Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township
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摘要: 滑坡易发性评价是精细化滑坡灾害风险评价的基础。为了提升滑坡易发性评价模型的精度和稳健性，以三峡库区万州区燕山乡为例，选取工程地质岩组、堆积层厚度等九个影响因子构建滑坡易发性评价指标体系，应用信息量模型定量分析滑坡发育与指标之间的关系。在此基础上，随机选取70%/30%的滑坡样本作为训练/验证数据集，应用极致梯度提升模型(extreme gradient boosting, XGBoost)开展易发性评价。随后从模型预测精度和模型稳定性两方面将其与决策树模型(decision tree, DT)和梯度提升树模型(gradient boosting decision tree, GBDT)进行对比。结果表明：研究区堆积层滑坡主要受长江水系、堆积层厚度和工程地质岩组影响。XGBoost模型具有最高的准确率(94.3%)和预测精度(97.3%)。在模型稳定性验证中，平均预测精度最高(97.3%)，优于DT(91.3%)和GBDT(95.7%)，模型标准差和变异系数均为0.01，低于其余两种模型。XGBoost在区域滑坡易发性评价与制图中得到了可靠的结果，为滑坡灾害空间预测提供了新的技术支撑。Abstract: Landslide susceptibility assessment forms the foundation for precise evaluation of landslide risk. To enhance the accuracy and robustness of landslide susceptibility mapping, a state-of-art machine learning algorithm named the extreme gradient boosting model (XGBoost) was introduced to this study. Yanshan Town in Wanzhou district, Three Gorges reservoir, was chosen as a case study. Nine influencing factors, including engineering geological lithology and thickness of deposit layer, were selected to construct the landslide susceptibility evaluation index system. The relationship between landslide development and these indicators is quantitatively analyzed using the information value model. Subsequently, 70% of landslide samples were randomly assigned for training, while the remaining 30% were used for validation. The XGBoost model was then employed for landslide susceptibility mapping. The output were compared with those of the decision tree model (DT) and gradient boosting decision tree (GBDT) in terms of prediction accuracy and model stability. The findings revealed that distance to the Yangtze River, soil thickness, and lithology were the primary factors influencing landslide development. The XGBoost model demonstrated the highest average prediction accuracy (97.3%) in 100 repeated trials, surpassing the DT (91.3%) and GBDT models. Moreover, the