Medium-depth geothermal energy mainly consists of two types, hydrothermal and dry-heat rock. With huge resource reserves, its exploration and development are of great significance to the transformation of China’s energy structure and the realization of the goal of “Double-Carbon”. From the differences in the electrical characteristics between hydrothermal and hot dry rock geothermal resources, this project uses the numerical modeling of the wide field electromagnetic (WFEM) method as a tool to design a model of typical geothermal resources. Then, we summarize and analyze the detection capability of the WFEM by calculating the WFEM response under the conditions of different depths, resistivity values, thickness-to-depth ratios and other parameters. Moreover, aiming at the key problems faced by the WFEM in real applications, we discuss the factors affecting the detection accuracy and give solutions to improve the reliability of the detection results. Finally, the medium-depth geothermal energy detection in the Jiyang Depression of Shandong is presented as an example, showing how the WFEM detection technique was used with the inversion result profile obtained. By comparing the results of geomagnetic and seismic detection on the same survey line, it is demonstrated that the WFEM method has great potential in the exploration of medium-depth geothermal energy and can be the preferred method.

The static shift effect is a distortion in electromagnetic data that severely impacts exploration results. Traditional static effect correction methods are often ineffective, prone to overcorrection or undercorrection, and make it difficult to accurately assess the applicability of the correction. Furthermore, some correction processes require additional data, which increases correction costs. This paper first presents the theoretical foundation for correcting static shift effects in the electric field components using magnetic field component information. Based on time-frequency electromagnetic exploration technology, a method is proposed to correct static shift effects in the electric field by using simultaneously collected magnetic field data, aiming to address the distortion issues caused by static shift effects in the electric field and apparent resistivity. The method is validated through both theoretical models and field data, demonstrating its excellent correction performance. Additionally, the paper introduces the use of the multifractal spectrum analysis algorithm to analyze profile measurement points and study the fractal dimension characteristics of static shift effects, providing an effective way to evaluate the appropriateness and potential overcorrection of the correction. Finally, the multifractal features of field data are discussed, validating the ability of the multifractal spectrum to identify subsurface electrical complexity.

The static shift effect is a distortion in electromagnetic data that severely impacts exploration results. Traditional static effect correction methods are often ineffective, prone to overcorrection or undercorrection, and make it difficult to accurately assess the applicability of the correction. Furthermore, some correction processes require additional data, which increases correction costs. This paper first presents the theoretical foundation for correcting static shift effects in the electric field components using magnetic field component information. Based on time-frequency electromagnetic exploration technology, a method is proposed to correct static shift effects in the electric field by using simultaneously collected magnetic field data, aiming to address the distortion issues caused by static shift effects in the electric field and apparent resistivity. The method is validated through both theoretical models and field data, demonstrating its excellent correction performance. Additionally, the paper introduces the use of the multifractal spectrum analysis algorithm to analyze profile measurement points and study the fractal dimension characteristics of static shift effects, providing an effective way to evaluate the appropriateness and potential overcorrection of the correction. Finally, the multifractal features of field data are discussed, validating the ability of the multifractal spectrum to identify subsurface electrical complexity.