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Accurate soil structure models are crucial references for substation grounding system design. Typically, inversion algorithms are employed to obtain uniform or horizontal layered soil models based on measured apparent resistivity. However, soil resistivity outlier distribution areas can affect the accuracy of these inversion algorithms, particularly when these areas are near the surface. Traditional algorithms do not account for the outlier distribution of soil resistivity, leading to significant discrepancies between the calculated results of the design scheme and actual operation, thereby impacting the safety and economy of the grounding system. Therefore, this paper proposes an inversion method for soil structures with outlier distribution characteristics based on deep belief networks (DBNs). Firstly, we introduce a statistical criterion for identifying the outlier distribution characteristics of soil resistivity. Subsequently, we construct a database of soil models with outlier distribution characteristics to train the DBN. Finally, we verify the inversion accuracy of the optimal DBN using apparent resistivity data measured in a 220 kV substation and the Qinghai-Tibet Railway. The results demonstrate that the inversion accuracy of the method proposed in this paper is comparable to that of the traditional method for horizontally layered soil but exhibits a remarkable improvement of approximately 40% when dealing with soil apparent resistivity exhibiting outlier distribution characteristics.

Optimal Inversion Method for Composite Layered Soil Model Considering Outlier Dispersion