Abstract

Tongchuan City, located in Shaanxi Province, northwest China, has limited groundwater resources. Rational planning and exploitation of groundwater are crucial to the sustainable development of the city, for which investigating the distribution of groundwater is the premise. Traditional resistivity sounding methods are often used to detect groundwater; however, these methods are not applicable in the study area where thick Quaternary loess is extensively distributed. In this study, we arranged five audio-frequency magnetotelluric (AMT) profiles to detect the deep clastic rock groundwater and carbonate karst fissure groundwater in Tongchuan. Firstly, we analyzed the electromagnetic interference (EMI) noises in Tongchuan City, revealing that the main EMI is power frequency interference (PFI). We used the dictionary learning processing technology to suppress the PFI. Secondly, the two-dimensional (2D) nonlinear conjugate gradient method was employed to invert a 2D electrical structure model for the area shallower than 1 km. We analyzed the characteristics of the electrical structure and its geological significance. Lastly, the three-dimensional (3D) electrical structure model of the study area was inverted using the 3D nonlinear conjugate gradient method, and the spatial distribution characteristics of the water-bearing strata were further analyzed. The results show that the PFI in urban environment can be suppressed by the dictionary learning processing technology. In Tongchuan city, the distribution of clastic rock fissure water is controlled by folds and faults, as well as the thickness of sandstone layers, and that of the carbonate karst fissure water is mainly controlled by faults. On this basis, we infer that the water-bearing areas are in the middle east and south of the study area.

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