黄河流域刘家峡—兰州段滑坡灾害的InSAR识别及成因分析

Identification and Cause Analysis of Potential Landslides in Liujiaxia-Lanzhou Section of the Yellow River Basin with InSAR Technique

摘要:

我国滑坡灾害频发，尤其是西部地区，滑坡的隐蔽性强且危害巨大，对其灾害隐患进行早期识别对防灾减灾意义重大。传统的人工排查、大地测量等手段在山区难以开展且耗时耗力，合成孔径雷达干涉测量技术（InSAR）作为新兴的遥感测量手段，可以更精确、高效地进行大范围的滑坡灾害隐患识别。以黄河流域刘家峡-兰州段为研究区，采用永久散射体InSAR技术对覆盖该区域的111景Sentinel-1 C-SAR数据进行处理，并使用GPS数据进行趋势改正，获取研究区2014年10月—2019年12月间视距向形变场及形变特征，成功识别出1处位于永靖县的滑坡隐患区。该区的规模和量级均大于永靖县黑方台滑坡区，且未被前人提及，具有较大危险性。将InSAR结果与投影到视距向的GPS结果及前人结果进行比较，验证PS-InSAR方法的有效性；并结合历史强震资料、实地考察及时间序列对研究结果进行验证分析，结果表明该滑坡隐患区可能与1125年兰州M7.0地震和2017年九寨沟M_S7.0地震有关。本研究可为当地防灾减灾提供数据和技术支持。

Abstract:

Landslides occur frequently in China,especially in the western regions,with largely concealment and harmfulness.Early identification of potential landslides is pretty essential for disaster prevention and mitigation.The traditional methods,such as manual inspection and geodetic survey,are difficult to carry out and time-consuming in mountainous areas.Interferometric Synthetic Aperture Radar (InSAR),as a new remote sensing measurement method,can accurately and efficiently identify the hidden dangers of landslides in wide regions.In this study,based on 111 Sentinel-1 C-SAR images and GPS three-dimensional velocity field in the Liujiaxia-Lanzhou section of the Yellow River Basin,the permanent scatterer InSAR (PS-InSAR) technology was used and calibrated to get the line-of-sight (LOS) deformation field of the study area from October 2014 to December 2019.One potential landslide area,which was located in the Yongjing County,was detected.This potential landslide area has never been mentioned before,but its size and magnitude are much larger than those of Heifangtai landslide area.Compared with GPS and previous results,the accuracy of the InSAR results was verified.Based on the historical data of strong earthquakes and field survey,it is found that this potential landslide area may be related to the Lanzhou M7.0 earthquake in 1125 and the Jiuzhaigou M_S7.0 earthquake in 2017.This study can provide data and technical support for disaster prevention and mitigation in the study area.