###### Abstract  
The initial inversion model is typically established in a transient electromagnetic nonlinear inversion, assuming the accurate capture of the number of layers in the geoelectric model; however, this assumption leads to significantly poorer inversion results for complex models when obtaining the exact number of layers from available a priori information, which is challenging. This study proposes a segmented regularized inversion method to enhance inversion accuracy and stability under varying conditions. The process involves two key steps: Firstly, a segmented initial model is established based on preliminary information. The layering criteria and layer thickness threshold for each segment are set during inversion to reduce dependence on the accuracy of the preliminary information. Secondly, a segmented regularization constraint is added to the objective function to improve the efficiency and stability of the inversion, as numerous parameters can exacerbate the problem of inversion ambiguity. Subsequently, an improved sparrow search algorithm (ISSA) is utilized to optimize the inversion objective function. This enhances the efficiency of searching for the objective function and the algorithm's ability to escape local optimal solutions. The proposed method is evaluated using one-dimensional and two-dimensional models with different initial models and inversion algorithms and applied to the inversion of on-site exploration data in a coal mining area in Chongqing. Comparative results demonstrate that the proposed segmented regularization method, based on the improved sparrow search algorithm, exhibits superior practicality and a higher fitting accuracy. t 1 2024  
data are usually discrete and finite, the solution to the inversion problem is not unique. In one-dimensional sections, even vastly different resistivity distributions can yield similar electromagnetic fields when measured at the surface. Conventional linear inversion methods typically use conductivity-depth imaging outcomes or a uniform half-space as the initial model. These methods optimize conductivity and layer thickness parameters to obtain geoelectric structure graphics. For instance, [PERSON] et al. [6] introduced an inversion method that utilizes Gaussian Newton iteration in conjunction with Tikhonov regularization for layered media. [PERSON] et al. [7], on the other hand, employed the inversion scheme proposed by [PERSON] and [PERSON]. They applied this approach to conduct one-dimensional joint inversion on transient electromagnetic data obtained from the central loop and long offset. Additionally, [PERSON] et al. [8] utilized the damping least squares method for inverting transient electromagnetic data originating from conical sources; however, this type of inversion method requires a high-quality initial model and is prone to getting stuck in local optima. In contrast, nonlinear inversion approaches involve designing the initial model directly based on a priori information, as seen in works by [PERSON] et al. [9] and [PERSON] et al. [10]. These methods are efficient but heavily dependent on the quality of a priori information, which can be challenging to obtain in large-area exploration. [PERSON] et al. [11] implemented a strategy to fit the model automatically by globally designing and optimizing many layers through a simulated annealing algorithm, yielding satisfactory fitting results. Compared to the classical linear inversion method, the nonlinear global optimization algorithm offers several advantages, including zero-order features (not involving the derivative of the forward model) and global exploration. As a result, scholars have introduced nonlinear algorithms to geophysical inversion. For instance, [PERSON] et al. [12] proposed a novel barnacle mating optimization algorithm for the inversion and interpretation of magnetic anomalies. [PERSON] et al.