| 基于斜率模型的突发型黄土滑坡失稳时间预测 |
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| 引用本文： | 方汕澳,许强,修德皓,赵宽耀,李志刚,蒲枫.基于斜率模型的突发型黄土滑坡失稳时间预测[J].水文地质工程地质,2021,48(4):169-179. |
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| 作者姓名： | 方汕澳 许强 修德皓 赵宽耀 李志刚 蒲枫 |
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| 作者单位： | 成都理工大学地质灾害防治与地质环境保护国家重点实验室，四川 成都 610059 |
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| 基金项目： | 国家自然科学基金重点项目（41630640）；国家自然科学基金重大项目（41790445） |
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| 摘 要： | 突发型黄土滑坡灾前变形量小，加速阶段历时短，预警预报难度大。为探究该类滑坡失稳时间预测的新途径，降低滑坡造成的经济损失和人员伤亡，以2019年甘肃黑方台地区发生的4起滑坡为研究对象，基于改进的切线角模型确定滑坡变形阶段，提出以改进切线角为指标的简化累计计算方法；采用斜率模型（SLO模型）从滑坡各变形阶段起算进行失稳时间预测，从速度倒数变化趋势、滑坡成灾模式等方面分析预测结果差异。研究发现:（1）斜率模型在突发型黄土滑坡失稳时间预测方面具有一定的可行性，从80°切线角起算得到的预测精度最高；（2）以切线角为划分指标进行简化累计计算能降低数据波动对预测结果的影响，反映预测寿命变化趋势，提高预测精度；（3）速度倒数变化趋势呈“凹”型时提前预测概率大，速度倒数变化趋势呈“凸”型时滞后预测概率大，速度倒数变化趋势呈线性时模型预测精度较高；（4）该模型在黄土滑移崩塌型滑坡中的预测效果要优于静态液化型滑坡。
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| 关 键 词： | 黄土滑坡 突发型滑坡 时间预测 斜率模型 切线角 |
| 收稿时间： | 2020-09-03 |
A study of the predicted instability time of sudden loess landslides based on the SLO model |
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| Institution: | State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China |
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| Abstract: | The deformation and displacement of sudden loess landslides are small and the time of duration is short, which make early warning and forecast of landslides difficult. In order to explore a new way to predict the instability time of these landslides and reduce economic losses and casualties, four landslides in the Heifangtai area of Gansu Province in 2019 are taken as the research objects, and the deformation stage of landslide is determined with the improved tangent angle mode. A simplified cumulative calculation method based on the improved tangent angle is proposed. The SLO model is used to predict the instability time. The difference in the predicted results is analyzed from the speed change trend and disaster-causing mode. The results show that (1) the SLO model is of a certain feasibility in predicting the instability time of sudden loess landslides, and the predicted accuracy obtained from the tangent angle of 80° is the highest. (2) The simplified accumulative calculation performance using the tangent angle as the dividing index reduces the impact of data fluctuations on the predicted results and improves the predicted accuracy. (3) When the inverse velocity change trend is “concave”, the probability of early prediction is large.When the inverse velocity change trend is “convex”, the probability of early prediction is small. And when the inverse velocity change trend is linear, the prediction accuracy is relatively high. (4) The prediction effect of this model in loess fall landslides is better than that of loess flow landslides. |
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