Prediction of Quaternary Cover Thickness and 3D Geological Modeling Based on BP Neural Network
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摘要: 地质灾害风险精细化调查和评估是目前地质灾害减灾防控的重要内容. 斜坡三维地质建模技术的发展为滑坡灾害风险精细化调查评估提供了新的思路，可大幅提高区域范围内滑坡灾害调查的效率和评估精度. 基于Skua-Gocad平台，针对第四系覆盖物和下伏基岩两大模块开展区域斜坡三维地质建模技术研究，以重庆市万州区大周镇为例，采用BP神经网络模型，通过构建研究区第四系覆盖物厚度与地质环境指标的多维非线性网络实现了第四系覆盖物厚度预测. 结合现场调查数据进行方法验证，基于BP神经网络的第四系覆盖物厚度预测精度达91.49%，在此基础上构建了三维地质模型，具有良好的可视化效果，并确保了数据的可靠性. 克服了传统基于克里金插值方法无法反应地质环境因素的缺点，解决了区域范围第四系覆盖物厚度预测的难题.Abstract: Fine investigation and assessment of geological disaster risk is an important part of prevention and control of geological disaster reduction at present. The development of 3D slope geological modeling technology provides a new idea for detailed investigation and assessment of landslide hazard risk, which can greatly improve the efficiency and assessment accuracy of landslide hazard investigation in the region.In this paper, based on Skua-Gocad platform, three-dimensional geological modeling technology of regional slope is studied for two modules of Quaternary cover and underlying bedrock. Taking Dazhou Town, Wanzhou District, Chongqing as an example, BP neural network model is used to predict the thickness of Quaternary cover by building a multi-dimensional nonlinear network of Quaternary cover thickness and geological environment indicators in the study area.Combined with the field survey data, the method is verified, and the prediction accuracy of Quaternary cover thickness based on BP neural network reaches 91.49%. On this basis, a 3D geological model is built, which has good visualization effect and ensures the reliability of data.It overcomes the shortcoming of traditional Kriging interpolation method that can't reflect geological environment factors, and solves the difficult problem of prediction of Quaternary cover thickness in regional scope.
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图 1 两层隐藏层的BP神经网络结构图
Fig. 1. BP neural network structure diagram with two hidden layers
图 2 神经网络预测第四系覆盖物厚度及三维建模流程图
Fig. 2. Prediction of Quaternary Cover Thickness by Neural Network and Flow Chart of 3D Modeling
图 3 大周镇位置与地层分段及第四系覆盖物厚度样本点分布图
a.重庆市区划；b.万州区乡镇分布；c.大周镇平面图
Fig. 3. Location of Dazhou Town, stratigraphic segmentation and distribution map of sample points of quaternary accumulation layer thickness
图 4 研究区指标因子图
a.高程；b.坡度；c.坡向；d.地形湿度；e.平面曲率；f.剖面曲率；g.归一化植被指数；h.第四系覆盖物类型及岩性分类
Fig. 4. Index factor diagram of study area
图 5 大周镇第四系平面覆盖物厚度预测结果与实地勘察对比图
a.大周镇第四系覆盖物厚度预测平面分布图；b.大周镇八角树滑坡全貌图；c.八角树滑坡1-1’工程地质剖面图；d.八角树滑坡钻孔柱状图
Fig. 5. Plane distribution of Quaternary cover thickness prediction in Dazhou Town and comparison with field investigation
图 6 第四系覆盖物厚度预测值与真实值对比
Fig. 6. Comparison between predicted and true values of Quaternary accumulation layer thickness
图 7 基于大周镇三维模型的虚拟工程地质剖面图与实地钻孔柱状图
Fig. 7. Virtual engineering geological profile and field borehole histogram based on 3D model of Dazhou Town
表 1 沉积及地形地貌指标及选取原因
Table 1. Deposit and topographic indicators and selection reasons
地形地貌因素 选取原因 归一化植被指数 第四系覆盖物对植被演化具有关键作用，植被覆盖也反映地区第四系覆盖物厚度（柴强, 2015） 高程 与第四系覆盖物厚度具有显著相关性，根据高程不同具有垂直分布特点（Penížek and Borůvka, 2006） 坡度 Ziadat et al.（2010）从DEM栅格图层中提取地形指标预测土壤厚度，利用实地测量的土壤厚度数据与预测数值做对比，结果显示土壤厚度与坡度及坡向指标呈显著的正相关 坡向 平面曲率 Patton et al.（2018）建立第四系覆盖物厚度-曲率关系模型，结果表示两者具有相关度的线性关系 剖面曲率 地形湿度 第四系覆盖物厚度易受流水搬运及库岸堆积影响，地形湿度直接影响覆盖物运移（Mehnatkesh et al., 2013） 第四系覆盖物类型 风化、崩落方式而形成的残积、崩积物影响第四系覆盖物厚度 软硬岩性 第四系覆盖物的下伏基岩软硬程度为第四系覆盖物急供聚集基础（孙立群等, 2021） 
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表 2 第四系覆盖物厚度预测值与实测值对比
Table 2. Comparison between predicted and measured valuesof Quaternary covering thickness
钻孔编号 实测值(m) 预测值(m) 绝对误差(m) 相对误差 ZK1 18.8 12.90 5.90 31.38% ZK2 12.5 8.78 3.72 29.76%
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