The detection of buried geometrical model parameters is vital to full interpretation of potential field data, especially that related to gravity and/or self-potential anomalies. This study introduced a proposed non-linear least-squares algorithm for solving a combined formula for gravity and self-potential anomalies due to simple geometric shapes. This proposed algorithm was relied upon delimiting the origin anomaly value and two symmetric anomaly values with their equivalent distances along with the anomaly profile in order to invert the buried geometry model parameters. After that, a root mean square error (μ-value) for each parameter value at different postulated shape factor was assessed. The μ-value was considered as a benchmark for detecting the true-values of the subsurface geometry structures. The efficacy and rationality of the proposed approach were revealed by numerous synthetic cases with and without random noise. Furthermore, the sensitivity analysis between shape factor and μ-value were investigated on synthetic gravity and self-potential data. It was evident that the inverted parameters were reliable with the genuine ones. This proposed method was tested on samples of gravity data and self-potential data taken from Senegal and USA. To judge the satisfaction of this approach, the results gained were compared with other available geological or geophysical information in the published literature.

掩埋的几何模型参数的检测对于完全解释势场数据至关重要，特别是与重力和/或自势异常有关的数据。这项研究介绍了一种拟议的非线性最小二乘算法，用于解决由于简单几何形状而引起的重力和自势异常的组合公式。提出的算法依赖于对原点异常值和两个对称异常值及其等效距离以及异常轮廓进行定界，以反转埋藏的几何模型参数。之后，评估在不同假定形状因子下每个参数值的均方根误差（μ值）。μ值被认为是检测地下几何结构真实值的基准。大量有或没有随机噪声的合成案例都揭示了该方法的有效性和合理性。此外，在合成重力和自势数据的基础上，研究了形状因子和μ值之间的敏感性分析。显然，反演参数与真实参数是可靠的。在从塞内加尔和美国获得的重力数据和自势数据样本上测试了该方法。为了判断该方法是否令人满意，将获得的结果与已发表文献中其他可用的地质或地球物理信息进行了比较。显然，反演参数与真实参数是可靠的。在从塞内加尔和美国获得的重力数据和自势数据样本上测试了该方法。为了判断该方法是否令人满意，将获得的结果与已发表文献中其他可用的地质或地球物理信息进行了比较。显然，反演参数与真实参数是可靠的。在从塞内加尔和美国获得的重力数据和自势数据样本上测试了该方法。为了判断该方法是否令人满意，将获得的结果与已发表文献中其他可用的地质或地球物理信息进行了比较。