## 2 Geo-Environmental Condition Evaluation Methods  
To minimize resource waste and safeguard the environment, precise mining of the underground resource is crucial, which requires fine reconstruction of the occurrence state of underground resources. At present, exploration technologies mainly based on geological analysis, drilling, geophysical exploration, geochemical exploration, and remote sensing provide foundational data for safe, efficient, and intelligent mining of coal mines in China, as shown in Figure 1.  
Conventionally, the resolution of geological analysis has been largely contingent upon the thoroughness and precision of geological data [10]. The results obtained when drilling have the characteristics of being direct and true; however, such results come at the cost of higher time and economic cost, which is accompanied by the potential risk of blind zone detection [11]. In contrast, as one of the methods used in geophysical exploration, electromagnetic tomography (EMT) is widely applied due to its economic advantages and rapidness; it is also a non-destructive technique when implemented [12]. Due to the significant differences in the absorption abilities of various media to electromagnetic waves,  
Figure 1: The exploratory technology used to assess geo-environmental conditions within a working face. abnormal areas within a working face can be imaged. However, unlike in the medical industry, where electromagnetic waves can cover the human body with a 360-degree field of view, coal mine working faces are often narrow and rectangular, with roofs and floors covered by rock layers, making it difficult to arrange the transmitting source and the receiving device [13]. As a result, the obtained projection data are insufficient and incomplete in terms of precisely reconstructing geological environments within the detection areas of working faces, which is considered to be the burning issue in intelligent mines that are under development [14]. As an indispensable technology in the coal mine industry, electromagnetic tomography (EMT) was developed from the theoretical basis of tomography known as the Radon transform, which was put forward by [PERSON] in 1917 [15]. Limited by the shape of coal mine working faces and observation systems, the target matrix equation to be inversed by EMT does not always contain the full rank of the column, resulting in infinite solutions [16]. The most classical solutions to the matrix equation are the algebra reconstruction algorithm (ART) proposed by [PERSON] [17] and the simultaneous iterative reconstruction technique (SIRT) algorithm proposed by [PERSON] [18] and their optimization algorithms. Subsequently, many scholars stated that the regularization method could be used in the tomographic inversion of a working surface [19]. However, the majority of these widely used tomographic inversion methods start with a single initial model and repeatedly approach the improved adjacent solution set until the final solution is obtained and the permitted requirements are satisfied [20]. As a result, the initial model choice has a significant impact on the inversion results. If the initial model is poorly chosen, the objective function is vulnerable to local optimal solutions, which could have a negative impact on the stability and accuracy of the reconstruction of the geological environment in working faces. Currently, utilizing EMT technology to provide a more accurate, stable, and reliable initial model for the traditional tomographic inversion algorithm has become the core in the related field [21; 22], which is also the focus of this research. Since 1949, intelligent algorithms that emulate the principles of survival and evolutionary dynamics in nature have been gradually developed, such as the Monte-Carlo method [23], BP neural network [24], single genetic algorithm (SGA) [25], and particle swarm optimization algorithm [26].