Monitoring, Prediction, and Evaluation of Mountain Geological Hazards Based on InSAR Technology

Abstract

When traditional geological hazard survey methods are used for deformation monitoring in mountainous areas, it often shows the disadvantages of low applicability of monitoring methods and limited accuracy of detection results. In recent years, synthetic aperture radar interferometry (InSAR) technology has incomparable advantages in surface deformation monitoring, such as all-weather detection, wide detection range, high detection accuracy, and low detection cost. At the same time, InSAR technology can also provide data and technical support for the subsequent task of potential geological disaster point identification and geological disaster risk zoning in the study area. Alos-2 radar is selected; in this paper the satellite image is the research data, and the InSAR technology is used to complete the surface deformation detection. Then, based on the previous surface deformation monitoring results, the potential geological disaster points in the study area are extracted, and the distribution law and incubation conditions of the disaster points are analyzed and described. According to the field conditions of a certain area, the surface distribution, development causes, and inducing mechanism of the potential geological disaster points are explored; the results show that the development of geological disasters in the study area is affected by many factors such as landform, geological environment, climate, hydrology, and human activities. Based on this, 11 factors such as formation lithology, slope, and river are used as evaluation factors for mountain geological disaster monitoring, prediction, and evaluation analysis. Finally, the improved analytic hierarchy process information model is used to complete the monitoring, prediction, evaluation, and analysis of regional geological hazards in the study area. In this paper, the improved AHP-information method is used to classify the risk of mountain geological disasters in the study area. Finally, the evaluation results are verified, which proves that the improved AHP-information method is reliable, and its mountain geological disaster monitoring and prediction evaluation effect is better than the traditional AHP-information method.