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Advanced DINSAR analysis on dam stability monitoring: A case study in the Germano mining complex (Mariana, Brazil) with SBAS and PSI techniques
Remote Sensing Applications: Society and Environment 16 (2019) 100267
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Remote Sensing Applications: Society and Environment journal homepage: http://www.elsevier.com/locate/rsase
Advanced DINSAR analysis on dam stability monitoring: A case study in the Germano mining complex (Mariana, Brazil) with SBAS and PSI techniques Fabio F. Gama a, *, Waldir R. Paradella a, Jos�e C. Mura a, Cleber G. de Oliveira b a b
National Institute for Space Research-INPE, Av. dos Astronautas 1758, S~ ao Jos�e dos Campos, SP, CEP 12227-010, Brazil VISIONA Tecnologia Espacial, Estrada Dr. Altino Bondensan 500, S~ ao Jos�e dos Campos, SP, CEP 12247-016, Brazil
A R T I C L E I N F O
A B S T R A C T
Keywords: Tailings Dam SBAS PSI TerraSAR
This work presents an investigation of detection and monitoring of surface motions using Advanced Differential SAR Interferometry (A-DInSAR) with SBAS (Small Baseline Subset) and PSI (Persistent Scatterer Interferometry) techniques in the Germano iron mining complex region (Mariana, Brazil), after the collapse of the Fund~ ao tailing dam occurred on November 5th, 2015. The research was carried out aiming to provide useful information about ground movement monitoring, planning and risk assessment in the area after the tragic event. The analysis was performed using 46 TerraSAR-X images and a Digital Elevation Model (DEM) obtained by Pleiades data. ADInSAR processing provided the removal of the atmospheric phase artifacts, baseline related errors, DEM height error estimation and finally the ground displacement determination. The results pointed out that with both techniques it was possible to assess the stability conditions of the Germano mining assets, particularly the main structures of the Germano dam and the detection of small settlements in the mining tailings storage, pointing out that these may be an important components for risk management of open pit mining operations, including tailings dams.
1. Introduction Open pit iron mining generally occupies extensive areas including portions of land adjacent to the mine pit, with presence of pipelines, industrial plant, waste piles as well as tailing dams. Instabilities of these mining structures are normally expected due to the intense rock mass movement, steep slope of mine benches, blasting practices and rainfall. For this kind of activity, tailings dams have to be built to store mining waste material from the beneficiation of iron ore. In the afternoon of ~o November 5th, 2015, an enormous tailings dam called Funda collapsed, which contained around 55 million cubic meters of tailing ~o dam was part of the Germano iron mining Complex, materials. Funda �tero and it was located in a region called Iron Ore Quadrangle (Quadrila Ferrífero – QF), municipality of Mariana (Minas Gerais State). As a consequence, 32.6 million of cubic meters of mining waste material spilled from the dam and reached the Bento Rodrigues village, 2.5 km far ~o, which was completely flooded and destroyed by mud from Funda slides. Other villages and rural districts in the Gualaxo River valley, like Paracatu de Baixo and Gesteira, were also affected by these mud waves. This tragic event has assumed enormous proportions, polluting more than 650 km of watercourses from the Doce River basin up to the
Atlantic Ocean (Espírito Santo State), and involving the Brazilian Atlantic Forest (one of the world’s biodiversity hot spots), estuarine, coastal and marine environments (Morgenstern et al., 2016; Carmo et al., 2017). It was considered the largest social-environmental disaster in the Brazilian history and one the world’s largest disaster involving tailings dams (IBAMA, 2015). With the techniques of Advanced Differential Interferometric SAR (A-DInSAR) based on satellite systems, a powerful tool for monitoring ground deformation in different applications (Lanari et al., 2004; Gama et al., 2017) is becoming more relevant due to several reasons: they allow geologists/mining engineers to detect and monitor surface dis placements over large areas with a synoptic view, with reliable mea surements of surface motions with high accuracy (mm scale); the information can be extracted over a dense grid of points, and without the need for conventional ground instrumentation commonly used or field campaigns (Zhao et al., 2019). Basically, two kinds of A-DInSAR Time-Series approaches have been applied to obtain ground deforma tion information, the Small Baseline Subset (SBAS) and Persistent Scatterer Interferometry (PSI). The PSI technique is based on a stack of master referenced differ ential interferograms, with the identification of pixels, which scattering
* Corresponding author. National Institute for Space Research-INPE, Av. dos Astronautas, 1758, S~ ao Jos�e dos Campos, SP, 12227, Brazil. E-mail address: [email protected] (F.F. Gama). https://doi.org/10.1016/j.rsase.2019.100267 Received 6 June 2019; Received in revised form 11 September 2019; Accepted 21 September 2019 Available online 4 October 2019 2352-9385/© 2019 The Authors. Published by Elsevier B.V. This is an open (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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F.F. Gama et al.
Remote Sensing Applications: Society and Environment 16 (2019) 100267
tailings dams are built progressively “upstream” of the starter dam by incorporating tailings materials into the dam for support through the controlled deposition. While using tailings materials to build the dam reduce construction costs, upstream tailings dams are more susceptible to cracking, liquefaction and erosion, and may be less stable than other dam design in the occurrence of seismic events, because tailings mate rials may liquefy and lose their strength. The Germano tailings dam was constructed with a planned capacity around 116 million cubic meters, and up to 1995, this dam only received sandy waste. Since 1977 slime materials were also deposited, mainly in the central, north and north west sectors of the reservoir. The newest tailings dam in the Germano ~o, which was installed in 2008 using the same plant unit was Funda upstream type, followed by a water dam called Santar�em. The Germano tailings dam has several supporting structural dikes named as Sela, Selinha, Auxiliary dike1, Auxiliary dike 2, Tulipa and Baia-3. ~o rupture, it was noted that the After the tragic event of the Funda waste tailings released by the failure flowed over the Santar�em dam causing partial damage of its main body by eroding its structure. In addition, since the reservoir of the Fund~ ao passed by a rapid downgrade, damages were also detected on the Germano dam, particularly on the Sela, Tulipa and Selinha dikes, which are the supporting structures on the side walls of the remaining dam. An important aspect to monitor the Germano dam was due to the bigger volume of this mega structure, with ~o dam. A new collapse more than twice in volume of the collapsed Funda in the Germano dam could worsen the already critical situation in terms of lives and environment. According to the report on “Immediate causes of the failure of the ~o dam” (Morgenstern et al., 2016), the collapse was due to Funda liquefaction of the sandy tailings on the base of the dam, resulting in loss of mechanical resistance. This liquefaction process was caused by a chain of events and conditions related to the dam construction and op erations, such as (1) damage to the original starter dike