The electromagnetic (EM) method is commonly used in mineral exploration due to the method’s sensitivity to conductive targets. Controlled source audio-frequency magnetotellurics (CSAMT) is developed from magnetotelluric (MT) method with an artificial EM source to improve the signal amplitude. It has been used for mineral exploration for many years. In this study, we performed a case study of the CSAMT application for the Eagles-Nest lead–zinc (Pb–Zn) ore deposits in Jianshui, China. The Eagles-Nest deposit is located in southwest in China in forest-covered complex terrain, making it difficult to acquire the geophysical data. Based on the previous dual-frequency induced polarization (IP) results, we designed four profiles for the CSAMT data acquisition. After data processing and inversion, we mapped the subsurface resistivity distribution. From the CSAMT results, we inferred the location of the ore body, which was verified by the drilling wells. The Pb–Zn ore body was found at a depth between 373.70 m to 407.35 m in the well.

With the rapid growth of the economy and population, the desire to use groundwater supplies is increasing. In order to detect groundwater, conducting a geophysics survey is a common way to map the subsurface. This method can describe the subsurface’s physical properties. For example, the direct current (DC) resistivity method provides a resistivity map of the subsurface, in which the groundwater is normally located in low resistivity anomaly zones. However, the DC method needs to overcome the challenges of the urban environment, with its infrastructure and background noise. In this paper, we propose a pseudo-3D resistivity surveying method to solve these problems, and it is applied to situations of groundwater detection within the city environment, dam leakage, and drinking water. From the pseudo-3D resistivity surveying, we detected the low-resistivity anomaly in the area, which is interpreted as groundwater through borehole verification.

In a geophysical survey, one of the main challenges is to estimate the physical parameter using limited geophysical field data with noise. Geophysical datasets are measured with sparse sampling in a survey. However, the limited data constrain the geophysical interpretation. Traditionally, the field data has been interpolated using mathematical algorithm. In many cases, the estimated field data uncertainties are required to determine which earth models are consistent with the observations. A model-based data-estimation method can provide precise information for imaging and interpretation. The approach used in this paper is based on a stochastic partial differential equation, and it is employed to predict the geophysical data. With this statistical model-based approach, the sparse sample from a survey is used to estimate the underlying spatial surface, and it is assumed that the predicted geophysical data have the same probability density function as the observed data. Furthermore, this method can return the uncertainties of the prediction. Both the synthetic data and the gold mineral exploration field data cases illustrate that this approach leads to better results than traditional methods.

The Washan iron deposits in Ningwu district contain different magma-related genetic natures, including magmatic, magmatic–hydrothermal and hydrothermal types, and their ore-forming processes remain a subject of debate. To elucidate the ore-forming processes of iron ores from Washan, we present textural, major element analytical, and thermal data of magnetites from various ore bodies in Washan, probing the crystallization conditions and subsequent formation sequence of magnetites. SEM analysis with back-scattered electron (BSE) imaging reveals diverse magnetite textures, including mineral inclusions, exsolution lamellae, and recrystallization features, reflecting the transitional environment from magmatic to hydrothermal. Based on Ti, V, and Cr compositions of magnetite from different ore bodies, two distinct evolution trends of genetic processes are identified, including evolution paths from porphyry-type to IOA- and IOCG-type magnetite. High-resolution WDS mapping highlights the intensifying alterations during this process. Calculated magnetite crystallization temperatures among different types of magnetite range from 597 °C to 378 °C, suggesting a cooling trend from porphyry-type magnetite (~558 °C) to IOA-type magnetite (~515–439 °C) and IOCG-type magnetite (~378 °C). These results underscore the significant role of magma-derived hydrosaline liquids and vapors in the formation of iron ores from Washan, where variations in the salinity of ore-forming fluids lead to different evolutionary paths for subsequent generations of magnetite. The metallogenic model of the Washan iron deposit suggests that highly saline, iron-rich fluids connect the varying geneses of magnetite, transitioning from deeply formed porphyry-type magnetite to IOA- or IOCG-type magnetite generated in the subaerial zone.

Models of subsurface structures are important for successful deposit exploration, but are challenged by the need to integrate data from different geophysical methods. In the present study, we evaluated a method of joint inversion in which audio-magneto telluric (AMT) and dual frequency induced polarization (DFIP) data sets are inverted simultaneously to produce a consistent 2D resistivity model to show a clear image of subsurface structures. To achieve the objectives, we conducted AMT and DFIP surveys along the same survey line within the Dongjun lead–zinc deposit in inner Mongolia by measuring 31 AMT survey sites with a station spacing of 40 m on a 1440 m survey track and operated in fifty-three frequencies in the range of 1–10,400 Hz to record the resistivity distribution of subsurface to depths exceeding 800 m. The same survey setup up was applied to the DFIP method using a pole–dipole array configuration and operating frequencies of 4 Hz and 4/13 Hz. The two-dimensional (2D) model obtained from AMT data revealed distinct low-resistivity anomalies in the middle of the 2D inversion model. In contrast, the DFIP inversion model showed a high resistive body in the same region with relatively high percent frequency effect (PFE) indicating high chargeability. In response to the discrepancies observed in the separate 2D inversion models, we implemented a joint inversion for both the AMT and DFIP data sets. The joint inversion resistivity model shows surficial conducting bodies and a high conductive body along the profile with relatively high PFE, indicating high chargeability. The final joint inversion resistivity model clearly images the large silica alteration zone and the Pb-Zn mineralization. This study demonstrates the feasibility of a joint inversion methodology and highlights the value of integrating geophysical methods through joint inversion for enhanced characterization and exploration of lead–zinc ores.