###### Abstract  
Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters. microseismic events; machine learning; signal processing; waveform recognition; image classification; sensor technology +
Footnote †: journal: Medical  
selected a total of 1286 articles published between 2012 and 2023, spanning the past decade. Based on the titles, abstracts, and keywords of these articles, we classified them into four application domains: underground energy exploration and development (e.g., oil and gas resources), real-time monitoring, and early warning of geological hazards (e.g., landslides and karst collapses), seismic activity research and earthquake monitoring, and monitoring and evaluation of underground storage and waste disposal facilities (e.g., nuclear waste repositories). The statistical results are shown in Figure 2.  
Considering the changing mining environment and the need for safety assurance, we initiated this review of contemporary techniques applied to the recognition and classification of microseismic events. This work not only addresses the pressing need for advanced methods, but also aligns with the broader trend of intelligent mining practices. By combining deep learning with microseismic event analysis, we aim to provide a comprehensive survey for the geological engineering and computer science communities, enabling them to harness the potential of AI-driven microseismic monitoring. The fracturing of rock masses during seismic events, geological disasters, and underground excavation processes generates abundant microseismic signals (MS) [11]. Microseismic monitoring systems (MMS) used in deep subterranean engineering can collect a vast amount of waveform data in real time [12]. Certain deceptive noise signals, on the other hand, bear striking similarities to microfracture signals, requiring engineers to cross-reference signal features from distinct domains [13]. This challenge poses difficulties in  
Figure 1: Application of microseismic monitoring in smart mining. Figure 2: Application of microseismic monitoring technology in various fields. rapidly and accurately detecting microseismic data. Notably, the presence of noise signals closely resembling microseismic signals significantly hampers swift and precise detection, consequently undermining effective geological hazard assessment and risk prevention [14]. MMS generates vast volumes of data, primarily capturing microseismic events--a task fraught with challenges due to the intricate nature of on-site monitoring environments. Traditionally, the identification of these events relied heavily on manual experience.