that resulted in increased saturation, (2) deposition of slime in areas where this was not previously intended, (3) structural problems with a concrete conduit that caused the dam to be raised over the slime, and (4) a series of three small seismic shocks (Agurto-Detzel et al., 2016), that occurred 90 min before the rupture, and although the movements were quite small, they likely must have accelerated the failure process that was already well advanced. Fig. 1 shows the Pleiades image of the study area after the ~o with the highlighted Germano structures, which collapse of Funda were monitored by A-DInSAR. According to Pereira (2005), the iron ore tailings products from the study area present a mineralogical composition of Fe and SiO2 and subordinately, AL2O3. In addition, they are classified as sand and clay, with variation in size from fine to granular. Due to these heterogeneous nature and particle sizes, the tailings deposit exhibits a large variation of permeability along the vertical and horizontal direction, with several layers related to deposition events from distinct sources along the time. Up to 1995, the Germano dam only received sandy waste, but since 1977, slime material was also deposited. In essence, after the initial phase of material deposition, the weight of successive layers normally provokes reduction in terms of the reservoir volume. This reduction is expressed by settlements, which need to be moni tored within safety margin defined by geotechnical limits. It is also important to consider that two additional mechanisms also contributed to deformations within the tailings deposit: the break of mineral grains and the relative movements between them. Finally, iron ore tailings materials present low cohesion and are partly under saturated condi tions, due to the waste material transport/deposition using water and the rainfall, which contribute to settlements. Under very extreme con ditions, a liquefaction phenomenon can occur. A detailed study was carried out in the Germano tailings dam based on granulometry, void ratio, pore water pressure, loading-cycles, Scanning Electron Micro scopy (SEM) tests and numerical modelling, with the purpose of char acterizing the tailings material (Castilho, 2017) aiming at predicting liquefaction events. The results demonstrated a great heterogeneity of
properties do not vary along time (point-wise reflectors), denominated Persistent Scatterers or PS (Ferretti et al., 2001; Werner et al., 2003; Crosetto et al., 2011; Hopper et al., 2012; Paradella et al., 2015; Wang et al., 2011; Tomas et al., 2013; Di Martire et al., 2014; Milillo et al., 2016a, 2016b). With this approach, it is possible to get a temporal analysis of the interferometric phase of individual point targets as well as to extract accurate linear information related to surface target dis placements. This technique was used for concrete and earth-rock-filled dams. On the other hand, the SBAS technique consists of the combination of images in order to limit spatial decorrelation effects and the history of the displacement evolution is derived of the different interferograms connected in time. Surface displacement is related to distributed scat terers with homogeneous characteristics, such as low-vegetated surface, manmade structures, debris or desert areas, which exceed a certain coherence threshold. SBAS technique has been successfully used to detect non-linear ground deformation of volcanoes (Berardino et al., 2002; Usai., 2002; Lanari et al., 2004), mining benches (Gama et al., 2017; Mura et al., 2018), and some studies were also carried out to monitor concrete dams of hydric reservoirs (Zhou et al., 2016; Emadali et al., 2017) which were able to detect the structural movements and the water level, as well as settlement of these structures. This study presents an evaluation of the SBAS and PSI techniques to monitor ground motions on the remaining Germano tailings dam, after ~o dam. Particularly, The Germano dam differs from the collapse of Funda water dams in that it was built using sterile mining material rather than concrete or earth-rock-filled, being more susceptible to erosion and liquefaction processes. To this end, it was used a stack with 46 TerraSAR-X StripMap images, acquired under interferometric configu ration (sequential acquisition with same viewing geometry), which covered the period from December 2015 to April of 2016. 2. Study area The Germano Iron mining Complex is part of the Alegria Mining Complex, located in the easternmost portion of the Quadril� atero Ferrí ~o Francisco Craton. The Alegria fero (QF), southern border of the Sa Complex is related to the Alegria Sinclinal, a geological structure formed by Archean rocks of the Rio das Velhas Supergroup and Paleoproterozoic rocks of the Minas Supergroup (Dorr, 1969). The Cau^ e Formation of the Minas Supergroup hosts banded iron formations, locally known as ita birites, deposited in shallow marine passive margin settings. Itabiritos are characterized by chemical sediments and metassediments interlay ered with chert and/or quartz and iron oxides. Two major compositional types of itabirite, dolomitic and quartz itabirites, are found in the northwestern part of QF. The former consists of alternating dolomite-rich and hematite-rich bands, whereas the latter is formed with alternating quartz-rich and hematite-rich bands. Accessory min erals are chlorite, sericite, and apatite in both types (Spier et al., 2007). In addition, other ferruginous materials such as cangas, breccias, compact hematite, and non-ferruginous rocks such as quartzites, phyl lites, dolomitic phyllites, schists and metabasites are also mapped in the area. Massive and soft iron ores were formed due to supergene process acting during the Neogene over products associated to hydrothermal enrichments (Rosiere and Rolim, 2016). This mining complex is an important iron producer in the Brazil, ~o S.A., with open which is explored by the Brazilian SAMARCO Mineraça pit mines, waste piles, industrial plant, pipelines, tailings and water dams. The Germano plant had an installed production in 2017 of iron pellets of 30.5 million of metric tons and iron waste of 20.7 million of ~o dam in 2015, metric tons (Castilho, 2017). Up to the rupture of Funda the Germano Complex encompassed two mega tailings dams and a reservoir to store water for industrial reuse. The first tailings dam was Germano, which was installed in 1976 using the upstream method of construction with the implantation of a 70 m high start dike. Upstream 2
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Remote Sensing Applications: Society and Environment 16 (2019) 100267
Fig. 1. Germano mining complex– Mariana/MG.
the iron waste products, which makes liquefaction susceptibility anal ysis very complex. The accelerations from potential liquefaction triggers were measured by using an accelerometer, and the response of the whole structure to these requests was analyzed by using other auxiliary in struments such as ground-based radars and piezometers. Just after the ~o rupture, three IDS (Ingegneria Dei Sistemi) ground-based SAR Funda were installed and monitored the main dike and the Sela and Sela/Tu lipa dikes, from the period of November 2015 up to March 2016. No signal of ground displacements was detected. The results also demon strated that the accelerations monitored during the study period were insufficient to cause deformations in the dam, even though there were slight elevations in pore water pressure in some situations. The nu merical analysis showed that the horizontal deformation on a section on the centre of the reservoir was small and around 10 cm.