XGBoost model exhibited superior robustness with a standard deviation and coefficient of variation of 0.01, lower than the other two models. It also achieved the highest accuracy (94.3%) and prediction accuracy (97.3%) in the validation process. The proposed XGBoost model serves as a reliable assessment method and yields optimal results in regional landslide susceptibility mapping.
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0. 引言
由碎石土、尾矿砂等颗粒材料堆积而成的松散体在自然界和工业生产活动中广泛存在，普遍具有结构松散、孔隙度大、颗粒间结合力差等特点，有复杂的力学性质和相对较高的失稳风险。受自然条件和人类活动等影响，松散体在斜坡地形中更易发生地质灾害。因此如何有效预测斜坡地形松散体边坡滑移破坏的过程对于矿山的生产安全和实际工程项目的实施具有重大意义。目前松散体边坡的监测方式主要有位移监测和变形监测，包含光学遥感、三维激光扫描、GPS、RS等技术[1 − 5]。但以上技术都存在明显局限性，监测范围有限并且对边坡内部变形和滑坡孕育过程情况不能及时掌握。声发射（acoustic emission，AE）是基于声波的发射和检测，并且和检测对象内部结构的断裂或者变形息息相关。由于松散体滑移破坏过程中，其内部松散颗粒间的相互作用力也会产生弹性波，这使得AE技术能够识别其信号，及时发现和记录边坡破坏的发展过程，在滑坡灾害前做出有效的预警。因此，AE技术在近年来的边坡稳定性监测中逐渐得到更多关注。
近年来，针对松散体滑坡的研究重点从滑坡监测技术的相关话题逐渐转向滑坡变形的监测预警和边坡稳定性的相关评估研究[6 − 9]。Codeglia等[10]提出一种利用AE监测边坡稳定性的思路，认为AE趋势与内部应力、外部斜坡载荷等有关。Berg等[11]在前人研究基础上在加拿大的和平河地区进行了声发射实地试验。Hu等[12]提出了用声发射和微震联合监测的方法，并获得了更准确的预测结果。此外，不少学者一直以来针对松散体边坡的组成物质开展室内试验，探寻其在不同试验过程中声发射信号特征，Chen等[13]对BFRP混凝土粘结滑移破坏过程特征进行了研究。Deng等[14]尝试搭建用于滑坡监测的声发射阵列系统，并进行了相关试验。李文彪等[15]将声发射用于松散颗粒介质边坡或路堤稳定性研究。胡训健等[16]模拟了单轴压缩实验中细观结构的非均质性对岩石颗粒的声发射特性的影响。吴鑫等[17]研究了不同剪切速率下松散颗粒的声发射信号特征。
综上所述，目前关于松散体边坡监测相关研究已取得一定进展，但是其滑移过程的声学规律研究仍不充分。因此本研究将搭建斜坡试验平台，开展松散体模拟滑坡试验，并考虑连续改变装置倾角，监测松散体静止、蠕变到滑移整个过程的声发射信号演化规律，为松散体边坡稳定性监测提供研究思路和数据支撑。
1. 试验设计
采用中国ISO标准砂（GSB08-
1337 ）作为试验材料，利用高频振筛机筛分出0.5～1 mm的砂粒作为试验原材料，试件尺寸Φ61.8×40 mm。试验装置如图1—2所示，主要由升降装置、滑坡装置和声发射数据采集系统3个部分组成。升降装置由滑轮、固定物和绳子组成，滑轮采用不锈钢材质，长度为85 mm，滑轮轮直径为25 mm。滑坡装置由2个透明的0.3 m×0.3 m×0.8 m的空心长方体模型箱组成。滑坡装置的滑道分为2层，底层作为表面层，将试验砂均匀地粘在倾斜滑道上来模拟自然条件下的坡面；上层为堆积层，将试验砂均匀堆放在底层上形成堆积层来模拟松散体。
声发射系统为北京软岛DS5-16B多通道声发射仪，采样频率为3MHz，默认触发阈值为100 mV，频率范围为100～400 kHz，模拟滤波器信号设为直通，储存设置为波形。在滑坡装置安装3个陶瓷压电传感器全程记录声发射信号参数与波形信息。传感器型号是RS-2A，尺寸为Φ18.8×15 mm，接口类型为M5-KY，放大器为40 dB。该声发射仪能够实现声发射信号的采集、回放、波形处理和声源定位，并基于阈值法获取时差，进而采用穷举算法进行定位。同时安装2个30 fps、分辨率为
