泸石高速公路沿线历史地震诱发滑坡遥感调查及发育分布规律

魏旭; 彭志忠; 刘兴臣; 肖励之

doi:10.19509/j.cnki.dzkq.tb20220653

泸石高速公路沿线历史地震诱发滑坡遥感调查及发育分布规律

doi: 10.19509/j.cnki.dzkq.tb20220653

1.
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, 成都 610059
2.
四川泸石高速公路有限责任公司, 成都 610000

基金项目:

国家自然科学基金项目 41941019

详细信息

作者简介:
魏旭, E-mail: 1207288906@qq.com

通讯作者:
彭志忠, E-mail: 99975724@qq.com

中图分类号: P642.22
计量
- 文章访问数: 287
- PDF下载量: 31
- 被引次数: 0
出版历程
- 收稿日期: 2022-11-22
- 录用日期: 2023-01-09
- 修回日期: 2022-12-22

Remote sensing investigation and development distribution of historical earthquake-induced landslides along Lushi Expressway

1.
State Key Laboratory of Geological Disaster Prevention and Environmental Protection, Chengdu University of Technology, Chengdu 610059, China
2.
Sichuan Lushi Expressway Co., Ltd., Chengdu 610000, China

More Information

Author Bio:
WEI Xu, E-mail: 1207288906@qq.com

Corresponding author: PENG Zhizhong, E-mail: 99975724@qq.com

摘要

摘要:
泸(定)石(棉)高速公路沿线断裂构造发育, 地震频发, 造成区域内历史地震诱发的滑坡数量多且规模大, 然而这些地震滑坡体表面生长大量植被, 传统调查难以高效查明地震滑坡分布位置及发育规律。为了减少地震滑坡对公路建设带来的安全隐患, 首先以高精度机载LiDAR数据对地震滑坡进行了识别, 并通过野外复核验证识别的准确性；其次利用高精度机载LiDAR影像对地震滑坡的变形特征进行了分析；最后, 综合考虑地形、地质、地震三大因素(6个因子), 对地震滑坡的空间分布特征进行了分析。结果表明: 利用机载LiDAR技术能够有效地发现植被层下的地震滑坡, 在泸石高速公路沿线共识别出地震滑坡23处, 野外复核验证精度达100%；通过对控制地震滑坡空间分布的6个因子的分析, 得出与断裂构造和地震相关性最高。研究结果为植被茂密山区高速公路的滑坡识别调查提供了一定的参考并为泸石高速公路滑坡灾害防治与风险评价提供了数据支撑。
- 机载LiDAR /
- 地震滑坡 /
- 滑坡识别 /
- 分布规律 /
- 遥感调查 /
- 高速公路
Abstract: Objective
The fault structure along the Lu (ding) Shi (main) highway has developed, and earthquakes are frequent, resulting in a large number and large scale of historical earthquake-induced landslides in the region. However, a large amount of vegetation grows on the surface of these seismic landslides, and it is difficult for traditional surveys to efficiently identify the distribution location and development law of earthquake landslides.
Methods
To reduce the safety hazards caused by earthquake landslides during highway construction, this paper first identifies earthquake landslides with high-precision airborne LiDAR data and verifies the accuracy of identification through field review.Second, high-precision airborne LiDAR images are used to analyse the deformation characteristics of seismic landslides.Finally, the spatial distribution characteristics of the seismic landslides are analysed by comprehensively considering the three major factors (6 factors) of topography, geology and earthquakes.
Results
The results show that airborne LiDAR technology can be used to effectively detect seismic landslides under vegetation layers. A total of 23 seismic landslides are identified along the Lushi Expressway Common Road, and the accuracy of field review and verification is 100%.By analysing the six factors controlling the spatial distribution of seismic landslides, it is concluded that the correlations with fault structure and earthquakes are the highest.
Conclusion
The research results provide a reference for landslide identification and investigations of expressways with dense vegetation and provide data support for landslide disaster prevention and risk assessment of the Lushi Expressway.
- airborne LiDAR /
- earthquake landslide /
- landslide identification /
- distribution law /
- komote sensing investigation /
- expressway
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 研究区地理位置与地质背景图

Figure 1. Geographic location and geological background map of the study area

下载: 全尺寸图片幻灯片

图 2 地震滑坡分布图

Figure 2. Earthquake landslide distribution map

下载: 全尺寸图片幻灯片

图 3 烂田湾滑坡LiDAR影像及现场复核照片

a.滑坡机载LiDAR影像; b.滑坡现场情况; c.滑坡前缘局部滑塌; d.烂田湾滑坡A—A′纵剖面示意图

Figure 3. LiDAR image and on-site review photos of Lantianwan landslide

下载: 全尺寸图片幻灯片

图 4 泸石高速公路沿线地震滑坡变形特征

a.红军渡地震滑坡拉张裂缝；b.马列村地震滑拉张裂缝；c.摩岗岭地震滑坡的下错陡坎；d.由次级滑动形成的半圆形线条

Figure 4. Deformation characteristics of earthquake landslides along Lushi Expressway