In order to validate the A-DInSAR results with in situ geodetic survey data, field monitoring measurements at discrete points were carried out by the geotechnical team of SAMARCO Company, providing surface motions information related to the Germano dam and its structures. These measurements were acquired by a robotic station Leica TM50 (0.6 mm þ 1.0 ppm of nominal accuracy). Taking into account that the prisms-station distances were around 800 m and the angular errors of the prisms and station were 2.5" (2.5 seconds), the nominal geodetic measurements error was �8 mm. Since, in theory, the zero reading can present the same nominal error, the geotechnical team of the SAMARCO Company established the limit of two times the nominal error (accu mulated error totalizing 16 mm) as a safety limit for detection of movements. Fig. 2 shows a prism installed on the top of a dike and the concrete base that was used to set the total station. The acceptable limit of nominal error (�16 mm) was projected to the satellite geometry (35.2 degrees of incidence angle), that corresponded to the interval of �13 mm. Measurements point (MP) values within this interval were considered as random errors, and values outside this in terval, probably represented real ground deformation. Prism measure ments were projected along the satellite line of sight (LoS), by multiplying the values by the cosine of the satellite incidence angle of each prism position used, in order to be compared with A-DInSAR measurement values. It is important to mention that the selected prisms that were used for validation were monitored with different periods when compared to the TerraSAR acquisitions. Fig. 3 plot illustrates the time relationship for both set of measurements. In addition to the SAMARCO’s geodetic field survey, INPE’s team also carried out a field campaign during the investigation in order to verify the presence of ground displacements, such as the presence of fractures and cracks on berms and benches along the main wall of the dam, on the supporting auxiliary dikes, and on traffic routes around and within the reservoir. The workflow in Fig. 4 shows the methodology used in this study for the SBAS and PSI techniques. Wilcoxon test was used to verify if A-DInSAR measurement values would be a similar displacement inference to the geotechnical mea surements. Wilcoxon is a statistical nonparametric test that compares two paired groups and calculates the differences between each set of pairs. This procedure verifies if two data populations have the same median value and, therefore, can be considered statistically similar. If
3. Methodology The A-DINSAR analysis was carried out using the SBAS and PSI techniques available in the SARSCAPE software version 5.4.1. A total of 46 TerraSAR-X images was used to continuous monitor the ground displacements of the Germano dam for a period of 17 months (December 2015–April 2016). The images were acquired in StripMap mode (TSXSM), on ascending orbits (looking azimuth ~80 degrees), revisiting time of 11 days, and incidence angle range of 33.9–36.5 degrees (near and far range). During the SBAS processing, a constraint was used on spatial and temporal baselines, which allowed generating a set of interferograms to perform the analysis. These interferograms were spatially filtered using multi-look processing and unwrapped using a Minimum Cost Flow (MCF) algorithm. Visual inspection was used to choose the best unwrapped interferograms. Due to the complex variation of topography in the study area (sup porting dikes with vertical walls and flat surface of the tailings storage), a high-resolution digital elevation model (DEM) was used to remove the topographic phase component for the SBAS analysis and to improve the geolocation of the deformation map. DEM was provided by the company VISIONA Space Technology with 1 m of spatial resolution, based on triplet of stereoscopic images with spatial resolution of 0.5 m, collected by the satellite Pleiades 1A on June 19th, 2016. 3
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Fig. 2. a) Prism installed; b) concrete base for Leica total station.
this is the case, then the measurement strategy based on the A-DInSAR may be presumed to be representative of the surface displacement expressed by prism values. 4. Results and discussion 4.1. SBAS processing Based on the stack of 46 co-registered SLC images, the interfero metric pairs were generated in relation to a reference image (11/07/ 2016), which perpendicular baseline was the lowest and this acquisition was nearly at the center of the time series, as illustrated in Fig. 5a. The interferograms were obtained with normal baseline restriction up to 15% of the critical baseline (6074.20 m) and temporal baseline of 45 days, totalizing 156 interferograms (Fig. 5b). Before the phase unwrapping process, they were spatially filtered by a complex multilook operation, with 4 looks in range and azimuth, and a coherence threshold of 0.40 was used. The tests indicated that the value 4 looks was sufficient to meet the Sbas processing, allowing to distinguish the main targets of interest. A spatial filter, corresponding 1200 m was used to remove the atmospheric phase artifacts. Distributed Ground Control Points (GCPs), in areas considered as stable, were selected assuming a zero-deformation reference for the refinement/re-flattening of the interferometric phase to perform the second inversion step. Atmospheric phase filtering and the estimate of the displacement time series were based on the same set of GCPs. The displacement rate in the area can be expressed through the
Fig. 3. Plot of acquisition of TerraSAR images and geotechnical surveying with prisms. Red line means the date of dam collapse. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4. Methodological workflow. 4
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Fig. 5. a) Acquisition time versus normal baseline. Yellow dot means reference image (11/07/2016), b) Interferometric pairs selected (solid lines). Yellow dot means reference image. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
velocity deformation map in LoS (Fig. 6a) computed with millimeter precision. Positive values correspond to surface motion toward the sat ellite (blue colors), negative values correspond to ground motion away from the satellite (red colors). The SBAS velocity deformation map (Fig. 6a) showed that great part of the study area was stable during the time span of the TSX-1 acquisi tions, particularly with a stability behavior for Sela, Tulipa and Selinha Dikes (green-yellowish regions). A noticeable subsidence was detected over the tailings inside of the Baia-3 Dike area, as well as along the access road on top of Auxiliary Dike 2 (yellow-reddish regions). The Standard Deviation of the velocity deformation map (Fig. 6b) showed an overall error between 0 to 3 mm/year for the SBAS mea surements (yellow-greenish regions), with the lowest values observed on top of the Sela, Tulipa and Selinha dikes, and on the mining headquarter (greenish regions). Some regions within the Baia-3 reservoir are related to very low values of Standard Deviation, and in some cases near zero. The site locations for the prisms (ST1, ST3, ST6, ST7), which were installed on the top of the Sela and Tulipa dikes, can be seen on the SBAS ground displacement map shown in Fig. 7a. The field picture of these supporting dikes is shown in Fig. 7b. The comparison of the in situ geodetic survey values (prisms ST1, ST3, ST6, ST7) to the SBAS corresponding MP can be seen in the plots of Fig. 8. It can be noticed a pattern of stability for all cases, with no
tendency for subsidence or uplift. The SBAS results showed a greater dispersion of measurements in relation to the total/station prism values, with a greater standard deviation too (Table 1). An important issue to consider is the error limit of �13 mm due to the total station accuracy, pointing out that all values do not extrapolate this stability acceptance limit. It was also detected subsidence related to settlements inside the reservoir of the Baia-3 dike, with displacement rate up to 150 mm/ year (Fig. 9a), and for the top of the Baia-3 dike with displacement rate up to 70 mm/year, respectively. In addition, in the Auxiliary Dike 2, which has an access road on its top, the accumulated displacement reached 86.81 mm, which corresponded to a deformation rate of 60.09 mm/year. Fig. 9b shows a view of Baia-3 reservoir during the field campaign. The Germano tailings dam is the biggest reservoir of the SAMARCO’s plant unit and the buttress sector holds an iron ore conveyor belt. This buttress sector was monitored by the geotechnical team of the company using 6 prisms (24ZI400, 24ZI401, 24ZI419, 24ZI430, 24ZI431, 24ZI435), that were installed on the higher, medium and lower part of the area. The location of each prism plotted over the SBAS deformation map can be seen in Fig. 10a, and a field picture of the iron ore conveyor belt on the buttress sector is shown in Fig. 10b. The behavior of the values of total station/reflective prisms and SBAS
Fig. 6. a) SBAS velocity deformation map, b) Standard deviation of the velocity deformation map. 5
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Fig. 7. a) Ground displacement map and the prims positions; b) Field view of the Sela and Tulipa Dikes.