1080 p的高清摄像头记录试验过程。如图1所示传感器设置在倾斜箱体的300，600 mm处的底部背面和水平箱体位置200 mm处。摄像头和倾斜箱体的传感器位置相互对应，在其上方300 mm处。试验开始前将倾斜滑道与水平面的初始高度设置为450 mm，并将400 g粒径分布为0.5～1 mm的标准砂均匀堆积在倾斜滑道上，以模拟自然条件斜坡地形上的松散体。在堆放标准砂时要保持滑道上的堆积层不会发生滑移现象。试验装置起始角度为34°，试验过程倾角变化范围在34°～39°。试验开始后，保持0.75 mm/s的速度启动升降装置，在上升过程中由于倾角不断地发生变化使得松散体经历静止，蠕变到滑移整个过程。当松散体滑移过程结束后即停止试验同时停止采集相关数据，通过声发射传感器和高清摄像头分别采集得到松散体的整个滑移过程的信号数据和视频图像。通过多组重复试验研究松散体滑移的启动机制、速度变化、变形特征以及滑移前兆。
本试验进行了3组重复试验，结合布置的2个位置互相对应的声发射探头和高清摄像头一共得到了6组速度数据。各组速度均值分别为0.018，0.013，0.012，0.026，0.011，0.015 m/s，标准差为
0.0056 ，平均误差为0.0041 ，最大误差为0.0102 ，变异系数为0.35。变异系数的大小与数据稳定性有着密切的关系。通常情况下，变异系数越小，表示数据集中的观测值越稳定，波动性较小。因此，本试验误差较小，结果较为稳定。2. 数据分析
2.1 数据预处理
为研究松散体滑移过程中的AE信号规律，选择了2个关键时间节点，如图3所示将整个滑移过程分为3个阶段。时间节点选取为视频观察到的颗粒滑动点和滑移点。I阶段为平稳阶段，对应滑坡过程中的平稳阶段，该阶段无滑动现象；II阶段为蠕变阶段，对应滑坡过程中的滑动阶段和滑移前阶段，该阶段能观察到个别及少数颗粒滑动；III阶段为滑移阶段，对应滑坡中的滑坡阶段，该阶段能观察到较多颗粒滑动甚至宏观滑坡现象。
2.2 振铃计数及AE能量阶段分析
松散体在滑移过程中的AE信号特征是：振铃计数和能量随滑移过程呈现先逐渐增大后衰减，最终趋于稳定。具体表现如图4所示：I阶段时，振铃计数保持不变；在II阶段时，振铃计数逐渐上升，达到较高点后保持平稳，此时颗粒间的相互作用力在不断增加，松散体自身的状态逐渐不稳定，滑移门槛值为
5000 次，当振铃计数达到门槛值后发生滑移现象。在III阶段振铃计数逐渐衰减。此时松散颗粒重新排列或沉降，从而导致松散体内部结构和稳定性得到改善，使得振铃计数逐渐减小。若发生二次滑移，则振铃计数会有波动变化。在滑移结束时，振铃计数变化逐渐稳定。滑移过程中AE能量释放规律与振铃计数演化规律类似。如图4所示，相比振铃计数，能量的滑移门槛值并不固定，当能量大于
1500 mV·ms后，易发生滑移现象。试验发生二次滑移的原因是由于松散颗粒之间的相互作用产生的，遵循Omori定律。其反映的是大的断裂声发射信号引发的次生断裂信号行为。在本试验中是随着滑移过程的进行，松散体内部结构重新发生变化，使得松散体出现新的不稳定状态进而发生二次滑移现象。2.3 b值分析
b值（b-value）是小事件数与大事件数的比值，b值常作为判定裂隙发展情况的参数[17]。b值的计算公式可表示为：
$$ b=\frac{20\times\mathrm{lge}}{(A-A_{\mathrm{min}})} $$ (1) 式中：e——自然常数；
A、$ {A}_{{\mathrm{min}}} $——平均幅值和最小幅值/mV。
b值在计算时将其门槛值设为0.004；频率设置为3 MHz，将原始数据划分为900组，每组数据集样本数为
100000 。b值随时间的演化规律如图5所示，具体表现为：b值在滑移过程中逐渐减小，这是由于滑移过程引起的颗粒间的摩擦和力链重排等事件越来越频繁和显著，即大事件比例不断增加。随着滑移过程的进行，b值减小趋势逐渐放缓。b值门槛值为0.2，当b值达到门槛值后易发生滑移现象；在III阶段b值逐渐回升。在滑移结束时，b值变化逐渐稳定。2.4 频谱分析
采用快速傅里叶变换（fast Fourier transform，FFT）对松散体滑移试验AE信号数据进行处理得到功率谱，进一步获得主频、频谱重心等信息。主频即峰值频率，频谱重心计算公式如下：
$$ FC=\frac{{\displaystyle\int }_{0}^{+\infty }fP\left(f\right){\mathrm{d}}f}{{\displaystyle\int }_{0}^{+\infty }P\left(f\right){\mathrm{d}}f} $$ (2) 式中：FC——频谱重心/Hz；
f——频谱重心的横坐标；
(f)——信号的功率谱。
