Optimization of ground-based SAR data and analysis of disaster body area early warning and prediction

doi:10.13474/j.cnki.11-2246.2022.0356

Abstract

Abstract: Aiming at the problems that it is difficult to identify the ground-based SAR abnormal data, the development characteristics and laws of potential landslide mass are not obvious, and it is difficult to estimate the critical sliding area, this paper introduces the processing method of ground-based SAR deformation data based on the landslide data of a mine in Gansu province. The abnormal deformation data are effectively identified, screened and eliminated by Matlab software, and distinguished by color differences, which further highlights the evolution law of potential disaster body area in different stages. Based on the principle of ground-based radar monitoring, a polygon area estimation method is proposed. This method can estimate the area of potentially hazardous areas. Study on velocity reciprocal curve group with unequal period treatment, it is found that the inverse square method of velocity can further amplify the characteristic trend. The early warning result is accurate and the prediction effect is good through this early warning and prediction method, which provides a new idea for realizing the early warning and prediction of mine landslide.

Key words: ground-based SAR, landslide, data optimization, early warning, prediction

CLC Number:

P237

LIU Yuxi, YANG Fengyun, QIN Hongnan. Optimization of ground-based SAR data and analysis of disaster body area early warning and prediction[J]. Bulletin of Surveying and Mapping, 2022, 0(12): 51-56,96.

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