Application of rule-based models for seismic hazard prediction in coal mines

The paper presents results of application of a machine learning method, namely the induction of classification and regression rules, for seismic hazard prediction in coal mines. The main aim of this research was to verify if machine learning methods would be able to predict seismic hazard more accurately than methods routinely used in Polish coal mines on the basis of data gathered by monitoring systems. In this paper three classification and two regression tasks of prediction of seismic hazards in a longwall were defined. The first part of the paper describes the principles according to which the assessment of seismic hazard in Polish mines is made. These methods are called routine and allow to assess seismic hazard for a particular longwall. The next part of the paper discusses the algorithms of classification and regression rule induction and describes their use for seismic hazard assessment. The input data, which are the basis for rule induction, are: measurement data coming from seismometers and geophones, and the results of routine methods of hazard assessment. Conducted tests showed that automated hazard prediction based on induced rules gives better sensitivity and specificity of predictions than methods currently used in mining practice.