下载: 全尺寸图片幻灯片

图 5 不同因子下地震滑坡分布图

Figure 5. Distribution map of earthquake landslides under different factors

下载: 全尺寸图片幻灯片

表 1 研究区地震滑坡解译标志图谱

Table 1. Map of earthquake landslide interpretation indicators in the study area

地震滑坡类型	拉裂-滑移型	拉裂-抛射型
解译特征	①滑坡后缘岩体较为破碎，通常可见拉张裂隙和下错陡坎 ②滑坡的剖面形态近“L”型 ③滑坡堆积体位于斜坡中下部，且整体完整性较好	①滑坡后缘岩体十分破碎，滑坡后壁坡度较陡 ②滑源区一般位于斜坡的中上部，且存在明显的凹腔 ③滑源区与堆积区距离较远，且滑坡堆积体较破碎
光学影像
机载LiDAR影像
三维示意图谱

下载: 导出CSV

表 2 地震滑坡信息

Table 2. Earthquake landslide information table

编号	滑坡名称	面积/m²	体积/m³	规模	经度	纬度	岩性
0	四湾里滑坡	1.3×10⁶	5.0×10⁷	特大型	102°13′49.812″E	29°56′18.401″N	片麻岩
1	小海子滑坡	3.7×10⁵	3.7×10⁶	大型	102°12′28.718″E	29°54′13.943″N	片麻岩
2	竹林坪滑坡	1.1×10⁶	1.5×10⁷	特大型	102°10′38.920″E	29°54′15.876″N	闪长岩
3	甘草村滑坡	7.2×10⁶	7.2×10⁸	巨型	102°11′39.650″E	29°54′1.210″N	闪长岩
4	甘谷地滑坡	6.4×10⁵	6.4×10⁶	大型	102°13′35.174″E	29°50′9.026″N	花岗岩
5	上松林滑坡	5.7×10⁶	3.4×10⁸	巨型	102°11′57.907″E	29°49′18.698″N	角闪岩
6	下松林滑坡	3.5×10⁶	8.9×10⁷	特大型	102°12′47.852″E	29°46′46.107″N	花岗岩
7	鸟支索滑坡	5.4×10⁶	2.0×10⁸	巨型	102°13′11.861″E	29°49′28.429″N	角闪岩
8	海子村滑坡	1.3×10⁶	2.6×10⁷	特大型	102°11′30.545″E	29°43′13.100″N	角闪岩
9	瓦斯沟滑坡	3.9×10⁶	1.9×10⁷	特大型	102°12′49.410″E	29°43′0.062″N	花岗岩
10	高河坝滑坡	2.6×10⁶	4.6×10⁷	特大型	102°12′44.599″E	29°41′45.495″N	花岗岩
11	寨子坪滑坡	6.5×10⁵	2.3×10⁶	大型	102°11′39.283″E	29°41′35.822″N	花岗岩
12	加郡滑坡	7.7×10⁶	1.3×10⁸	巨型	102°12′34.711″E	29°40′20.625″N	花岗岩
13	上奎武滑坡	2.5×10⁶	5.5×10⁷	特大型	102°11′25.072″E	29°39′45.096″N	花岗岩
14	马列村滑坡	1.1×10⁶	3.1×10⁷	特大型	102°11′7.757″E	29°36′51.953″N	角闪岩
15	摩岗岭滑坡	9.4×10⁵	4.5×10⁷	特大型	102°9′27.219″E	29°37′20.338″N	闪长岩
16	小马场滑坡	7.1×10⁵	3.4×10⁶	大型	102°11′7.689″E	29°36′15.737″N	超基性岩
17	烂田湾滑坡	1.7×10⁶	3.0×10⁷	特大型	102°11′20.646″E	29°35′33.054″N	花岗岩
18	得妥滑坡	3.7×10⁵	4.3×10⁶	大型	102°11′3.553″E	29°34′10.887″N	花岗岩
19	新华滑坡	2.8×10⁵	2.7×10⁶	大型	102°10′39.879″E	29°31′14.973″N	花岗岩
20	干谷地滑坡	1.3×10⁶	3.9×10⁷	特大型	102°14′58.679″E	29°22′47.424″N	花岗岩
21	干池沟滑坡	1.1×10⁶	3.2×10⁷	特大型	102°16′0.304″E	29°21′6.399″N	花岗岩
22	红军渡滑坡	1.6×10⁵	4.7×10⁶	大型	102°19′1.994″E	29°14′37.330″N	花岗岩

