# Development of Rockburst Research: A Comprehensive Review
## 5 Rockburst Intensity Classification and Prediction
### Mathematical Model Approaches for Rockburst Classification and Prediction
#### 5.3.2 Machine Learning  
Mathematical models of rockburst classification and prediction based on empirical criteria have great limitations in considering the nonlinearity of influencing factors. In view of this, some researchers tried to use machine learning methods. The merit is that machine learning does not need any prior knowledge about the relationship between input/output variables and reduces the intervention of human factors. Machine learning  
Figure 6: Mathematical model approaches of rockburst classification and prediction. can be categorized into four categories: classical machine learning, reinforcement learning, ensemble learning and artificial neural network (ANN), as shown in Figure 6.  
Classical machine learning is often used in situations with less data and clear characteristics. At present, with the amount of in situ monitoring and physical model tests data about rockburst we are more inclined to choose classical machine learning methods for classification and prediction. Support vector machines (SVMs) [143], the logistic regression classifier [144], decision trees (DT) [145], Bayesian networks [146], naive Bayes [147] and the k-nearest neighbor (KNN) [148] are all invoked for rockburst classification and prediction. An artificial neural network is often used to deal with complex nonlinear relationships, which can model and extract unknown features and relationships. [PERSON] et al. used an ANN model to predict the rockburst risk of a deep gold mine in South Africa [149]. [PERSON] et al. predicted the type of rockburst via ANN according to the geological and construction characteristics of the mine [150]. Self-organizing mapping (SOM) clustering technology was invoked to divide rockburst data into four groups, which can accurately identify rockburst [151]. [PERSON] et al. used a firefly algorithm (FA) and ANN to establish a complex relationship model between rockburst risk and influencing factors in deep mines and tunnels for predicting the severity of rockburst [152]. An extreme learning machine (ELM) algorithm with particle swarm optimization (PSO) was invoked to predict fifteen typical rockburst examples of Jiangbian hydropower station in China, which achieved good results [153]. Compared with traditional machine learning algorithms, neural networks usually require more data, and the computational cost will be more expensive. Since the advent of deep learning (developed from ANN) in 2012, great achievements have been made in the fields of speech recognition and computer vision. Compared with classical machine learning technology, deep learning provides a more powerful prediction model and faster learning mechanism and is more adaptable to changes of environment. Deep learning needs a lot of data to improve the generalization ability of models; thus, deep learning is almost always applied in microseismic monitoring technology. [PERSON] et al. used a convolutional neural network (CNN) model (ConvNetQuake, Figure 7) to cluster microseismic events into six regions according to the three-component waveform received by the station, which realized the preliminary location of microseismic events and completed preliminary exploration of a deep learning algorithm for microseismic source location [80]. CNN was also invoked to identify the Time Delay of Arrival (TDOA) and the source location of seismic waves in underground mines. The data of field blasting tests and simulation tests show that compared with several existing typical methods, the proposed approach can identify noise waveforms recorded in an in situ blasting test more accurately [154], which gave more accurate microseismic source identifications. [PERSON] et al. proposed a lightweight network architecture named ResSCA based on CNN for complex signal recognition and classification in microseismic monitoring. While it improves the network performance, it does not produce additional computational overhead [155].