2019-07-22T07:18:45Z (GMT) by Ankit Dixit arek Sikora
Seismic hazard is the hardest detectable and predictable of natural hazards and in this respect it is comparable to an earthquake. More and more advanced seismic and seismoacoustic monitoring systems allow a better understanding rock mass processes and definition of seismic hazard prediction methods. Accuracy of so far created methods is however far from perfect. Complexity of seismic processes and big disproportion between the number of low-energy seismic events and the number of high-energy phenomena (e.g. > 10^4J) causes the statistical techniques to be insufficient to predict seismic hazard. Therefore, it is essential to search for new opportunities of better hazard prediction, also using machine learning methods. In seismic hazard assessment data clustering techniques can be applied (Lesniak A., Isakow Z.: Space-time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland. Int. Journal of Rock Mechanics and Mining Sciences, 46(5), 2009, 918-928), and for prediction of seismic tremors artificial neural networks are used (Kabiesz, J




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