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Mining Subsidence Based on Integrated sbas-insar and Unmanned Aerial Vehicles Technology

Journal of Ocean University of China 2025年第1期24卷 113-129页

作者： CHEN Xuewei CHEN Jianping WANG Genhou ZHANG Qian ZHENG Yanwei School of Earth Sciences and Resources China University of GeosciencesBeijing 100083China Beijing Key Laboratory of Development and Research for Land Resources Information Beijing 100083China

The Small Baseline Subset insar(sbas-insar)and unmanned aerial vehicles(UAVs)as common ocean-land technologies,have been extensively applied in subsidence,glacial movement,surface deformation,and maritime positioning and navigation.A novel method integrating sbas-insar and UAV photogrammetry is used to analyze ground subsidence deformation in the Gesar gold mine located in Maqu,Northwest *** approach uses sbas-insar to calculate two-dimensional deformation data for capturing ascending and descending *** method can provide precise information on small-sized deformations within mining *** deformation data obtained from UAVs and the vertical deformation data derived from insar are integrated to generate comprehensive and accurate ground subsidence data from the mining *** demonstrate that using a combined insar(vertical)and UAV technique to analyze surface subsidence in mining districts resolves inconsistency between the line-of-sight and deformation ***,the incoherence issue of insar in regions with large deformation gradients is addressed,while the inherent errors of UAV monitoring of mining surface subsidence are *** genetic algorithm(GA)-backpropagation(BP)neural network algorithm is combined with insar data to predict subsidence in collapsed *** observed,the GA-BP algorithm has the smallest residual under the same training ***,the GA-BP neural network model can effectively predict surface subsidence in mining areas and can be used for subsequent subsidence prediction.

关键词： sbas-insar two-dimensional deformation solution UAV photogrammetry data fusion subsidence prediction

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Early Identification of Geological Hazards Along the Power Transmission Line in Weinan Based on sbas-insar

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APPLIED SCIENCES-BASEL 2025年第2期15卷 920-920页

作者： Shan, Bo Qi, Jianguo Tian, Wucheng Zhu, Kuanxing Jin, Tie Yang, Qingkun An, Xiguan Yang, Guang Hu, Qi Cao, Chen Northeast Elect Power Design Inst Co Ltd China Power Engn Consulting Grp Changchun 130000 Peoples R China Jilin Univ Coll Construct Engn Changchun 130026 Peoples R China

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation of the transmission project along the route from Baishui County, Weinan City, Shaanxi Province to Lantian County, Xi'an City, Shaanxi Province. Small baseline subset interferometric synthetic aperture radar (sbas-insar) technology was used to acquire the surface deformation data of the study area from 4 February 2018 to 21 May 2023. The deformation data were spatially analyzed through kernel density analysis, which quickly and intuitively identified 52 potential geological hazard points in the region, including eight landslides and 44 ground subsidence. Detailed field investigations of the hazards confirmed the accuracy of the identification results. A thorough analysis of typical hazards, such as landslide No. 9 and ground subsidence No. 29, revealed severe deformation, posing a threat to the proposed transmission project. This study indicates that combining insar, kernel density analysis, and field investigations can accurately and quickly identify geological hazards around transmission lines, providing support for the site selection and implementation of transmission projects.

关键词： power transmission engineering sbas-insar early identification of geological hazards kernel density analysis field investigation

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Dual impacts of urbanization and precipitation on subsidence in Chongqing revealed by sbas-insar

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ENVIRONMENTAL EARTH SCIENCES 2025年第8期84卷 1-18页

作者： Zhang, Jinlai Tao, Yuxiang Kou, Pinglang Jin, Zhao Huang, Yijian Cui, Jinhu Liang, Wenli Liu, Rui Chongqing Univ Posts & Telecommun Chongqing Engn Res Ctr Spatial Big Data Intelligen Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Tourism Multisource Data Percept & Decis Minist Culture & Tourism TMDPD MCT Chongqing 400065 Peoples R China Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Sichuan Peoples R China Chinese Acad Sci Inst Earth Environm State Key Lab Loess & Quaternary Geol Xian 710061 Peoples R China Chongqing Normal Univ Sch Geog & Tourism Chongqing Key Lab GIS Applicat Chongqing Peoples R China

