## 6 Conclusions  
This paper proposes a k-medoids clustering algorithm that uses MDCAE as the feature extraction module and CSBD as the similarity measurement module and fuses volatility. The main conclusions are as follows. Despite these promising results, several limitations and delimitations exist. First, the model was primarily validated on microseismic data from a single mining face, which may limit its generalizability across different geological settings. Second, while the proposed volatility metric enhances shape similarity discrimination, it relies on statistical properties that may be sensitive to signal length or preprocessing techniques. Third, we focused solely on unsupervised clustering performance without incorporating domain knowledge or expert labels, which could further refine the results. Future research should explore multi-source microseismic data integration from different mines to test the robustness and transferability of the model. Additionally, extending the framework to semi-supervised or weakly supervised scenarios could leverage minimal expert labeling to further improve accuracy. Integration with time-series prediction models or real-time event detection systems is also a promising direction. Scinentifically, this work contributes a novel deep clustering architecture that leverages time-window constraints and waveform volatility, which can significantly improve the interpretability and robustness of clustering results. The algorithm enhances waveform characterization and clustering performance in noisy and unlabeled environments, offering a scalable and practical solution for dynamic event monitoring in mining and other geophysical domains. Figure 8: Confusion matrices for clustering results using CSBD-Vol, CSBD, SBD-Vol, and SBD. The MDCAE module shows excellent reconstruction accuracy when dealing with time series problems to improve the feature extraction ability. By introducing strategies such as spectral regularization, multi-scale fusion convolution, and dilated convolution blocks, the MDCAE module overcomes the reconstruction difficulties of the original autoencoder in the face of slight disturbances and solves the balance problem between the number of model parameters and the size of the receptive field. This series of improvements significantly enhances the accuracy of reconstruction. The Shape-Based Distance (SBD) algorithm shows significant advantages in processing time series data. Firstly, the SBD algorithm optimizes the distance calculation method and can effectively handle the deformation of time series data, which is particularly important in time series data analysis. Secondly, the SBD algorithm optimizes the calculation method of the centroid, making the clustering results more accurate and thereby improving the accuracy of data analysis. In addition, the SBD algorithm supports amplitude scaling and translation invariance, which enables it to better handle the characteristics of time series data and further improves the effect of data analysis. The CSBD algorithm solves the problem of the excessively high time complexity of traditional distance-based measurement methods represented by the Dynamic Time Warping (DTW) algorithm and introduces a constrained time window to further reduce the time complexity of the algorithm. The CSBD-Vol algorithm is proposed to solve the problem that the traditional SBD algorithm is easily disturbed by noise. This algorithm introduces a restricted window, thereby reducing the time complexity, and borrows the concept of volatility from the financial field and integrates the redefined volatility formula into the algorithm, thereby solving the problem that the traditional algorithm is sensitive to noise. The MDCAE-CSBD-Vol algorithm makes full use of the features extracted by MDCAE to measure similarity, thereby improving the clustering efficiency of the model. To sum up, the MDCAE-CSBD-Vol algorithm belongs to the unsupervised clustering algorithm, eliminating the dependence of traditional deep learning algorithms on labels.