# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
###### Abstract
However, in the face of microseism monitoring signals containing a large amount of information, it is difficult to manually define and extract parameters to reflect all the features of microseism events, which makes it easy to cause a large amount of effective information to be ignored. As a result, current microseism monitoring can only monitor and reflect the microseism events that have occurred, and are occurring, and it is difficult to accurately judge the potential microseism events that may occur. Traditionally, mine earthquake prediction usually uses geophysical methods to monitor some precursory signals, and the synthetic index method with artificially defined and extracted parameters is used to evaluate the possibility of the occurrence of high-energy mine earthquakes. The determination of the index and corresponding weight involves subjectivity and inconsistency. Machine learning can well overcome the problems caused by the synthetic index method. Its modeling process does not involve too much subjective decision-making, and is a data-driven strategy. Using the machine learning model, researchers do not need to pay attention to the weight of each index and the corresponding classification standard, they just need to know the specific value of each index, which is objective and measurable. At the same time, the use of intelligent devices such as microseism monitoring systems will generate massive real-time data carrying effective information. Empirically driven and mechanistically driven mine earthquake prediction methods are not enough to use these data, resulting in the loss of effective information. Using a data-driven method to solve the problem of mine earthquake prediction will become a breakthrough point for data-driven systems to enter the traditional engineering field, and is also a key step for mining to enter the smart mine and data mine era. After the great development of computer hardware and the upsurge of machine learning, researchers continue to try to use data-driven methods, such as machine learning,to predict and warn of mine earthquake, achieved good results. Using the machine learning method to analyze microseism monitoring signals can get the most effective information, most of which cannot be obtained by an explicit algorithm. The machine learning method also has unique advantages in establishing the relationship between monitoring parameters and the time, location, and intensity of a mine earthquake. It can analyze the automatic monitoring signal into a high-dimensional matrix, without the need to manually determine the type of extraction parameters, and maximize the retention of signal features. [PERSON] and [PERSON] [14, 15] established a logistic regression model to identify microseism events and mining blasting activities, respectively, with a higher accuracy than other analogy models. [PERSON] [16] established two neural network models to identify microseism events, one of which is used to extract signal features to construct training samples, and the other is used for event classification. In addition, machine learning methods such as random forest [17], Bayesian network [18], and support vector machine [19, 20], are widely used for monitoring microseism events. However, these studies do not consider the spatial-temporal aggregation and activity correlation of microseism events, and the prediction accuracy of mine earthquake is not high; there is a lot of room for improvement. Microseisms are not isolated events. Mine earthquakes caused by a stress release usually have a similar spatial location, short time interval, and have the law of time series. If we first cluster the microseism data and then use the machine learning method to establish the prediction model, we will get better results. Therefore, we propose to combine clustering analysis and machine learning methods to predict the high-energy mine earthquake in time sequence, including the occurrence location prediction and energy frequency prediction.