体的自势异常的定量解释

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摘要

We have developed a new numerical method to determine the shape (shape factor), depth, polarization angle, and electric dipole moment of a buried structure from residual self-potential (SP) anomalies. The method is based on defining the anomaly value at the origin and four characteristic points and their corresponding distances on the anomaly profile. The problem of shape determination from residual SP anomaly has been transformed into the problem of finding a solution to a nonlinear equation of the form q = f (q). Knowing the shape, the depth, polarization angle and the electric dipole moment are determined individually using three linear equations. Formulas have been derived for spheres and cylinders. By using all possible combinations of the four characteristic points and their corresponding distances, a procedure is developed for automated determination of the best-fit-model parameters of the buried structure from SP anomalies. The method was applied to synthetic data with 5% random errors and tested on a field example from Colorado. In both cases, the model parameters obtained by the present method, particularly the shape and depth of the buried structures are found in good agreement with the actual ones. The present method has the capability of avoiding highly noisy data points and enforcing the incorporation of points of the least random errors to enhance the interpretation results.
机译：我们已经开发了一种新的数值方法，可以根据残余自电势（SP）异常确定掩埋结构的形状（形状因子），深度，极化角和电偶极矩。该方法基于在原点和四个特征点上定义异常值以及在异常轮廓上的相应距离。由残余SP异常确定形状的问题已转化为寻找形式为q = f（q）的非线性方程的解的问题。已知形状，深度，极化角和电偶极矩可使用三个线性方程式分别确定。已经得出了球体和圆柱体的公式。通过使用四个特征点及其对应距离的所有可能组合，开发了一种从SP异常自动确定埋藏结构的最佳拟合模型参数的程序。将该方法应用于具有5％随机误差的合成数据，并在Colorado的现场实例中进行了测试。在两种情况下，通过本方法获得的模型参数，特别是掩埋结构的形状和深度都与实际参数非常吻合。本方法具有避免高噪声数据点并强制合并最小随机误差的点以增强解释结果的能力。

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