Fig. 8. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding SBAS measurements, as well as the standard deviation observed: a) ST1, b) ST3, c) ST6 and d) ST7. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS. Table 1 Statistic test. Prism Id
Distance from PS to Prism (m)
Prisms Standard deviation (mm)
SBAS Standard deviation (mm)
ST1 ST3 ST6 ST7 24ZI400 24ZI401 24ZI419 24ZI430 24ZI431 24ZI435
2.97 0.59 2.95 2.57 0.76 1.88 4.97 3.43 3.13 4.87
0.343 0.509 0.718 0.640 3.318 2.935 2.969 4.405 4.497 3.760
1.169 1.599 1.344 1.376 3.284 3.103 3.445 1.964 2.383 2.732
Significance level: p < 0.05. 6
Wilcoxon test results N (cases)
T (W test)
p-value
μ1 ¼ μ2
9 9 9 9 15 5 15 15 15 12
10 21 3 15 43 7 48 28 41 31
0.139 0.859 0.021 0.374 0.334 0.893 0.495 0.069 0.281 0.530
Ok Ok Ok Ok Ok Ok Ok Ok Ok Ok
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Fig. 9. a) Ground displacement map of Baia-3 dike; b) View of Baia-3 dike during the field work.
measurements for the Germano buttress sector is illustrated in the plots of Fig. 11. The in situ geodetic survey and SBAS measurements values did not extrapolate the stability limit of the company (�13 mm), but a higher variance for the prism results was noticed, oscillating around zero value of deformation. For the reference location 24ZI400, a subsidence trend was characterized by both systems of deformation measurements (Fig. 11a). Wilcoxon test was used for the statistical validation, in order to verify if the SBAS measurements would provide a similar displacement infer ence to the in situ geodetic measurements. Table 1 shows the Wilcoxon results for the SBAS and prisms data, which pointed out similarity of the data for all cases by this test criterion.
acquisition. Table 2 shows the 46 interferometric pairs and the perpendicular baseline size (Bperp). The lowest observed value was 41.649 m and the highest value was 601.216 m. All interferograms were processed with 2 looks in azimuth and 1 look in range (in order to preserve the details of the small structures), coherence threshold of 0.58, and 1200 m of the atmospheric filter. PSI technique also requests one reference point or GCP (Ground Control Point), which was selected on a stable area, using the interferometric phase image of the reference image (master). To perform the atmospheric filtering and estimate the displacement time series, the same GCP that was used during the SBAS processing was chosen. PSI deformation map in LoS is shown in Fig. 13a. Regions that move away from the satellite are represented by reddish color (negative values), the regions with motion toward the satellite are in blueish colors (positive values), while greenish color represents stable regions. Standard deviation map of the average displacement rates shows errors between 0 to 2 mm/year for PS, with very low values for the top of the dikes and the mining headquarter (Fig. 13b). The map of deforma tion (Fig. 14a) showed that for the top of the Sela/Tulipa, Selinha dikes, and the mining headquarter, the average LoS velocity is very close to zero. In addition, few PS were detected within the tailings storage in the
4.2. PSI processing PSI processing was carried out based on 46 TerraSAR-X images and DEM obtained from the satellite Pleiades data. PSI technique also re quests an image to be used as reference, and in order to compare both ADInSAR results, it was chosen the same master image used during the SBAS processing (11/07/2016). Fig. 12a shows the relative position of the satellite during the acquisitions and corresponding dates. Fig. 12b shows the 46 possible pairs from the reference image by the dates of
Fig. 10. a) Prims location over the SBAS displacement map of Germano dam; b) View of the iron ore conveyor belt on the Germano dam buttress sector. 7
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Fig. 11. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding SBAS measurements: a) 24ZI400, b) 24ZI401, c) 24ZI419, d) 24ZI430, e) 24ZI431, f) 24ZI435. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS.
Fig. 12. a) Acquisition time versus relative position, yellow dot means reference image (11/07/2016); b) Interferometric pairs selected (solid lines). Yellow dot means reference image. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 8
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Table 2 The acquisitions dates of the master and slave TerraSAR-X images, perpendicular baseline (Bperp) and time interval relative to the master image. Pair
Master*
Slave*
Bperp (m)
Pair
Master*
Slave*
Bperp (m)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619
20151112 20151123 20151204 20151215 20151226 20160106 20160117 20160128 20160915 20160926 20161007 20161018 20161029 20161109 20161120 20161201 20161212 20161223 20170103 20170114 20170125 20170216 20170227
212.725 258.581 313.201 366.006 156.075 126.177 290.914 208.349 227.984 216.419 180.147 266.808 129.147 178.348 240.259 205.336 294.991 492.267 156.442 285.458 41.649 601.216 395.017
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 -
20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 –
20170310 20170321 20170401 20170412 20170423 20170504 20170515 20160208 20160219 20160301 20160312 20160323 20160403 20160414 20160517 20160528 20160608 20160630 20160711 20160722 20160813 20160904 –
468.023 510.871 438.414 340.791 267.996 257.546 375.692 261.188 171.216 21.098 182.875 91.320 552.851 401.005 71.182 88.652 206.383 68.547 102.861 85.539 204.825 173.997 –
Obs *: yyyymmdd.
Fig. 13. a) PSI deformation map, b) Standard deviation of the deformation map.
Baia-3 dike (Fig. 14b). The comparison of PS measurements to four corresponding reflecting prisms values (ST1, ST3, ST6, ST7), located on the Sela and Tulipa dikes (Fig. 14a), can be seen in more details in the plots of Fig. 15. The PS values and the standard deviation showed a higher dispersion in relation to the geodetic field data. No evidence of subsidence or uplift was detected taking into account both sources of deformation measure ments. Thus, a pattern of stability was well characterized for all cases, since all values did not extrapolate the stability limit (�13 mm). PSI results for the Germano main wall area (Fig. 16a) showed that the number of PS was lower than the SBAS measurement points, but it was still possible to provide information related to the surface stability. Fig. 16b shows a view of the Germano buttress, which was partially ~o collapse due to damage caused by mudslides reinforced after the Funda on the base of this structure. PS and the reflective prisms results are compared in Fig. 17, which mean values did not extrapolate the stability limit of the company (�13 mm). However, for the case of the prism location #24ZI400
(Fig. 17a), a subsidence trend was detected for both orbital and in situ measurements, while for the remaining cases, the values varied without a clear subsidence tendency. Statistical validation (Wilcoxon test) for the prims and PS measure ment data pointed out a similarity for all cases (Table 3). Standard de viation of PS was higher than the prims data for the Sela and Tulipa dikes, but for the Germano buttress the behavior was the opposite. 4.3. Pluviometry With an automatic meteorological station in the SAMARCO mining unit (Fig. 18a), it was possible to obtain information related to precip itation during the period of the TerraSAR coverage. The mensal accu mulated precipitation plotted in Fig. 18b shows the rain variation during the seasons, with the lowest values for the winter and the highest values occurring during summer time. The PSI and SBAS results for all Terra SAR acquisitions related to the Baia-3 dike (tailings), Auxiliar dike-2 and mining headquarter were also plotted in Fig. 18b. The result for the 9
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Fig. 14. a) Ground displacement map of Sela & Tulipa dikes; b) Ground displacement map of Baia-3 dike.