下载: 导出CSV

表 3 地震滑坡控制因子分级

Table 3. Classification of earthquake landslide control factors

因子类型	因子名称	因子分级
地形	高程/m	＜1 200；[1 200, 1 400)；[1 400, 1 600)；[1 600, 1 800)；[1 800, 2 000]；＞2 000
	坡度/(°)	＜10；[10, 20)；[20, 30)；[30, 40]；＞40
	坡向	平；北；北东；东；东南；南；西南；西；北西
地质	地层岩性	花岗岩；角闪岩；片麻岩；闪长岩；志留系、超基性岩；其他
地震	距断层距离/km	[0, 1)；[1, 2)；[2, 3)；[3, 4];＞4
地震	PGA/g	< 0.1；[0.1, 0.2)；[0.2, 0.3]；＞0.3

下载: 导出CSV

参考文献(22)

[1]	吴俊峰. 大渡河流域重大地震滑坡发育特征与成因机理研究[D]. 成都: 成都理工大学, 2013. WU J F. Study on the development characteristics and genetic mechanism of major earthquake landslides in Dadu River Basin[D]Chengdu: Chengdu University of Technology, 2013. (in Chinese with English abstract)
[2]	许冲, 徐锡伟. 基于GIS与ANN模型的地震滑坡易发性区划[J]. 地质科技情报, 2012, 31(3): 116-121. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201203019.htm XU C, XU X W. Seismic landslide susceptibility zoning based on GIS and ANN model[J]Geological Science and Technology Information, 2012, 31(3): 116-121. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201203019.htm
[3]	郭晨, 许强, 董秀军, 等. 复杂山区地质灾害机载激光雷达识别研究[J]. 武汉大学学报(信息科学版), 2021, 46(10): 1538-1547. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202110012.htm GUO C, XU Q, DONG X J, et al. Study on airborne lidar identification of geological hazards in complex mountainous areas[J]. Journal of Wuhan University(Information Science Edition), 2021, 46(10): 1538-1547. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202110012.htm
[4]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版), 2019, 44(7): 957-966. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907002.htm XU Q, DONG X J, LI W L. Early identification, monitoring and early warning of major geological hazards based on the integration of space, space and earth[J]. Journal of Wuhan University(Information Science Edition), 2019, 44(7): 957-966. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907002.htm
[5]	NICHOL J, WONG M S. Detection and interpretation of landslides using satellite images[J]. Land Degradation & Development, 2005, 16(3): 243-255.
[6]	THOTA S, SWAKANGKHA G, MAYAK J. Correction to: Exploitation of optical and SAR amplitude imagery for landslide identification: A case study from Sikkim, northeast India. [J]. Environmental Monitoring and Assessment, 2021, 193(8).
[7]	SCHLÖGEL R, DOUBRE C, MALET J P, et al. Landslide deformation monitoring with ALOS/PALSAR imagery: A D-InSAR geomorphological interpretation method[J]. Geomorphology, 2015, 231: 314-330. doi: 10.1016/j.geomorph.2014.11.031
[8]	吴琪, 周创兵, 黄发明, 等. 基于双重注意力机制的滑坡识别方法优化[J]. 地质科技通报, 2022, 41(2): 246-253. doi: 10.19509/j.cnki.dzkq.2022.0053 WU Q, ZHOU C B, HUANG F M, et al. Optimization of landslide identification method based on dual attention mechanism[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 246-253. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.2022.0053
[9]	董秀军, 王栋, 冯涛. 无人机数字摄影测量技术在滑坡灾害调查中的应用研究[J]. 地质灾害与环境保护, 2019, 30(3): 77-84. https://www.cnki.com.cn/Article/CJFDTOTAL-DZHB201903014.htm DONG X J, WANG D, FENG T. Application of UAV digital photogrammetry technology in landslide hazard investigation[J]. Geological Hazards and Environmental Protection, 2019, 30(3): 77-84. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DZHB201903014.htm
[10]	LINDNER G, SCHRAML K, MANSBERGER R, et al. UAV monitoring and documentation of a large landslide[J]. Appl Geomat, 2016, 8: 1-11. doi: 10.1007/s12518-015-0165-0
[11]	ROSSI G, TANTERI L, TOFANI V, et al. Multitemporal UAV surveys for landslide mapping and characterization[J]. Landslides, 2018, 15(5): 1045-1052. doi: 10.1007/s10346-018-0978-0
[12]	VALKANIOTIS S, PAPATHANASSIOU G, GANAS A. Mapping an earthquake-induced landslide based on UAV imagery: Case study of the 2015 Okeanos landslide, Lefkada, Greece[J]. Engineering Geology, 2018, 245: 141-152. doi: 10.1016/j.enggeo.2018.08.010
