## 1 Introduction
### Current State of Research for Hard Coal Mines  
Machine learning algorithms have also been used to predict rock bursts and their intensity, strictly in underground coal mines. For example, [PERSON] et al. [40] proposed a fuzzy neural network risk prediction model for rock burst trained with the improved backpropagation algorithm. There were 10 input parameters representing conditions in the openings. The model was trained on 16 samples and tested on 10 samples. [PERSON] et al. [41] developed a rock burst prediction model based on the BP neural network. Eight input parameters were used representing geological and mining conditions and mining technology. The model was trained on seventeen cases and tested on six cases. [PERSON] et al. [42] classified cases of rock bursts and non-destructive tremors in one of the active underground coal mines. The database contained 150 samples, and the percentage of rock bursts was approximately 35%. These samples were characterized by 25 input parameters. Seven parameters described the geological conditions, ten parameters defined the mining conditions, and three parameters described the technical/technological conditions. There were also two seismic parameters and three parameters describing the rock burst hazard. The models of multilayer perceptron classifier and decision tree were the most effective. [PERSON] et al. [43] reduced the number of parameters in the rock burst database and replaced the geological and mining parameters with the rock mass bursting tendency index \(W_{TG}\) and the anomaly of the vertical stress component in coal seams, respectively. A total of eleven input parameters were used. The thickness of the seams, two seismic parameters, three technical/technological parameters, and three parameters describing the seismic hazard were left. Again, the models of decision tree and multilayer perceptron classifier classified the rock bursts best. [PERSON] et al. [44] proposed a rock burst prediction index system containing 16 indicators. A total of 118 cases were used as sample data. However, the Boruta-random forest model had better predictive performance than the others [44].  
Only about 1% of tremors with the energy that could lead to a rock burst in the opening end up in this way [45]. In hard coal mines in the USCB, the number of rock bursts is significantly smaller compared to the number of strong tremors that can lead to a rock burst. In recent years, the ratio of the number of rock bursts--classified by Polish mining regulations--to the number of strong tremors (i.e., tremors with energy greater than or equal 1 \(\times\) 10\({}^{5}\) I, so with a local magnitude \(M_{L}\geq\) 1.68) in coal mines in the USCB was as follows: \(\sim\)0.13% (2018), \(\sim\)0.32% (2019), \(\sim\)0.07% (2020), and \(\sim\)0.13% (2021). It should be noted that in underground coal mines located in the USCB, there are also dynamic phenomena that are less destructive (i.e., they do not cause a loss of functionality of the openings, or they cause a slight loss of their functionality or a slight deterioration in the safety of their use). Such phenomena are not classified as rock bursts in official statistics. However, the number of all dynamic phenomena, regardless of the extent of destruction and damage to underground mine openings (i.e., classified as a rock burst or not), is still very small compared to non-destructive strong tremors. The prepared database at least partially tries to reflect this imbalance in the number of destructive and non-destructive phenomena in mining openings.  
Based on the selected geological, geomechanical, mining and technical parameters as well as the tremor energy and the correlated peak particle velocity (PPV), an attempt was made to classify rock burst cases and non-destructive tremors in one of the active hard coal mines in the USCB. The rock mass proneness to rock bursts was assessed via the GEO system [46]. All geological and geomechanical parameters were included in the GEO index [46].