Southwest China's rapid urbanization has intensified ground subsidence, threatening infrastructure and the environment. However, the complex mechanisms behind urban subsidence are still poorly understood. This study harnesses 44 Sentinel-1 A images and insar to gauge spatiotemporal patterns of urbanized subsidence in Chongqing from 2021 to 2022. Results reveal subsidence up to 8 mm/yr across 26 km2, with hotspots near subway lines, construction sites, and agricultural regions. Subsidence along metro lines likely stems from excavation and vibrations during operation. The proliferation of high-rise buildings has sharply increased loading, accelerating consolidation and deformation. Agricultural land experiences substantial subsidence due to groundwater extraction. Moreover, precipitation demonstrates a negative correlation with subsidence. Heavy rainfall during spring and summer replenishes soil moisture, alleviating consolidation. Meanwhile, reduced precipitation in fall and winter diminishes support from bedrock, aggravating subsidence. These findings underscore anthropogenic activities as primary drivers of urban subsidence. Monitoring infrastructure and minimizing land conversion is critical for mitigation. This study demonstrates insar's prowess in unveiling subsidence mechanisms, guiding sustainable urban development.

关键词： Ground subsidence Precipitation Urbanization sbas-insar

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Change detection of slow-moving landslide with multi-source sbas-insar and Light-U2Net

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 136卷

作者： Cai, Jianao Ming, Dongping Liu, Feng Ling, Xiao Liu, Ningjie Zhang, Liang Xu, Lu Li, Yan Zhu, Mengyuan China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China China Univ Geosci Beijing Frontier Sci Ctr Deep Time Digital Earth Beijing 100083 Peoples R China China Univ Geosci Beijing Hebei Key Lab Geospatial Digital Twin & Collaborat Beijing 100083 Peoples R China Jiangsu Normal Univ Sch Geog Geomat & Planning Xuzhou 221116 Peoples R China Informat Engn Univ Inst Geospatial Informat Zhengzhou 450001 Peoples R China

Interferometric Synthetic Aperture Radar (insar) techniques are commonly used approach for identifying Slow- moving Landslide (SML). However, most SML boundary identification with deep learning are based on single- source insar data, which cannot fully explore the dynamic process of destabilization, and are inefficient due to high model complexity. Meanwhile, research on automatic procession with multi-source insar data is few. To enhance efficiency in geohazard monitoring, this paper proposed an automatic framework for Boundary- Changed Slow-moving Landslide (BCSML) detection by integrating multi-source Small Baseline Subset insar (sbas-insar), Convolutional Neural Network (CNN), and change detection methodologies. Firstly, surface deformation was estimated using multi-source sbas-insar. Then, a novel and effective Light-U2Net was constructed with decreased complexity to identify Significant Deformation Zone (SDZ) and locate SML candidate. Finally, BCSMLs were identified using a change detection approach based on newly defined geometric measurements. Two study areas were selected to test the model's performance: Zayu County and the Nu-Lancang River parallel flow (NLPF) area (in China). The proposed Light-U2Net model achieves high Precision (80.1 %), Recall (80.2 %), and F1-scores (80.1 %) in Zayu County. Additionally, the model's complexity has reduced by 42.4 % without compromising identification accuracy compared to the original model. The pre-trained model was then applied to the NLPF area, and a total of 273 BCSMLs were detected, with 176 identified as expanding and 97 as shrinking. BCSML identification accuracy can reach to 90.47 %. The results have proved that the proposed framework with the Light-U2Net model is effective and practically potential in landslide disaster prevention.

