Abstract: Geophysical electromagnetic data inversion is a typical underdetermined problem. Due to the limitations of observational data, individual geophysical methods often suffer from significant non-uniqueness and uncertainty, making it challenging to provide accurate and stable interpretations of subsurface geological structures. To address this issue, joint inversion techniques have emerged as a key research direction in geophysical exploration. By integrating the advantages of different geophysical methods, joint inversion enhances the resolution and reliability of inversion results. In recent years, with the rapid advancement of computational technologies and novel numerical algorithms, various joint inversion approaches have been proposed and widely applied. These primarily include empirical coupling methods based on petrophysical properties, structural coupling methods utilizing spatial correlation constraints, and prior information-constrained methods based on Bayesian theory and fuzzy clustering, and so on. The core concept of these methods is to exploit the complementary information from different geophysical techniques to optimize the joint inversion objective function through petrophysical relationships, spatial gradient constraints, or probabilistic modeling. This reduces solution non-uniqueness and enhances the characterization of subsurface geological structures. Joint inversion techniques have demonstrated significant success in applications such as mineral exploration, energy resource detection, geological mapping, and deep structural studies.Despite their promising potential, joint inversion techniques still face several challenges in practical applications. These challenges include the rational construction of relationships between physical fields, the optimization of regularization strategies, the improvement of computational efficiency, and the handling of complex terrain effects. Future research in joint inversion will focus on the following aspects: (1) leveraging artificial intelligence and data-driven methods to learn nonlinear mappings between petrophysical parameters from large-scale training datasets, thereby improving inversion speed and accuracy; (2) integrating multi-source data and prior information within a probabilistic inversion framework to provide uncertainty quantification and enhance the reliability of inversion results; (3) employing multi-resolution optimization strategies, wherein a coarse-scale inversion captures the overall structure before progressively refining the model to improve computational efficiency and mitigate local minima issues; and (4) integrating seismic, gravity, magnetic, and electromagnetic data to enhance inversion robustness, while incorporating real-time monitoring data to better capture subsurface dynamic processes.With advancements in high-performance computing, artificial intelligence, and novel geophysical observation technologies, joint inversion methods are expected to play an increasingly crucial role in resource exploration, subsurface structure detection, and geological hazard monitoring, providing higher-resolution and more accurate subsurface imaging techniques for Earth science research.