Bian Shiqiang, Yang Yunpeng, Ma Jianhua, et al. 2020. Two-dimensional imaging study of internal moisture in loess slope: A case study of the Luojiapo landslide in Heifangtai terrace[J]. Journal of Engineering Geology, 28(4): 840-851. doi: 10.13544/j.cnki.jeg.2019-345.
    Citation: Bian Shiqiang, Yang Yunpeng, Ma Jianhua, et al. 2020. Two-dimensional imaging study of internal moisture in loess slope: A case study of the Luojiapo landslide in Heifangtai terrace[J]. Journal of Engineering Geology, 28(4): 840-851. doi: 10.13544/j.cnki.jeg.2019-345.

    TWO-DIMENSIONAL IMAGING STUDY OF INTERNAL MOISTURE IN LOESS SLOPE: A CASE STUDY OF THE LUOJIAPO LANDSLIDE IN HEIFANGTAI TERRACE

    • High and steep loess slopes are formed due to the uplift of the Tibetan Plateau and the erosion of the Yellow River in the Loess Plateau. Under the influence of precipitation and agriculture irrigation, a large number of loess landslides occur every year due to the increased underground water table. Therefore, to reveal the spatial distribution and migration of water within the loess slope is key for the understanding of loess landslide mechanism and for the purpose of landslide early-warning. In this paper, electrical tomography(ERT) and model simulation methods are used to quantify the relationship between internal resistivity and water content of the slope. The two-dimensional imaging of the internal moisture of the slope is combined with the surfer software. The correlation analysis between loess resistivity and formation physicochemical parameters shows that the formation water content in the loess slope is the dominant factor in the formation resistivity change. The model fitting result based on the Archie formula can accurately quantify the relationship between resistivity and water content. The two-dimensional imaging results of formation water can visually show the temporal and spatial evolution of water inside the slope. This study quantifies and intuitively describes the water distribution characteristics inside the loess slope, which can provide the most direct and effective data for the prediction and warning of loess landslide.
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