Mine Geological Disaster Risk Assessment and Management Based on Multisensor Information Fusion

Abstract

Mining excavation is often the main cause of geological disasters in people’s construction activities. The geological disasters have the characteristics of large destruction, wide range of harm, and large loss. In particular, the collapse and slide geological disasters caused by underground mining are particularly prominent, and they have triggered a number of major natural disaster events. Therefore, it is particularly important to assess the exposure to geological hazards in mines. The purpose of this article is to study and analyze the assessment and management of the risk of geological hazards in mines based on multisensor data integration. This paper first introduces the process of multisource information fusion, and in the process of information fusion, the sensor needs to collect signals first, then preprocesses the signals provided by the sensor, and then analyzes the fusion process of D-S evidence theory algorithm and BP neural network algorithm in multisensor. Finally, the deformers in the study area are investigated by multisensor data integration techniques, the deformation and damage features of the deformers in the study area are evaluated, and the risk assessment and vulnerability evaluation of the key slopes are carried out. The experimental results of this paper show that according to the statistics of the distribution of slope disaster points, the geological disasters are mainly concentrated in 10–25°, a total of 361, accounting for 58.1% of the total disaster points. From the point density distribution, geological disasters are most concentrated at 20–30°, and the point density is 35 places/100 km2. The results show that in areas with large slope and height difference, it is easy to form air surface, deformation, and damage, resulting in geological disasters.

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