频谱重心随时间的演化规律如图5所示，在I阶段时，频谱重心保持在400 kHz左右，此时松散体内部结构相对稳定，没有明显的内部位移或结构变化。外部应力达到了一个稳定状态，松散体正处于相对静止或受均衡的应力状态。在II阶段，频谱重心在临滑移前期已经有30～50 kHz的降幅，说明在该时期松散体已经开始发生微观结构变化或应力分布变化，从而导致越来越多的颗粒开始出现滑动情况。在此之后频谱重心在350～450 kHz之间进行波动变化，这种波动变化预示着松散体内部结构正在不断发生变化，一般与松散体内部微观裂缝扩展、颗粒重新排列或颗粒间压力变化相关，最终导致了宏观滑坡现象的发生。
具体表现为：在I阶段时无论是振铃计数、能量、b值、频谱重心还是PIV分析得到的颗粒速度图像曲线均保持相对稳定。在II阶段，振铃计数和能量呈不断上升的趋势，b值不断减小，频谱重心在临滑移前期有30～50 kHz的降幅，而后过程中频谱重心在350～450 kHz之间进行波动变化。频谱重心的震荡时间区域正好对应振铃计数和能量数值相对较高及b值相对较低的时间区域。临近滑移点时，颗粒图像测速法（particle image velocimetry，PIV）速度图像曲线开始缓慢增长。其中，b值变化最早，对松散体的状态变化的更敏感。在III阶段，PIV速度图像曲线先增后减，随着滑移过程结束，各参数变化逐渐归于稳定（图6—7）。
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图 6 声发射结合PIV滑移过程分析图Figure 6. acoustic emission combined with PIV slip process analysis diagrams此外，还注意到在II阶段时，AE信号变化出现在PIV速度变化之前。当松散体发生滑动或滑移行为前，声发射（AE）参数就已经发生了相应的变化。这段AE参数变化的时间视为松散体滑坡发生前的“窗口期”，并可能将其用于松散体滑坡预测和预警[18 − 20]。
3. 滑移阶段预测
AE试验数据提供了关于松散体的状态变化的声学信号，这些信号可以识别松散体滑坡前的“窗口期”，并作为预测依据。考虑选取AE信号参数为预测指标，根据不同阶段AE信号参数对应的规律变化来预测松散体滑移过程的不同阶段[21]。由于b值对松散体状态变化较为敏感，所以选取b值为基本参数，结合各试验组b值演化情况，基于此将松散体滑移过程分为5个阶段。划分依据和计算过程如下：
$$ {R}_{b}=\left\{\begin{split} &{R}_{b}5 \quad\quad b\leqslant 1\\ &{R}_{b}4 \quad\quad 1 < b\leqslant 3\\ &{R}_{b}3 \quad\quad 3 < b < 4\\ &{R}_{b}2 \quad\quad 4\leqslant b < 5\\ &{R}_{b}1 \quad\quad b\geqslant 5 \end{split}\right. $$ (3) 式中：$ {R}_{b} $——基于b值的预测阶段；
$ {R}_{b}1 $～$ {R}_{b}5$——滑移阶段（$ {R}_{b}5$），滑移预警阶段II （${R}_{b}4 $），滑移预警阶段I（${R}_{b}3 $），滑动 阶段（${R}_{b}2 $）和平稳阶段（${R}_{b}1 $），$ {R}_{b} $等 级越高表示阶段越危险；
b——小事件数与大事件数的比值，与滑坡危险程 度成反比，b值越小表示阶段越危险。
预测情况如表1所示，滑移点为图3右侧竖线，提前时间为b值第一次到达（3，4）间的时间点。结果表明预警点（${R}_{b}3 $对应的滑移预警所致）出现在实际滑移发生之前，预测的滑移阶段包含了实际滑移阶段，且所有试验组均符合规律。当达到$ {R}_{b}5 $后，即使后面阶段发生变化，仍需警惕后续滑坡现象的发生。说明了Rb预测模型能提前预测松散体滑移现象的发生，考虑到b值门槛值的变化性和不确定性，未来可以建立多种声发射参数预测模型，并与各类机器学习的方法相结合，建立新的模型来预测松散体滑移过程，从而提高预测模型的准确性和可靠性。
表 1 各试验组预测时刻表Table 1. Predicted timetable for each test group编号 实际滑移点 滑移预警点 提前时间/帧 SY1Z1T 510 439 71 SY1Z2T 700 486 214 SY2Z1T 650 565 85 SY2Z2T 660 586 74 SY3Z1T 720 672 48 SY3Z2T 738 558 180 注：SY1Z1T含义为试验1组第1通道，下同理。 尽管现阶段将声发射技术实际应用于松散体滑坡工程仍有一定的难度，但可以利用相关技术一些尝试，如利用有源波导技术，改进有源、普通波导杆或WEAD波导杆等，克服AE信号在非固结材料中的快速衰减，并将其应用于实际松散体边坡场景，如渣土场，尾矿库等，以开展边坡风险预测预警。
