# Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
## 4 Data Analysis and Discussions  
### Machine Learning Clustering Results  
The MS monitoring system enables real-time capture of rock mass failure events occurring during the development of roof fractures and provides their location. The variation pattern in MS events reflects the dynamic development process of collapsed ore and the evolution of the rock mass fracture state at the top boundary of the collapsed ore body. The flow of data processing and analysis is shown in Figure 2.  
Before conducting clustering calculations, data preprocessing is required to remove obvious outliers and noisy data. At the same time, it is essential to ensure that the processed  
Figure 2: Data analysis and processing flow chart. data exhibit relatively good monitoring performance in at least local locations to guarantee the reliability of the input data. Traditional methods face challenges in distinguishing the location of crack initiation, particularly in areas with high-density MS events, as depicted in Figure 3a. To overcome this, unsupervised machine learning methods are employed to determine the affiliation of each MS event and identify different clusters of MS events. The Gaussian mixture model (GMM) is utilized, which is a weighted combination of multiple Gaussian distribution models capable of fitting various types of distributions. The clustering process involves randomly selecting the cluster center, obtaining initial clustering results, adjusting the cluster center based on the results, and iterating until no significant changes in the cluster center occur. The iterative machine learning process calculates the range of event coordinates contained in each event cluster, yielding the final result. The optimal number of clusters is selected based on onsite conditions, and different colors are used to distinguish various types of MS event clusters, as shown in Figure 3b. Additionally, to visualize the influence range of event clusters, the 95% confidence interval ellipsoid for each cluster is calculated, as depicted in Figure 3c. The calculation results indicate that the long axis \"a\" of the MS event cluster is predominantly parallel to the z-axis, suggesting a vertical concentration and development of cracks. The projection of MS events in various directions is shown in Figure 3d. Figure 3: 3D spatial clustering results of MS events in 2018. (**a**) MS event data; (**b**) cluster results; (**c**) cluster influence range; (**d**) projection results. Table 1 presents the ellipsoidal parameters of different clusters, revealing significant differences in their distribution patterns and ranges. Clusters 1 and 2 exhibit relatively small differences in the lengths of their three main axes and have larger curvature in the confidence interval ellipsoid model. In the case of Clusters 5 and 10, even though the length difference between the a-axis and b-axis is relatively small, the significant difference between the c-axis and the ab-plane suggests that microseismic events in these clusters may exhibit a tendency to occur along a particular plane. The selection of Clusters 5 and 10 based on the c-axis orientation is motivated by the hypothesis that microseismic events may exhibit preferential planes of occurrence, and this choice serves as a starting point for a more in-depth investigation into the geological and structural factors influencing crack propagation and event behavior. When MS events are located on a plane, they may indicate precursors to cracks and rock mass instability. Microseismic events, which are often associated with the release of stress and energy in the subsurface, can occur along planes where stress concentrations are prominent. These stress concentrations can result from geological structures, discontinuities, or pre-existing fractures in the rock mass. Microseismic events can signify the initiation of small cracks or fractures within the rock.