## 1 Introduction  
With the increase in large-scale mining equipment and the improvement in mine production management levels, long-term mining leads to the depletion of shallow mineral resources and the continuous increase in mining depth [1; 2]. Due to the existence of complex environments such as deep high stress, the incidence of high-energy rock bursts and mine earthquakes increases [3], and traditional monitoring methods are difficult to accurately analyze due to the main distribution of the surrounding rock surfaces [4]. Microseismic monitoring technology utilizes modern computing technology, communication technology, GPS timing, and precise positioning technology to determine the location and magnitude of microseismic events in rock mass in three-dimensional space. The technology catches microseismic events, which occur in the form of weak seismic waves in the process of fault failure, to perform a safety evaluation of the deformation activity and stability of the deep coal and rock mass [5]. The positioning accuracy of the source is an important indicator to measure the monitoring quality of the microseismic network, which determines the effect of the microseismic monitoring to a large extent [6]. The quality of the network layout directly affects the microseismic location accuracy [7; 8]. Foreign scholars have conducted a lot of research in this area and achieved remarkable results. For example, [PERSON] [9; 10] and [PERSON] et al. [11] proposed a microseismic network evaluation method based on the optimal design theory of \(D\) and \(C\) values (determinant value of the source covariance matrix and the condition number of non-linear travel-time equations with respect to the known source parameter vector). Although these two methods use numerical methods to evaluate the positioning capability of the network, they do not consider the key monitoring areas and actual engineering conditions. [PERSON] [12] used the covariance matrix of the microseismic arrival equation to evaluate the positioning ability of the combined single-component sensor and three-component sensor arrangements, respectively. [PERSON] et al. [13] significantly improved the positioning error of the three-channel sensor arrangement by increasing the number of sensors reasonably. [PERSON] et al. [14] used the \(D\)-value optimization method to study the optimization of the station network layout of the microseismic monitoring system, designed several spatial layout schemes for the sensor station network, and calculated the event source location error and system sensitivity for each scheme. [PERSON] et al. [15,16] put forward the general principles for determining the risk monitoring area of rockburst and the layout requirements for station candidate points by using the comprehensive index method and then using the \(D\)-value optimization criterion to form the final scheme. [PERSON] et al. [17] added relevant influencing factors to reconstruct the objective function of the network optimization based on the \(D\)-value theory, combined with the actual situation of mine engineering so that the microseismic monitoring system can meet the actual needs of the mine. [PERSON] et al. [18] used principal component analysis (PCA) to construct a comprehensive optimization analysis model for the microseismic monitoring network. However, these studies only solve the problems of monitoring range and monitoring accuracy calculation from the technical level. Due to the complexity of underground engineering, the arrangement of the microseismic monitoring network is greatly restricted, so it is often necessary to select multiple schemes while meeting the technical requirements. Moreover, the above methods give more certain evaluation indicators, and comprehensive network optimization determination and evaluation methods need to be developed and proposed. In terms of scheme optimization, the traditional empirical analogy method is highly subjective, and it is difficult to achieve a quantitative judgment.