4. 结论
（1）通过连续改变装置倾角，模拟松散体在斜坡地形上经历从静止到蠕变再到滑移的整个过程，利用数据挖掘来揭示松散体滑动变形过程中声发射信号的演化规律以及捕捉滑移前兆，对松散体滑移过程相关研究进行了一定的补充和完善。
（2）试验数据揭示了松散体滑移前存在一个重要的“窗口期”，通过声发射技术更早监测到松散体内部的变化信号，延长预警时间来提高监测和预警系统的效率，说明AE技术具有识别松散体滑坡前兆的潜力。
（3）本研究可应用于监测由生产活动或开采资源后形成的松散体边坡稳定性，并在实际生产和工程中及时感知潜在的滑动风险，从而有助于在地质灾害事故发生前采取及时、有针对性的措施，最大程度地减少或避免潜在的生命财产损失。
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图 7 研究区易发性评价指标图
Figure 7. Indicator plot for landslide susceptibility assessment in the study area
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图 8 参数与预测精度关系曲线
Figure 8. Relationship curve between parameters and prediction accuracy
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图 10 各易发区灾害点比例
Figure 10. Proportion of disaster points in different susceptibility zones
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图 12 抽样次数与预测精度关系曲线
Figure 12. The correlation curve between sampling times and prediction accuracy
表 1 各因素状态信息量表
Table 1 The weighted information values of each factor state
指标 分级 信息量 指标 分级 信息量 指标 分级 信息量 坡度/（°） 0～9 1.28 工程地质岩组 砂岩夹泥岩、砂岩 1.58 斜坡结构 顺向伏倾坡、顺向飘倾坡 0.75 9～21 0.75 砂泥岩互层 0.59 顺斜坡 −0.99 21～33 −0.83 泥岩夹砂岩、泥岩 1.46 横向坡、逆斜坡、逆向坡 −0.96 33～45 −3.28 页岩夹灰岩、灰岩 −1.79 斜坡形态 内向凸形坡 0.31 >45 −9.32 距长江距离/m 0-400 2.96 直线凸形坡、外向凸形坡 −0.21 植被归一化指数 <0.15 1.07 400～700 −0.46 内向直坡、直线直坡 −1.79 0.15～0.25 −0.30 700～1400 0.72 外向直坡、内向凹坡 −0.82 >0.25 −0.70 >1400 −1.84 直线凹坡、外向凹坡 −0.99 坡向/（°） 0～180 −1.33 地形湿度指数 0～6 −0.35 堆积层厚度/m 0～0.8 −9.98 180～234 0.01 6～12 0.83 0.8～1.6 −3.35 234～252 −0.13 12～18 0.23 1.6～2.4 −1.04 252～342 0.58 >18 −1.24 >2.4 2.54 342～360 −0.60 
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表 2 标准差和变异系数
Table 2 Standard deviation and coefficient of variation
模型 平均数 标准差 变异系数 95%置信区间下限 95%置信区间上限 DT 90.304 0.734 0.813 90.160 90.448 GBDT 95.612 0.062 0.065 95.600 95.624 XGBoost 97.281 0.010 0.010 97.279 97.283
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