###### Abstract  
Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning. In the early stage, the classification of microseismic events relies on human experiences, which is not only inefficient but also often causes some misclassifications. In recent years, the neural network-based classification method has become more favored by people because of its advantages in modeling procedures. A microseismic signal is a kind of time-series signal and the application of the classification method is widely optimistic. The number and the balance of the training data samples have an important impact on the accuracy of the classification result. However, the quality of the training data set obtained from the production cannot be guaranteed. A long short-term memory (LSTM) network can analyze the time-series input data, where the image classification at the pixel level can be achieved by the fully convolutional network (FCN). The two structures in the network can not only use the advantages of the FCN for extracting signal details but also use the characteristics of LSTM for conveying and expressing the long time-series information effectively. In this paper, a time-series data enhancement combination process is proposed for the actual poor microseismic data. A hybrid FCN-LSTM network structure was built, the optimal network parameters were obtained by experiments, and finally a reasonable microseismic data classifier was obtained. deep learning; microseismic data classification; data enhancement; FCN-LSTM network +
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modeling procedures. Long short-term memory (LSTM) is a special type of recurrent neural network (RNN), which can be used to analyze the time-series input data. LSTM can obtain the long-term dependence information and solve the long-term dependence problem in neural networks [20; 21]. It can deal with the gradient vanishing or gradient explosion problems [22]. The way to overcome gradient exploding is gradient clipping, meaning that when the gradient exceeds threshold c or is less than threshold \(-\)c, the gradient is set as c or \(-\)c, where c is a constant. It is widely used in natural language processing (NLP) and temporal signal prediction and classification [23; 24; 25]. Therefore, the microseismic signal is a kind of temporal signal, and LSTM has great potential in microseismic signal classification. A fully convolutional network (FCN) achieves image classifications at pixel level, solving the semantic-level image segmentation problem. Different from the classic CNN, the FCN can use input images of any size. It can upsample the feature map of the last volume layer, and then restore it to the same size as the input image through a deconvolution layer. Thus, a prediction can be generated for each pixel, while retaining the spatial information of the original image. Finally, parity is used to classify pixels in the upsampled feature map. A successful neural network requires many parameters, which are obtained by many training data sets in the training process. The training process constantly iterates the training model and finally achieves a required neural network [26]. As a common data expansion technique, data enhancement is crucial for deep learning training models [27], which can effectively avoid model overfitting caused by small training sets [28]. Researchers have already noticed that the unbalanced distribution of data samples in the training data set will adversely affect the trained classifier [29]. These events ignored by the neural network lead to an incorrect classification result [30]. Many scholars have proposed some data enhancement methods, such as adding a random linear bend in the frequency domain, which enhances sound timing signals to achieve sound signal recognition [31]. Data sparsification without changing labels can be used for modeling of deep neural networks [32].