Fig. 15. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding PSI measurements: a) ST1, b) ST3, c) ST6, d) ST7. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS.
mining headquarter was indicative of an overall stability independent of the precipitation, while for the Auxiliary dike-2 and Baia-3 dike the results were more sensitive.
Germano dam showed a tendency to an overall stability, even considering that the prims data were a little noisy, and a slow subsidence was detected on the ground at the position of the prism #24ZI400, with same behavior for corresponding SBAS and PSI measurements, but within the SAMARCO stability safety limit. The sector of the Germano ~o collapse, and the buttress (Fig. 16b) was reinforced after the Funda gravel that covered the ground surface was still accommodating in this part of the structure. The auxiliary dike-2 is also a road access for heavy trucks, and its pavement is frequently under maintenance, which may explain the surface displacement as illustrated in Fig. 18b. In the mining head quarter, the measurement values provided by both A-DInSAR techniques were very close (0.7 m) and so the behavior of the measurements were very similar. Finally, based on the plot of Fig. 18b, the pluviometry
4.4. Discussion The mining activities in the Germano mining plant were totally ~o dam. Thus, a gradual set interrupted after the rupture of the Funda tlement process occurred on the waste mining material in the Germano tailings dam, particularly on the dikes, mainly inside the Baia-3 dike, which accommodates fine tailings in the settlement process (Morgen stern et al., 2016). Comparing the results of the two A-DInSAR tech niques used, the SBAS was able to provide surface measurements on the tailings storage instead of the PSI technique. 10
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Fig. 16. a) Ground displacement map of the buttress Germano main wall, b) view of the buttress sector of the Germano dam.
Fig. 17. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding PSI measurements: a) 24ZI400, b) 24ZI401, c) 24ZI419, d) 24ZI430, e) 24ZI431, f) 24ZI435. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS.
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Table 3 Statistical test. Prism Id
Distance from PS to Prism (m)
Prism Standard deviation (mm)
PSI Standard deviation (mm)
ST1 ST3 ST6 ST7 24ZI400 24ZI401 24ZI419 24ZI430 24ZI431 24ZI435
0.710 1.343 1.181 2.156 2.713 2.708 4.714 6.530 4.521 1.873
0.343 0.509 0.718 0.640 3.318 2.935 2.969 4.405 4.497 3.760
2.281 1.243 1.231 1.469 3.610 1.245 2.639 1.678 3.478 2.394
Wilcoxon test results N (cases)
T (W test)
p-value
μ1 ¼ μ2
9 9 9 9 15 5 15 15 15 12
21 11 17 18 44 5 23 58 2 35
0.178 0.173 0.515 0.594 0.363 0.500 0.036 0.910 0.001 0.754
Ok Ok Ok Ok Ok Ok Ok Ok Ok Ok
Significance level: p < 0.05.
Fig. 18. a) View of meteorological station in the site, b) Mensal accumulated precipitation during the TerraSAR-X coverage and the PSI and SBAS results.
effected the subsidence rate of the tailings storage (Baia-3 dike), with a reduction of the deformation velocity during the dry period (April to September).
sequential satellite SAR acquisitions and the ground deformation in formation is not “real time”. To move toward a “near-real time” moni toring, the use of SAR data constellation, with very short revisits time, would be a profitable alternative for monitoring purposes. A comple mentary use of space-based SAR information with field “real time” monitoring system is necessary for operation perspectives.
5. Conclusions This study demonstrated the capabilities and potential of SBAS and PSI techniques for monitoring linear and nonlinear ground deformations of the mining structures of the Germano tailings dam, with A-DInSAR results compatible with in situ geodetic measurements. The space-based SAR data were able to provide a synoptic view of the deformation affecting the area of interest, as well as its spatial-temporal evolution for alarming, planning and mining risk assessment. SBAS processing allowed the identification of a large number of widely distributed MP over a complex terrain, showing its relevancy on providing a high-resolution ground deformation measurement with millimetric accuracy, with values indicating an overall stability of the structures of this large tailing dam (main and auxiliary dikes, tailings reservoir, traffic access, etc.). With this technique it was possible to obtain measurements on the tailings of Baia-3 reservoir, task that would be very difficult to carry out using the classical methods of geodetic surveying with ground instru mentation. On the other hand, PSI technique result, which works with persistent targets, was not able to provide measurements within the tailings reservoir. Considering that in this research only ascending TerraSAR-X images were available, it would be interesting to use acquisitions based on ascending and descending SAR passes, in order to be possible to decompose the displacement in the vertical and horizontal components, instead of only one component extracted along the satellite LoS. Finally, it is important to mention that the A-DInSAR techniques depend on
Declaration of competing interest The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consul tancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Acknowledgements The authors are especially grateful to the SAMARCO’s geotechnical team for providing the geodetic in situ data and supporting the field campaign; to Visiona Tecnologia Espacial SA and AIRBUS DEFENCE AND SPACE for supplying the TSX images and the DEM, as well as MSc Alessio Cantone and BSc Marco Defilippi from SARMAP SA for the technical support during the SBAS/PSI processing. The second author would like to thank to the National Council for Scientific and Techno logical Development(CNPq) for a grant received during the investigation (Process # 304825/2014-0), that allowed the SARscape license upgrade for all the interferometric processing.
