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Application of normalized full gradient method to self potential (SP) data

机译：归一化全梯度法在自电位（SP）数据中的应用

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摘要

Recently, Normalized Full Gradient (NFG) method has widespread applications to natural potential fields, especially in gravity and magnetic. In this study, usage of NFG in Self-Potential (SP) data evaluation is tested. Results are compared to other SP interpretation methods. The NFG method is applied to synthetic and field SP data. As a consequence of application of the method to the anomalies of spherical, cylindrical and vertical sheet models, whose theoretical structures are explicit, the structures were found very close to their actual locations. In order to see the capability of the method in detecting the number of sources, NFG method was applied to different spherical models at different depths and locations. The least-squares inverse solution was applied to the same models and NFG method was found more powerful in detecting model structure. Sensitivity of NFG method for application to noisy data is also tested. An anomaly is generated by adding a random noise to two close sphere SP anomalies. The method seems to work for the two close spheres at high S/N ratio. Then, NFG method was applied to two field examples. The first one is the cross section taken from the SP anomaly map of the Ergani-Suleymankoy (Turkey) copper mine. The depth of the mineral deposit at that site was found about 38 m from the ground level. This result is well matched to previous studies. NFG was also applied to SP data from Seferihisar Izmir (Western Turkey) geothermal field and the location of the point source was determined. The field data of this site have already been modeled by the thermoelectric source (coupling) solution method. When these two methods are compared, they seem to support each other. It is concluded that the NFG method works perfectly when the structure model is simple. It is observed that natural potential sources close to earth's surface are identified by the method more accurately at greater harmonics, while deep sources are identified at lesser harmonics. It produces reasonable results for noisy multi-source models than the other parameter identification methods (inverse solution, power spectrum, etc.).
机译：最近，归一化全梯度（NFG）方法已广泛应用于自然势场，尤其是重力和磁场领域。在本研究中，测试了NFG在自我潜能（SP）数据评估中的使用。将结果与其他SP解释方法进行比较。 NFG方法应用于合成和现场SP数据。由于将该方法应用于理论结构清晰的球形，圆柱形和垂直薄板模型的异常，因此发现该结构非常接近其实际位置。为了了解该方法检测源数量的能力，将NFG方法应用于不同深度和位置的不同球形模型。最小二乘反解应用于相同的模型，并且发现NFG方法在检测模型结构方面更有效。还测试了NFG方法应用于嘈杂数据的敏感性。通过将随机噪声添加到两个近球SP异常中来生成异常。该方法似乎适用于高信噪比的两个封闭球体。然后，将NFG方法应用于两个现场实例。第一个是从Ergani-Suleymankoy（土耳其）铜矿的SP异常图上截取的横截面。在该地点发现的矿床深度距地面约38 m。这个结果与以前的研究非常吻合。 NFG还应用于Seferihisar Izmir（土耳其西部）地热田的SP数据，并确定了点源的位置。该站点的现场数据已经通过热电源（耦合）求解方法进行了建模。比较这两种方法时，它们似乎相互支持。得出的结论是，当结构模型简单时，NFG方法是理想的。可以观察到，在高次谐波下，该方法可以更准确地识别出接近地球表面的自然电位源，而在次谐波下，可以识别出深层的自然电位源。与其他参数识别方法（逆解，功率谱等）相比，它为嘈杂的多源模型产生了合理的结果。

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