关键词： sbas-insar Convolutional neural network Boundary-changed slow-moving landslide Change detection Significant deformation zones

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Susceptibility evaluation of highway landslide disasters based on sbas-insar: a case study of S211 highway in Lanping County

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NATURAL HAZARDS 2025年第3期121卷 2587-2612页

作者： Li, Yimin Ji, Peikun Liu, Shiyi Zhao, Juanzhen Yang, Yiming Yunnan Univ Sch Earth Sci Kunming 650500 Peoples R China Res Ctr Domest High Resolut Satellite Remote Sensi Kunming 650500 Peoples R China Yunnan Univ Inst Int Rivers & Ecosecur Kunming 650500 Yunnan Peoples R China Jiangxi Geol Survey & Explorat Inst Basic Geol Survey Inst Nanchang Jiangxi Peoples R China Jiangxi Nonferrous Geol Mineral Explorat & Dev Ins Nanchang Jiangxi Peoples R China Hainan Ecol Environm Geol Survey Inst Haikou Hainan Peoples R China

Evaluation of landslide susceptibility along highways is critical for risk management in engineering development, construction, and operation and maintenance. The research target is the S211 Highway in Lanping County, Nujiang Prefecture, Yunnan Province, with its buffer zone extending 10 km as the research area. Eight evaluation factors are selected for the study, including slope, slope aspect, vegetation coverage, distance from the water system, rock group, rainfall, distance from the fault, and elevation. The findings of the susceptibility evaluation were classified into five categories, and the susceptibility grades of landslide disasters in the study area were evaluated using the information value and logistic regression coupling model. The accuracy of the coupling model was evaluated by the ROC curve and AUC value. The deformation rate in the study area was estimated by processing 28 Sentinel-1 A satellite images captured from January to December 2019 using the sbas-insar technology and was used to optimize the landslide susceptibility grade. The results show that the extremely high and high-risk areas of the information value-logistic regression coupling model account for 28.33% of the total area of the study area, which constitutes nearly 83.82% of the historical landslide disaster sites, mainly occupying areas along highways with low vegetation coverage and within 2000 m from rivers. The AUC values in the accuracy verification reach 0.843, indicating that the evaluation model can accurately predict the landslide susceptibility. The vulnerability grade of landslide geological disaster in the entire evaluation unit is significantly increased by optimizing the result of the surface deformation obtained by sbas-insar technology. A total of 79,587 grid cells were added to the extremely high susceptibility level region. This technique may optimize the evaluation results of landslide hazard susceptibility and provide decision support for disaster prevention and mai

关键词： Information value-logistic regression coupling model sbas-insar S211 highway Landslide susceptibility

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Exploration of slope-type geological hazard susceptibility evaluation based on dynamic correction of sbas-insar technology: A case study of Kang County in Gansu Province

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ECOLOGICAL INFORMATICS 2025年 85卷

作者： Li, Rongwei Wang, Pengwei Tan, Shucheng Zhou, Yangbiao Liu, Lifeng Gou, Chaodong Yu, Yalan Yunnan Univ Inst Int Rivers & Ecosecur Kunming 650500 Yunnan Peoples R China Yunnan Int Joint Lab Crit Mineral Resources Kunming 650500 Yunnan Peoples R China Yunnan Univ Sch Earth Sci Kunming 650500 Yunnan Peoples R China

The assessment of geological disaster susceptibility is crucial for early warning, prevention, and mitigation of disasters. Current methods for evaluating geological disasters rely primarily on static factors that cause disasters and historical data at disaster sites or dynamic factors that are introduced into the evaluation model;however, relatively few studies have focused on the use of dynamic data corrections on the basis of susceptibility evaluation. Interferometric synthetic aperture radar (insar) technology can effectively capture surface deformation features. Therefore, this study introduces a correction framework based on small baseline subset interferometric synthetic aperture radar (sbas-insar) data to improve the accuracy of geohazard susceptibility assessment results. This correction framework corrects the susceptibility results of the Random Forest (RF) model, which is based on 12 static factors and historical hazard data, using surface deformation data measured by the sbasinsar technique. Compared with the assessments based on static factors via the RF model, the revised results indicated a reduction in the low susceptibility area by 282.30 km2 and increases in the medium, high, and extremely high susceptibility areas by 90.00 km2, 138.98 km2, and 53.34 km2, respectively. The revised evaluation also revealed a lower density of disaster sites in the low- to high-susceptibility zones than before the revision but a higher density in the extremely high-susceptibility zones. This suggests a greater concentration of disaster sites in areas of higher susceptibility, aligning the zoning results more closely with the actual distribution of the sites. Thus, the proposed method has important reference value for improving the accuracy of regional geological disaster susceptibility evaluation.