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Appendix A. Supplementary data
differential SAR interferogramas. IEEE Trans. Geosci. Remote Sens. 42 (7), 1377–1386. Milillo, P., Perissin, D., Salzer, J.T., Lundgren, P., Lacava, G., Milillo, G., Serio, C., 2016. Monitoring dam structural health from space: insights from novel InSAR techniques and multi-parametric modeling applied to the Pertusillo dam Basilicata, Italy. Int. J. Appl. Earth Obs. Geoinf. 52, 221–229. Milillo, P., Bürgmann, R., Lundgren, P., Salzer, J., Perissin, D., Fielding, E., Biondi, F., Milillo, G., 2016. Space geodetic monitoring of engineered structures: the ongoing destabilization of the Mosul dam, Iraq. Sci. Rep. 6, 37408. Morgenstern, N.R., Vick, S.G., Viotti, C.B., Watts, B.D., 2016. Fund~ ao Tailings Dam Review Panel: Report on the Immediate Causes of the Failure of the Fund~ ao Dam. Samarco S.A., Vale S.A.. BHP Brasil Ltd., p. 76. Available on. http://fundaoinvestigat ion.com/the-panel-report/. (Accessed 18 May 2018) Mura, J.C., Gama, F.F., Paradella, W.R., Negr~ ao, P., Carneiro, S., De Oliveira, C.G., Brand~ ao, W.S., 2018. Monitoring the vulnerability of the dam and dikes in Germano iron mining area after the collapse of the tailings dam of Fund~ ao (Mariana-MG, Brazil) using DInSAR techniques with TerraSAR-X data. Remote Sens. 10, 1507. Paradella, W.R., Ferretti, A., Mura, J.C., Colombo, D., Gama, F.F., Tamburini, A., Santos, R.A., Novalli, F., Galo, M., Camargo, P.O., Silva, A.Q., Silva, G.G., Silva, A., Gomes, L.L., 2015. Mapping surface deformation in open pit iron mines of Caraj� as Province (Amazon Region) using an integrated SAR analysis. Eng. Geol. 193, 61–78, 2015. Pereira, E.L., 2005. Study of the Potential of Liquecfation on an Iron Ore Tailings Dam under Static Loading. MSC. Dissertation on Geotechnical Engineering. NUGEO, Federal University of Ouro Preto, (NUGEO/UFOP), p. 80 (in Portuguese). Rosiere, C.A., Rolim, V.K., 2016. Formaç~ oes ferríferas e min�erio de alto teor associado: o min� erio de ferro no Brasil, geologia, metalog^ enese e economia. In: Recursos Minerais no Brasil, problemas e desafios. Academia Brasileira de Ci^ encias, Rio de Janeiro, pp. 2–45. Spier, C.A., Oliveira, S.M., Sial, A.N., Rios, F.J., 2007. Geochemistry and genesis of the banded iron formations of the Cau^e Formation, Quadril� atero Ferrífero, Minas Gerais, Brazil. Precambrian Res. 152 (3–4), 170–206, 2007. Tom� as, R., Cano, M., García-Barba, J., Vicente, F., Herrera, G., Lopez-Sanchez, J.M., Mallorquí, J.J., 2013. Monitoring an earthfill dam using differential SAR interferometry: La Pedrera dam, Alicante, Spain. Eng. Geol. 157, 21–32, 2013. Usai, S., 2002. A least-squares approach for long-term monitoring of deformations with differential SAR interferometry, 2002. In: Proc. IGARSS, vol. 2, pp. 1247–1250. Toronto, ON, Canada, June. Wang, T., Perissin, D., Rocca, F., Liao, M.S., 2011. Three Gorges Dam stability monitoring with time-series InSAR image analysis. Sci. China Earth Sci. 54 (5), 720–732. Werner, C., Wegmuller, U., Strozzi, T., Wiesmann, A., 2003. Interferometric point target analysis for deformation mapping. In: Proc. IGARSS 2003, Toulouse (France), vol. 7, pp. 4362–4364. Zhao, S., Fan, S., Chen, J., 2019. Quantitative assessment of the concrete gravity dam damage under earthquake excitation using electro-mechanical impedance measurements. Eng. Struct. 191, 162–178. Zhou, W., Li, S., Zou, Z., Chang, X., 2016. Remote sensing of deformation of a high concrete-faced Rockfill dam using InSAR: a study of the Shuibuya dam, China. Remote. Sens. 8, 255.
Supplementary data to this article can be found online at https://doi. org/10.1016/j.rsase.2019.100267. References Agurto-Detzel, H., Bianchi, M., Assumpç~ ao, M., Schimmel, M., Collaço, B., Ciardelli, C., Barbosa, J.R., Calhau, J., 2016. The tailings dam failure of 5 November 2015 in SE Brazil and its preceding seismic sequence. Geophys. Res. Lett. 8. https://doi.org/ 10.1002/2016GL069257. AGU Publications. Berardino, P., Fornaro, G., Lanari, R., Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sens. 40 (11), 2375–2383. November. Carmo, F.F., Kamino, L.H.H., Tobias Jr., R., Campos, I.C., Silvino, G., Castro, K.J.S.X., Mauro, M.L., Rodrigues, N.U.A., Miranda, P.S., Pinto, C.E.F., 2017. Fund~ ao tailings dam failures: the environment tragedy of the largest technological disaster of Brazilian mining in global context. Perspect. Ecol. Conserv. 15, 145–151. Castilho, B.M., 2017. Thesis for the Master Degree in Geotechnical Engineering. An� alise dos gatilhos de liquefaç~ ao din^ amica e modelagem num� erica da barragem do Germano, vol. 97. Federal University of Ouro Preto. Crosetto, M., Monserrat, O., Cuevas, M., Crippa, B., 2011. spaceborne differential SAR interferometry: data analysis tools for deformation measurement. Remote Sens. 3, 305–318. Di Martire, D., Iglesias, R., Monells, D., Centolanza, G., Sica, S., Ramondini, M., Pagano, L., Mallorqui, J.J., Calcaterra, D., 2014. Comparison between differential SAR interferometry and ground measurements data in the displacement monitoring of the earth-dam of Conza della Campania (Italy). Remote Sens. Environ. 148, 58–69. Dorr II, J.V.N., 1969. Physiographic, Stratigraphic and Structural Development of the Quadril� atero Ferrífero. Professional Paper. Geological Survey, Washington, United States. n.641-A. Emadali, L., Motagh, M., Haghighi, M.H., 2017. Characterizing post-construction settlement of the Masjed-Soleyman embankment dam, Southwest Iran, using TerraSAR-X SpotLight radar imagery. Eng. Struct. 143, 261–273. Ferretti, A., Prati, C., Rocca, F., 2001. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 39 (1), 8–20. Gama, F.F., Cantone, A., Santos, A.R., Pasquali, P., Paradella, W.R., Mura, J.C., Silva, G. G., 2017. Monitoring subsidence of open pit iron mines at Caraj� as Province based on SBAS interferometric technique using TerraSAR-X data. Remote Sens. Appl. Soc. Environ. 8, 199–211. Hopper, A., Bekaert, D., Spaans, K., Arikan, M., 2012. Recent advances in SAR Interferometry time series analysis for measuring crustal deformation. Tectonophysics 514–517. IBAMA, 2015. Laudo T� ecnico Preliminar -Impactos ambientais decorrentes do desastre envolvendo o rompimento da barragem de Fund~ ao, em Mariana, Minas Gerais, Novembro de 2015 available on. http://www.ibama.gov.br/phocadownload/barra gemdefundao/laudos/laudo_tecnico_preliminar_Ibama.pdf. (Accessed 18 May 2018). Lanari, R., Mora, O., Manunta, M., Mallorquí, J.J., Berardino, P., Sansosti, E., 2004. A small-baseline approach for investigating deformations on full-resolution
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Advanced DINSAR analysis on dam stability monitoring: A case study in the Germano mining complex (Mariana, Brazil) with SBAS and PSI techniques Fabio F. Gama a, *, Waldir R. Paradella a, Jos�e C. Mura a, Cleber G. de Oliveira b a b
National Institute for Space Research-INPE, Av. dos Astronautas 1758, S~ ao Jos�e dos Campos, SP, CEP 12227-010, Brazil VISIONA Tecnologia Espacial, Estrada Dr. Altino Bondensan 500, S~ ao Jos�e dos Campos, SP, CEP 12247-016, Brazil
A R T I C L E I N F O
A B S T R A C T
Keywords: Tailings Dam SBAS PSI TerraSAR
This work presents an investigation of detection and monitoring of surface motions using Advanced Differential SAR Interferometry (A-DInSAR) with SBAS (Small Baseline Subset) and PSI (Persistent Scatterer Interferometry) techniques in the Germano iron mining complex region (Mariana, Brazil), after the collapse of the Fund~ ao tailing dam occurred on November 5th, 2015. The research was carried out aiming to provide useful information about ground movement monitoring, planning and risk assessment in the area after the tragic event. The analysis was performed using 46 TerraSAR-X images and a Digital Elevation Model (DEM) obtained by Pleiades data. ADInSAR processing provided the removal of the atmospheric phase artifacts, baseline related errors, DEM height error estimation and finally the ground displacement determination. The results pointed out that with both techniques it was possible to assess the stability conditions of the Germano mining assets, particularly the main structures of the Germano dam and the detection of small settlements in the mining tailings storage, pointing out that these may be an important components for risk management of open pit mining operations, including tailings dams.
