Assessment of Seismic Hazards in Underground Mine Operations using Machine Learning

Abstract

The most common causes of coal mining accidents are seismic hazard, fires, explosions, and landslips. These accidents are usually caused by various factors such as mechanical and technical failures, as well as social and economic factors. An analysis of these accidents can help identify the exact causes of these accidents and prevent them from happening in the future. There are also various seismic events that can occur in underground mines. These include rock bumps and tremors. These have been reported in different countries such as Australia, China, France, Germany, India, Russia, and Poland. Through the use of advanced seismological and seismic monitoring systems, we can now better understand the rock mass processes that can cause a seismic hazard. Unfortunately, despite the advancements, the accuracy of these methods is still not perfect. One of the main factors that prevent the development of effective seismic hazard prediction techniques is the complexity of the seismic processes. In order to carry out effective seismic risk assessment in mines, it is important that the discrimination of seismicity in different regions is carried out. The widespread use of machine learning in analyzing seismic data, it provides reliability and feasibility for preventing major mishaps. This paper provides uses various machine learning classifiers to predict seismic hazards.