## 1 Introduction  
Time series data mining has applications in many areas [1, 2]. Time series are commonly used for data classification, fault detection, pattern recognition, and prediction [3]. However, supervised methods such as convolutional neural networks (CNNs) are not suitable for datasets with non-uniform lengths [4]. The Hidden Markov Model (HMM) can adapt to some datasets, but its performance is poor [5, 6]. Recurrent Neural Networks (RNNs) can also be used to process temporal data, but there are problems of long training time and difficulty in convergence [7]. The SBD (Shape-Based Distance) algorithm has the advantages of high computational efficiency, ease of comparison, and strong adaptability [8]. The SBD algorithm is currently mainly applied in the fields of time series clustering, anomaly detection, etc. [9]. With the rapid development of the coal industry in China, the intensity of mining in mines continues to rise, resulting in frequent non-natural seismic events during the mining process, which seriously threaten the life and property safety of on-site workers. According to the monitoring data released by the Seismic Network of Shandong Province, 337 natural seismic events and 561 non-natural seismic events were monitored in 2020; 304 natural seismic events and 269 non-natural seismic events occurred in 2021. The frequency of non-natural seismic events has clearly surpassed that of natural seismic events. Therefore, it is very important to identify and classify microseismic events in a timely and accurate manner [10], providing an important guarantee for the production safety of coal mines [11]. Domestic and foreign research on coal mine microseismic events mainly focuses on the focal mechanism [12], the optimization of the microseismic sensor network, etc. [13], while research on the identification of microseismic events is relatively insufficient. Recent studies have focused on unsupervised representation learning for seismic data [14] and lightweight architectures for real-time detection [15], yet challenges remain in robust shape-based similarity metrics under noisy conditions [16]. The motivation for this study stems from the urgent need to improve the classification and clustering of microseismic events--particularly in distinguishing between microseisms, blasting events, and background noise. In mining environments, the ability to accurately and efficiently identify such events is crucial for ensuring operational safety and optimizing early warning systems [17]. However, the waveform signals of different microseismic events often exhibit highly similar characteristics, making classification a challenging task [18]. Moreover, the presence of noise and the lack of labeled datasets further complicate traditional analytical approaches [19]. The research on microseismic waveform identification began in the 1960s and initially relied on manual observation, but this method was inefficient. Over time, scholars at home and abroad have developed various microseismic waveform identification techniques. For example, researchers such as [PERSON] reviewed the existing microseismic identification technologies [20]; [PERSON] et al. developed a microseismic signal identification method based on the VGG4-CNN model, which requires data to be labeled manually [21]. In addition, [PERSON] et al. improved the LeNet5 convolutional neural network and proposed a new microseismic monitoring waveform identification technology [22]. [PERSON] et al. further classified microseismic waveforms into categories such as coal compression type, roof fracture type, and fault dislocation type for detailed research [23]. To solve the practical problems of large fluctuations in the accuracy of conventional mathematical methods when disturbed by noise and over-reliance on labels by supervised learning methods, this paper proposes a new unsupervised learning method. Based on the classic Shape-Based Distance (SBD) algorithm, a multi-scale fusion spatial convolutional autoencoder is proposed for feature extraction of the original waveform.