关键词： Geological disaster Susceptibility sbas-insar Kang County

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融合概率积分法与sbas-insar的开采沉陷计算方法

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矿业科学学报 2025年第1期10卷 48-56页

作者：丁星丞李培现康新亮王明亮张涛郝登程中国矿业大学(北京)地球科学与测绘工程学院北京100083 中国矿业大学(北京)内蒙古研究院内蒙古鄂尔多斯017010 西山煤电(集团)有限责任公司地质处山西太原030053 中国资源卫星应用中心北京100094

针对开采沉陷概率积分法参数反演过程中存在容易陷入局部最优解、反演结果无法准确预计边缘沉降的问题,提出将蜣螂优化算法应用于概率积分法参数反演,结合sbas-insar沉降监测值获取矿区整体沉降信息。首先依据sbas-insar技术监测形变的... 详细信息

针对开采沉陷概率积分法参数反演过程中存在容易陷入局部最优解、反演结果无法准确预计边缘沉降的问题,提出将蜣螂优化算法应用于概率积分法参数反演,结合sbas-insar沉降监测值获取矿区整体沉降信息。首先依据sbas-insar技术监测形变的梯度信息获取可靠的矿区小梯度形变区域沉降值;然后将寻优能力强、准确度高的蜣螂优化算法应用于概率积分法参数反演,计算获取矿区大梯度形变区域沉降值;最后基于距离平方加权法将概率积分法预计沉降值与sbas-insar沉降监测值融合计算,得到开采沉陷变形信息。以山西省古交市马兰矿10604工作面作为研究对象,采用实地62个水准监测点数据与25景Sentinel-1A数据进行实验分析。结果表明,蜣螂优化算法参数反演结果优异,数据融合后可获取准确的沉降信息,计算精度相对于单独使用sbas-insar和概率积分法分别提高59%与32%。

关键词：开采沉陷概率积分法蜣螂优化算法 sbas-insar 数据融合

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Analysis of Uneven Settlement of Long-Span Bridge Foundations Based on sbas-insar

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REMOTE SENSING 2025年第2期17卷 248-248页

作者： Zhang, Kaixuan Xiao, Weifo Zhu, Haojie Ning, Shaowei Huang, Shenjiang Jin, Dongxing Rong, A. Thapa, Bhesh Raj Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China Pokhara Univ Universal Engn & Sci Coll Lalitpur 44700 Nepal Nepal Acad Sci & Technol Lalitpur 44700 Nepal

Bridge foundation settlement monitoring is crucial for infrastructure safety management, as uneven settlement can lead to stress redistribution, structural damage, and potentially catastrophic collapse. While traditional contact sensors provide reliable measurements, their deployment is labor-intensive and costly, especially for long-span bridges. Current remote sensing methods have not been thoroughly evaluated for their capability to detect and analyze complex foundation settlement patterns in challenging environments with multiple influencing factors. Here, we applied Small Baseline Subsets Synthetic Aperture Radar Interferometry (sbas-insar) technology to monitor foundation settlement of a long-span bridge. Our analysis revealed distinct deformation patterns: uplift in the north bank approach bridge foundation and the left-side main bridge foundation (maximum rate: 36.97 mm/year), concurrent with subsidence in the right-side main bridge foundation and south bank approach bridge foundation (maximum rate: 35.59 mm/year). We then investigated the relationship between these settlement patterns and various environmental factors, including geological conditions, Sediment Transport Index (STI), Topographic Wetness Index (TWI), precipitation, and temperature. The observed settlement patterns were attributed to the combined effects of stratigraphic heterogeneity, dynamic hydrological conditions, and seasonal climate variations. These findings demonstrate that sbas-insar technology can effectively capture complex bridge foundation deformation processes, offering a cost-effective alternative to traditional monitoring methods. This advancement in bridge monitoring technology could enable more widespread and frequent assessment of bridge foundation stability, ultimately improving infrastructure safety management.