[13]	董秀军, 许强, 佘金星, 等. 九寨沟核心景区多源遥感数据地质灾害解译初探[J]. 武汉大学学报(信息科学版), 2020, 45(3): 432-441. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202003015.htm DONG X J, XU Q, SHE J X, et al. Preliminary study on interpretation of geological hazards from multi-source remote sensing data in Jiuzhaigou core scenic area[J]. Journal of Wuhan University(Information Science Edition), 2020, 45(3): 432-441. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202003015.htm
[14]	SCHULZ W H. Landslide susceptibility revealed by LiDAR imagery and historical records, Seattle, Washington[J]. Engineering Geology, 89(1/2): 67-87.
[15]	WENL, FUMIO Y, YOSHIHISA M. Detection of earthquake-induced landslides during the 2018 Kumamoto Earthquake using multitemporal airborne lidar data[J]. Remote Sensing, 2019, 11(19): 2292. doi: 10.3390/rs11192292
[16]	王敏杰, 李天斌, 孟陆波, 等. 四川″Y字形″断裂交汇部应力场反演分析[J]. 铁道科学与工程学报, 2015, 12(5): 1088-1095. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201505016.htm WANG M J, LI T B, MENG L B, et al. Inversion analysis of stress field at the intersection of "Y-shaped" fault in Sichuan[J]. Journal of Railway Science and Engineering, 2015, 12(5): 1088-1095. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201505016.htm
[17]	王欣, 方成勇, 唐小川, 等. 泸定M_s 6.8级地震诱发滑坡应急评价研究[J/OL]. 武汉大学学报(信息科学版): 1-15[2022-12-02]. DOI: 10.13203/j.whugis20220586. WANG X, FANG C Y, TANG X C, et al. Study on emergency assessment of landslide induced by Luding M_s 6.8 earthquake[J/OL]. Journal of Wuhan University(Information Science Edition): 1-15[2022-12-02]. DOI: 10.13203/j.whugis20220586.(in Chinese with English abstract)
[18]	周荣军, 赖敏, 李大虎, 等. 2011年4月10日四川炉霍M_s5.3级地震强震记录与震害特点[J]. 成都理工大学学报(自然科学版), 2013, 40(2): 217-224. https://www.cnki.com.cn/Article/CJFDTOTAL-CDLG201302014.htm ZHOU R J, LAI M, LI D H, et al. Strong earthquake records and damage characteristics of the Sichuan Luhuo M_s 5.3 earthquake on April 10, 2011[J]. Journal of Chengdu University of Technology(Natural Science Edition), 2013, 40(2): 217-224. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-CDLG201302014.htm
[19]	庞鑫, 袁明, 卢渊, 等. 基于无人机LiDAR仿地飞行技术的高陡边坡危岩体快速识别方法[J]. 地质科技通报, 2023, 42(6): 21-30. doi: 10.19509/j.cnki.dzkq.tb20220427 LONG X, YUAN M, LU Y, et al. A rapid identification method for high and steep slope dangerous rock body based on drone LiDAR geomorphic flight technology[J]. Bulletin of Geological Science and Technology, 2023, 42(6): 21-30. (in Chinese with English abstract) doi: 10.19509/j.cnki.dzkq.tb20220427
[20]	郭晓光. 大渡河流域石棉-泸定段大型古滑坡与河谷侵蚀的孕生关系[D]. 成都: 成都理工大学, 2015. GUO X G. The relationship between large-scale ancient landslides and valley erosion in the Shimian Luding section of the Dadu River Basin[D]. Chengdu: Chengdu University of Technology, 2015. (in Chinese with English abstract)
[21]	赵波. 川西北高烈度峡谷区大型地震滑坡成因机制研究[D]. 成都: 成都理工大学, 2019. ZHAO B. Research on the genesis mechanism of large-scale earthquake landslides in high-intensity canyon areas of northwest Sichuan[D]. Chengdu: Chengdu University of Technology, 2019. (in Chinese with English abstract)
[22]	庄建琦, 崔鹏, 葛永刚, 等. 5.12汶川地震崩塌滑坡分布特征及影响因子评价: 以都江堰至汶川公路沿线为例[J]. 地质科技情报, 2009, 28(2): 16-22. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ200902003.htm ZHUANG J Q, CUI P, GE Y G, et al. 5.12 Evaluation of the distribution characteristics and impact factors of landslides in the Wenchuan earthquake: Taking the Dujiangyan Wenchuan highway as an example[J]. Geological Science and Technology Information, 2009, 28(2): 16-22. (in Chinese with English abstract) https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ200902003.htm