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滑坡研究中的多层传感技术——克罗地亚赫瓦尔卡·科斯塔伊尼察案例研究。

Multi-Level Sensing Technologies in Landslide Research-Hrvatska Kostajnica Case Study, Croatia.

机构信息

Croatian Geological Survey, Sachsova 2, 10000 Zagreb, Croatia.

出版信息

Sensors (Basel). 2021 Dec 28;22(1):177. doi: 10.3390/s22010177.

Abstract

In March 2018, a landslide in Hrvatska Kostajnica completely destroyed multiple households. The damage was extensive, and lives were endangered. The question remains: Can it happen again? To enhance the knowledge and understanding of the soil and rock behaviour before, during, and after this geo-hazard event, multi-level sensing technologies in landslide research were applied. Day after the event field mapping and unmanned aerial vehicle (UAV) data were collected with the inspection of available orthophoto and "geo" data. For the landslide, a new geological column was developed with mineralogical and geochemical analyses. The application of differential interferometric synthetic aperture radar (DInSAR) for detecting ground surface displacement was undertaken in order to determine pre-failure behaviour and to give indications about post-failure deformations. In 2020, electrical resistivity tomography (ERT) in the landslide body was undertaken to determine the depth of the landslide surface, and in 2021 ERT measurements in the vicinity of the landslide area were performed to obtain undisturbed material properties. Moreover, in 2021, detailed light detection and ranging (LIDAR) data were acquired for the area. All these different level data sets are being analyzed in order to develop a reliable landslide model as a first step towards answering the aforementioned question. Based on applied multi-level sensing technologies and acquired data, the landslide model is taking shape. However, further detailed research is still recommended.

摘要

2018 年 3 月,克罗地亚科斯塔伊尼察发生山体滑坡,多个家庭被完全摧毁。损失巨大,生命受到威胁。问题仍然存在:这种情况还会再次发生吗?为了增强对这一地质灾害事件前后土壤和岩石行为的了解,在滑坡研究中应用了多层次传感技术。事件发生后的第二天,进行了实地测绘,并使用现有的正射影像和“地质”数据收集了无人机(UAV)数据。对滑坡进行了新的地质柱状图编制,包括矿物学和地球化学分析。应用差分干涉合成孔径雷达(DInSAR)检测地面位移,以确定失效前的行为,并提供失效后的变形迹象。2020 年,在滑坡体中进行了电阻率层析成像(ERT),以确定滑坡面的深度,2021 年,在滑坡区域附近进行了 ERT 测量,以获取未受干扰的材料特性。此外,2021 年还为该地区获取了详细的光探测和测距(LIDAR)数据。为了开发可靠的滑坡模型,作为回答上述问题的第一步,正在对所有这些不同层次的数据进行分析。基于应用的多层次传感技术和获取的数据,滑坡模型正在形成。然而,仍建议进行进一步的详细研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a77e/8749565/4e25886756d4/sensors-22-00177-g001.jpg

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