关键词： sbas-insar uneven settlement bridge foundation deformation monitoring

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基于StaMPS和sbas-insar的隆尧地裂缝及周边地表形变监测

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大地测量与地球动力学 2025年第4期45卷 418-425页

作者：侯祖行杨成生董继红陈利东王坤赵子逸河北省地质矿产勘查开发局第六地质大队(河北省地质矿产勘查开发局航空测量应用中心) 石家庄市050000 长安大学地质工程与测绘学院西安市710054 自然资源部地裂缝地质灾害重点实验室南京市210004 西部矿产资源与地质工程教育部重点实验室西安市710054 四川省综合地质调查研究所稀有稀土战略资源评价与利用四川省重点实验室成都市610081

基于StaMPS和sbas-insar技术获取2017-03—2022-10河北隆尧地裂缝及周边的地表形变结果。结果显示,2种技术监测结果较为一致,说明结果可靠;巨鹿县周边的地面沉降较严重,已形成较大规模沉降区,最大形变速率超过-30 mm/a;提取的时序结果... 详细信息

基于StaMPS和sbas-insar技术获取2017-03—2022-10河北隆尧地裂缝及周边的地表形变结果。结果显示,2种技术监测结果较为一致,说明结果可靠;巨鹿县周边的地面沉降较严重,已形成较大规模沉降区,最大形变速率超过-30 mm/a;提取的时序结果均呈周期性变化,且与地下水水位和降水量变化一致性较高;隆尧地裂缝两侧地表的最大形变速率差超过40 mm/a,最大形变量差超过200 mm,实地调查发现,地裂缝引发的地表差异形变对当地造成巨大影响。

关键词：地面沉降 StaMPS sbas-insar 隆尧地裂缝地下水降水量

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A method for monitoring three dimensional surface deformation in mining areas combining sbas-insar, GNSS and probability integral method

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Tao, Qiuxiang Liu, Ruixiang Li, Xuepeng Gao, Tengfei Chen, Yang Xiao, Yixin He, Huzhen Wei, Yunguang Shandong Univ Sci & Technol Coll Geodesy & Geomat 579 Qianwangang Rd Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Demonstrat Ctr Expt Surveying & Mapping Educ 579 Qianwangang Rd Qingdao 266590 Peoples R China Shandong GEO Geosurveying & Mapping Inst 11101 East Erhuan Rd Jinan 250003 Peoples R China

In the process of mineral resource extraction, monitoring surface deformation is crucial for ensuring the safety of engineering and ground infrastructure. Monitoring complete three-dimensional surface deformation is particularly significant. Traditional synthetic aperture radar (insar) technology provides deformation components only along the line of sight (LOS) and often lacks sufficient effective data in vegetation-covered mining areas and mining subsidence centers. To address this, this study proposes a method (sbas-PIM) that combines sbas-insar with the probabilistic integral method (PIM). This method leverages high-coherence points in mining areas and GNSS data from vegetation-covered regions to invert the parameters required by PIM, thus obtaining three-dimensional surface deformation results. The proposed method allows for the acquisition of three-dimensional deformation data with fewer insar points and GNSS data, significantly reducing labor costs and addressing the gap in insar monitoring of three-dimensional surface deformation in densely vegetated areas. Additionally, it accounts for the mutual influence of multiple adjacent working faces. Finally, through the application to a mining area in Heze, China, the maximum displacements in the vertical, east-west, and north-south directions were obtained as -2011, -418, and - 281 mm, respectively. The correlation coefficients between the vertical and east-west directions and GNSS data were both greater than or equal to 0.9, indicating that this method can effectively monitor the three-dimensional surface deformation of the mining area.

关键词： 3D displacement sbas-insar Probability integral method Vegetation-covered mining areas Adjacent multiple working faces

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