1. Introduction Open pit iron mining generally occupies extensive areas including portions of land adjacent to the mine pit, with presence of pipelines, industrial plant, waste piles as well as tailing dams. Instabilities of these mining structures are normally expected due to the intense rock mass movement, steep slope of mine benches, blasting practices and rainfall. For this kind of activity, tailings dams have to be built to store mining waste material from the beneficiation of iron ore. In the afternoon of ~o November 5th, 2015, an enormous tailings dam called Funda collapsed, which contained around 55 million cubic meters of tailing ~o dam was part of the Germano iron mining Complex, materials. Funda �tero and it was located in a region called Iron Ore Quadrangle (Quadrila Ferrífero – QF), municipality of Mariana (Minas Gerais State). As a consequence, 32.6 million of cubic meters of mining waste material spilled from the dam and reached the Bento Rodrigues village, 2.5 km far ~o, which was completely flooded and destroyed by mud from Funda slides. Other villages and rural districts in the Gualaxo River valley, like Paracatu de Baixo and Gesteira, were also affected by these mud waves. This tragic event has assumed enormous proportions, polluting more than 650 km of watercourses from the Doce River basin up to the
Atlantic Ocean (Espírito Santo State), and involving the Brazilian Atlantic Forest (one of the world’s biodiversity hot spots), estuarine, coastal and marine environments (Morgenstern et al., 2016; Carmo et al., 2017). It was considered the largest social-environmental disaster in the Brazilian history and one the world’s largest disaster involving tailings dams (IBAMA, 2015). With the techniques of Advanced Differential Interferometric SAR (A-DInSAR) based on satellite systems, a powerful tool for monitoring ground deformation in different applications (Lanari et al., 2004; Gama et al., 2017) is becoming more relevant due to several reasons: they allow geologists/mining engineers to detect and monitor surface dis placements over large areas with a synoptic view, with reliable mea surements of surface motions with high accuracy (mm scale); the information can be extracted over a dense grid of points, and without the need for conventional ground instrumentation commonly used or field campaigns (Zhao et al., 2019). Basically, two kinds of A-DInSAR Time-Series approaches have been applied to obtain ground deforma tion information, the Small Baseline Subset (SBAS) and Persistent Scatterer Interferometry (PSI). The PSI technique is based on a stack of master referenced differ ential interferograms, with the identification of pixels, which scattering
* Corresponding author. National Institute for Space Research-INPE, Av. dos Astronautas, 1758, S~ ao Jos�e dos Campos, SP, 12227, Brazil. E-mail address: [email protected] (F.F. Gama). https://doi.org/10.1016/j.rsase.2019.100267 Received 6 June 2019; Received in revised form 11 September 2019; Accepted 21 September 2019 Available online 4 October 2019 2352-9385/© 2019 The Authors. Published by Elsevier B.V. This is an open (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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tailings dams are built progressively “upstream” of the starter dam by incorporating tailings materials into the dam for support through the controlled deposition. While using tailings materials to build the dam reduce construction costs, upstream tailings dams are more susceptible to cracking, liquefaction and erosion, and may be less stable than other dam design in the occurrence of seismic events, because tailings mate rials may liquefy and lose their strength. The Germano tailings dam was constructed with a planned capacity around 116 million cubic meters, and up to 1995, this dam only received sandy waste. Since 1977 slime materials were also deposited, mainly in the central, north and north west sectors of the reservoir. The newest tailings dam in the Germano ~o, which was installed in 2008 using the same plant unit was Funda upstream type, followed by a water dam called Santar�em. The Germano tailings dam has several supporting structural dikes named as Sela, Selinha, Auxiliary dike1, Auxiliary dike 2, Tulipa and Baia-3. ~o rupture, it was noted that the After the tragic event of the Funda waste tailings released by the failure flowed over the Santar�em dam causing partial damage of its main body by eroding its structure. In addition, since the reservoir of the Fund~ ao passed by a rapid downgrade, damages were also detected on the Germano dam, particularly on the Sela, Tulipa and Selinha dikes, which are the supporting structures on the side walls of the remaining dam. An important aspect to monitor the Germano dam was due to the bigger volume of this mega structure, with ~o dam. A new collapse more than twice in volume of the collapsed Funda in the Germano dam could worsen the already critical situation in terms of lives and environment. According to the report on “Immediate causes of the failure of the ~o dam” (Morgenstern et al., 2016), the collapse was due to Funda liquefaction of the sandy tailings on the base of the dam, resulting in loss of mechanical resistance. This liquefaction process was caused by a chain of events and conditions related to the dam construction and op erations, such as (1) damage to the original starter dike that resulted in increased saturation, (2) deposition of slime in areas where this was not previously intended, (3) structural problems with a concrete conduit that caused the dam to be raised over the slime, and (4) a series of three small seismic shocks (Agurto-Detzel et al., 2016), that occurred 90 min before the rupture, and although the movements were quite small, they likely must have accelerated the failure process that was already well advanced. Fig. 1 shows the Pleiades image of the study area after the ~o with the highlighted Germano structures, which collapse of Funda were monitored by A-DInSAR. According to Pereira (2005), the iron ore tailings products from the study area present a mineralogical composition of Fe and SiO2 and subordinately, AL2O3. In addition, they are classified as sand and clay, with variation in size from fine to granular. Due to these heterogeneous nature and particle sizes, the tailings deposit exhibits a large variation of permeability along the vertical and horizontal direction, with several layers related to deposition events from distinct sources along the time. Up to 1995, the Germano dam only received sandy waste, but since 1977, slime material was also deposited. In essence, after the initial phase of material deposition, the weight of successive layers normally provokes reduction in terms of the reservoir volume. This reduction is expressed by settlements, which need to be moni tored within safety margin defined by geotechnical limits. It is also important to consider that two additional mechanisms also contributed to deformations within the tailings deposit: the break of mineral grains and the relative movements between them. Finally, iron ore tailings materials present low cohesion and are partly under saturated condi tions, due to the waste material transport/deposition using water and the rainfall, which contribute to settlements. Under very extreme con ditions, a liquefaction phenomenon can occur. A detailed study was carried out in the Germano tailings dam based on granulometry, void ratio, pore water pressure, loading-cycles, Scanning Electron Micro scopy (SEM) tests and numerical modelling, with the purpose of char acterizing the tailings material (Castilho, 2017) aiming at predicting liquefaction events. The results demonstrated a great heterogeneity of
