## 4 Conclusions  
This study aims to explore the application of machine learning techniques in the field of microseismic event waveform image recognition and classification, as well as their potential and limitations. Through a comprehensive analysis and discussion of the relevant literature, we have drawn the following conclusions:  
1. Early spectrum analysis methods in microseismic event recognition relied heavily on domain knowledge and expertise, requiring experts to engage in time-consuming manual analysis and interpretation. Another common statistical analysis method classified microseismic events based on statistical features, but it required manual definition and selection of appropriate features, as well as manual extraction and analysis. Moreover, traditional manual recognition methods suffer from heavy workloads and low efficiency. 2. In contrast, adopting machine learning methods such as deep learning can improve recognition efficiency and accuracy by automatically learning features and pattern recognition, thereby reducing the burden of manual involvement. These methods are more effective in handling large-scale datasets and enhancing the automation of microseismic event recognition. 3. Combining machine learning algorithms with computer vision techniques provides a direct solution for analyzing microseismic event waveform images, with the ability to handle large-scale datasets and enhance the precision and speed of event recognition and classification. 4. However, in the field of microseismic event recognition, deep learning algorithms face challenges such as insufficient annotated data, high computational resource requirements, and the need to improve generalization performance. Addressing these difficulties requires interdisciplinary collaboration and ongoing research efforts. 5. Future directions include several aspects: firstly, establishing strategies to improve the robustness of models, thereby enhancing the stability and reliability of microseismic event waveform recognition and classification models. This can be achieved through the use of more complex network structures, introducing regularization techniques, or integrating multiple models. Secondly, creating diverse datasets is crucial. These datasets should contain abundant samples of microseismic events, covering various geological environments and working conditions. Training and evaluating models using diversified datasets can improve their generalization ability and adaptability. Thirdly, data augmentation techniques can effectively increase the diversity and quantity of training data, thereby enhancing model performance. For example, data augmentation can be achieved through operations such as rotation, scaling, and translation to expand the dataset, allowing the model to better adapt to variations and differences among microseismic events. Furthermore, exploring transfer learning and self-supervised learning is a promising research direction in microseismic event recognition using deep learning. Transfer learning can leverage existing models and knowledge to quickly adapt to new microseismic event recognition tasks, thereby improving model performance and generalization ability. Self-supervised learning can use appropriate methods to generate labels automatically, utilizing unsupervised data for training and further enhancing model performance. Through continuous exploration and improvement, deep learning will provide more reliable safety guarantees for mining engineering in the field of microseismic monitoring. In conclusion, deep learning approaches provide effective instruments for microseismic event identification, with the potential to increase the accuracy and efficiency of microseismic event categorization, as well as provide more dependable security guarantees for underground engineering and mining activities. However, further study and effort are required to solve the current problems and ensure the long-term implementation of deep learning approaches in microseismic monitoring. Conceptualization, A.Y.D. ; methodology, H.S.