Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks

During hard coal mining operations conducted under conditions of rockburst hazard, one of the most important preventive measures can be the prediction of occurrence time and location of the strong seismic mine tremors of energy E-s >= 10(4) J. This is a very difficult task and the way it is being currently performed appears to be unsatisfactory. Therefore, attempts have been made to use neural networks, specifically trained for this application. The paper presents an approach for determining an influence of the type and shape of the input data on the efficiency of such a prediction. The considerations are based on a selected example of the seismic activity recorded during longwall mining operations conducted in one of the Polish mines.