Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach

Du, Zheyuan; Liu, Ge; Ng, Alex Hay‐Man; Zhu, Qinggaozi; Horgan, Finbarr G.; Zhang, Qi

doi:10.1016/j.scitotenv.2020.137125

Cited by 72 publications

(28 citation statements)

References 26 publications

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“…We generated line-of-sight (LOS) deformation velocity maps over Wuhan for three periods: 2007-2010 (ALOS-1), 2008-2010 (Envisat), 2015-2019 (Sentinel-1). As the InSAR results are along the LOS directions, assuming that deformation occurs primarily in the vertical direction, all of the results are converted from line-of-sight (d los ) into vertical (d v ) with respect to the corresponding incidence angles (θ): [43,44]. Figure 2a highlights the vertical velocity map from 2007 to 2010 calculated with L-band ALOS-1 data, Figure 2b shows the vertical velocity over a part of the study area from 2008 to 2010 with Envisat data because of the limited coverage of Envisat images (coverage outline of Envisat shown in Figure 2a).…”

Section: Velocity Of Land Subsidencementioning

confidence: 99%

Ground Deformation of Wuhan, China, Revealed by Multi-Temporal InSAR Analysis

et al. 2020

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Wuhan, the largest city in central China, has experienced rapid urban development leading to land subsidence as well as environmental concerns in recent years. Although a few studies have analyzed the land subsidence of Wuhan based on ALOS-1, Envisat, and Sentinel-1 datasets, the research on long-term land subsidence is still lacking. In this study, we employed multi-temporal InSAR to investigate and reveal the spatiotemporal evolution of land subsidence over Wuhan with ALOS-1, Envisat, and Sentinel-1 images from 2007–2010, 2008–2010, 2015–2019, respectively. The results detected by InSAR were cross-validated by two independent SAR datasets, and leveling observations were applied to the calibration of InSAR-derived measurements. The correlation coefficient between the leveling and InSAR has reached 0.89. The study detected six main land subsidence zones during the monitoring period, with the maximum land subsidence velocity of −46 mm/a during the 2015–2019 analysis. Both the magnitude and the extent of the land subsidence have reduced since 2017. The causes of land subsidence are discussed in terms of urban construction, Yangtze river water level changes, and subsurface water level changes. Our results provide insight for understanding the causes of land subsidence in Wuhan and serve as reference for city management for reducing the land subsidence in Wuhan and mitigating the potential hazards.

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Section: Velocity Of Land Subsidencementioning

confidence: 99%

Ground Deformation of Wuhan, China, Revealed by Multi-Temporal InSAR Analysis

et al. 2020

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“…In addition to the tailings dam inventory data that can be obtained from high-resolution satellite images, satellite data from multiple sources can be used to monitor critical features of tailings dams remotely. Interferometric synthetic aperture radar (InSAR) and light detection and ranging (LiDAR) data can be used to monitor structural changes in and displacement of a tailings dam before failure [29,53,54]. Optical and thermal satellite data can be used to monitor environmental impacts such as water and soil pollution near tailings dams [55,56].…”

Section: Tailings Dams In the Jing-jin-ji Regionmentioning

confidence: 99%

“…Du et al [29] developed a new INSAR time series approach to derive ground displacement maps for dam safety monitoring. Tailings facilities are often built in areas with complex topography and geomorphology.…”

Section: Introductionmentioning

confidence: 99%

Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

Chen

Zhang

et al. 2020

Remote Sensing

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The timely and accurate mapping and monitoring of mine tailings dams is crucial to the improvement of management practices by decision makers and to the prevention of disasters caused by failures of these dams. Due to the complex topography, varying geomorphological characteristics, and the diversity of ore types and mining activities, as well as the range of scales and production processes involved, as they appear in remote sensing imagery, tailings dams vary in terms of their scale, color, shape, and surrounding background. The application of high-resolution satellite imagery for automatic detection of tailings dams at large spatial scales has been barely reported. In this study, a target detection method based on deep learning was developed for identifying the locations of tailings ponds and obtaining their geographical distribution from high-resolution satellite imagery automatically. Training samples were produced based on the characteristics of tailings ponds in satellite images. According to the sample characteristics, the Single Shot Multibox Detector (SSD) model was fine-tuned during model training. The results showed that a detection accuracy of 90.2% and a recall rate of 88.7% could be obtained. Based on the optimized SSD model, 2221 tailing ponds were extracted from Gaofen-1 high resolution imagery in the Jing–Jin–Ji region in northern China. In this region, the majority of tailings ponds are located at high altitudes in remote mountainous areas. At the city level, the tailings ponds were found to be located mainly in Chengde, Tangshan, and Zhangjiakou. The results prove that the deep learning method is very effective at detecting complex land-cover features from remote sensing images.

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“…For example, the Feijão tailings dam in Brumadinho, Minas Gerais, Brazil, collapsed on 25 January 2019, causing 259 deaths and 11 missing person cases [7]. Furthermore, approximately 10 million cubic meters of tailings-water mixtures flowed into the surrounding streams and rivers, causing serious ecological and environmental damage [8,9]. According to statistics, there have been approximately 50 tailings dam accidents in the past 20 years, and the frequency of accidents is on the rise [10].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Mesostructure Deformation Characteristics of Unsaturated Tailings with Different Moisture Content

Geng

Wang

Wei

et al. 2020

Water

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A portion of the accumulated tailings in a tailings pond exhibits an unsaturated state. The mechanical properties of unsaturated tailings affect the safety and stability of tailings dams. To investigate the effect of moisture content on the deformation characteristics of unsaturated tailings in the mesoscale, a special testing apparatus is applied to experimentally study the settlement deformation and mesostructure evolution of unsaturated tailings under continuous load. The results show that the mesostructure deformation of unsaturated tailings with different moisture contents under load is the same and can be divided into four stages: pore compression, elastic deformation, structure change, and further compaction. However, the critical pressures of the four stages are significantly different; there is an optimal moisture content corresponding to the maximum deformation resistance. Moreover, the influence of the liquid bridge regime on the mesostructure deformation of unsaturated tailings is discussed in this paper.

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Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach

Cited by 72 publications

References 26 publications

Ground Deformation of Wuhan, China, Revealed by Multi-Temporal InSAR Analysis

Ground Deformation of Wuhan, China, Revealed by Multi-Temporal InSAR Analysis

Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

Experimental Study of Mesostructure Deformation Characteristics of Unsaturated Tailings with Different Moisture Content

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