properties do not vary along time (point-wise reflectors), denominated Persistent Scatterers or PS (Ferretti et al., 2001; Werner et al., 2003; Crosetto et al., 2011; Hopper et al., 2012; Paradella et al., 2015; Wang et al., 2011; Tomas et al., 2013; Di Martire et al., 2014; Milillo et al., 2016a, 2016b). With this approach, it is possible to get a temporal analysis of the interferometric phase of individual point targets as well as to extract accurate linear information related to surface target dis placements. This technique was used for concrete and earth-rock-filled dams. On the other hand, the SBAS technique consists of the combination of images in order to limit spatial decorrelation effects and the history of the displacement evolution is derived of the different interferograms connected in time. Surface displacement is related to distributed scat terers with homogeneous characteristics, such as low-vegetated surface, manmade structures, debris or desert areas, which exceed a certain coherence threshold. SBAS technique has been successfully used to detect non-linear ground deformation of volcanoes (Berardino et al., 2002; Usai., 2002; Lanari et al., 2004), mining benches (Gama et al., 2017; Mura et al., 2018), and some studies were also carried out to monitor concrete dams of hydric reservoirs (Zhou et al., 2016; Emadali et al., 2017) which were able to detect the structural movements and the water level, as well as settlement of these structures. This study presents an evaluation of the SBAS and PSI techniques to monitor ground motions on the remaining Germano tailings dam, after ~o dam. Particularly, The Germano dam differs from the collapse of Funda water dams in that it was built using sterile mining material rather than concrete or earth-rock-filled, being more susceptible to erosion and liquefaction processes. To this end, it was used a stack with 46 TerraSAR-X StripMap images, acquired under interferometric configu ration (sequential acquisition with same viewing geometry), which covered the period from December 2015 to April of 2016. 2. Study area The Germano Iron mining Complex is part of the Alegria Mining Complex, located in the easternmost portion of the Quadril� atero Ferrí ~o Francisco Craton. The Alegria fero (QF), southern border of the Sa Complex is related to the Alegria Sinclinal, a geological structure formed by Archean rocks of the Rio das Velhas Supergroup and Paleoproterozoic rocks of the Minas Supergroup (Dorr, 1969). The Cau^ e Formation of the Minas Supergroup hosts banded iron formations, locally known as ita birites, deposited in shallow marine passive margin settings. Itabiritos are characterized by chemical sediments and metassediments interlay ered with chert and/or quartz and iron oxides. Two major compositional types of itabirite, dolomitic and quartz itabirites, are found in the northwestern part of QF. The former consists of alternating dolomite-rich and hematite-rich bands, whereas the latter is formed with alternating quartz-rich and hematite-rich bands. Accessory min erals are chlorite, sericite, and apatite in both types (Spier et al., 2007). In addition, other ferruginous materials such as cangas, breccias, compact hematite, and non-ferruginous rocks such as quartzites, phyl lites, dolomitic phyllites, schists and metabasites are also mapped in the area. Massive and soft iron ores were formed due to supergene process acting during the Neogene over products associated to hydrothermal enrichments (Rosiere and Rolim, 2016). This mining complex is an important iron producer in the Brazil, ~o S.A., with open which is explored by the Brazilian SAMARCO Mineraça pit mines, waste piles, industrial plant, pipelines, tailings and water dams. The Germano plant had an installed production in 2017 of iron pellets of 30.5 million of metric tons and iron waste of 20.7 million of ~o dam in 2015, metric tons (Castilho, 2017). Up to the rupture of Funda the Germano Complex encompassed two mega tailings dams and a reservoir to store water for industrial reuse. The first tailings dam was Germano, which was installed in 1976 using the upstream method of construction with the implantation of a 70 m high start dike. Upstream 2
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Remote Sensing Applications: Society and Environment 16 (2019) 100267
Fig. 1. Germano mining complex– Mariana/MG.
the iron waste products, which makes liquefaction susceptibility anal ysis very complex. The accelerations from potential liquefaction triggers were measured by using an accelerometer, and the response of the whole structure to these requests was analyzed by using other auxiliary in struments such as ground-based radars and piezometers. Just after the ~o rupture, three IDS (Ingegneria Dei Sistemi) ground-based SAR Funda were installed and monitored the main dike and the Sela and Sela/Tu lipa dikes, from the period of November 2015 up to March 2016. No signal of ground displacements was detected. The results also demon strated that the accelerations monitored during the study period were insufficient to cause deformations in the dam, even though there were slight elevations in pore water pressure in some situations. The nu merical analysis showed that the horizontal deformation on a section on the centre of the reservoir was small and around 10 cm.
In order to validate the A-DInSAR results with in situ geodetic survey data, field monitoring measurements at discrete points were carried out by the geotechnical team of SAMARCO Company, providing surface motions information related to the Germano dam and its structures. These measurements were acquired by a robotic station Leica TM50 (0.6 mm þ 1.0 ppm of nominal accuracy). Taking into account that the prisms-station distances were around 800 m and the angular errors of the prisms and station were 2.5" (2.5 seconds), the nominal geodetic measurements error was �8 mm. Since, in theory, the zero reading can present the same nominal error, the geotechnical team of the SAMARCO Company established the limit of two times the nominal error (accu mulated error totalizing 16 mm) as a safety limit for detection of movements. Fig. 2 shows a prism installed on the top of a dike and the concrete base that was used to set the total station. The acceptable limit of nominal error (�16 mm) was projected to the satellite geometry (35.2 degrees of incidence angle), that corresponded to the interval of �13 mm. Measurements point (MP) values within this interval were considered as random errors, and values outside this in terval, probably represented real ground deformation. Prism measure ments were projected along the satellite line of sight (LoS), by multiplying the values by the cosine of the satellite incidence angle of each prism position used, in order to be compared with A-DInSAR measurement values. It is important to mention that the selected prisms that were used for validation were monitored with different periods when compared to the TerraSAR acquisitions. Fig. 3 plot illustrates the time relationship for both set of measurements. In addition to the SAMARCO’s geodetic field survey, INPE’s team also carried out a field campaign during the investigation in order to verify the presence of ground displacements, such as the presence of fractures and cracks on berms and benches along the main wall of the dam, on the supporting auxiliary dikes, and on traffic routes around and within the reservoir. The workflow in Fig. 4 shows the methodology used in this study for the SBAS and PSI techniques. Wilcoxon test was used to verify if A-DInSAR measurement values would be a similar displacement inference to the geotechnical mea surements. Wilcoxon is a statistical nonparametric test that compares two paired groups and calculates the differences between each set of pairs. This procedure verifies if two data populations have the same median value and, therefore, can be considered statistically similar. If
3. Methodology The A-DINSAR analysis was carried out using the SBAS and PSI techniques available in the SARSCAPE software version 5.4.1. A total of 46 TerraSAR-X images was used to continuous monitor the ground displacements of the Germano dam for a period of 17 months (December 2015–April 2016). The images were acquired in StripMap mode (TSXSM), on ascending orbits (looking azimuth ~80 degrees), revisiting time of 11 days, and incidence angle range of 33.9–36.5 degrees (near and far range). During the SBAS processing, a constraint was used on spatial and temporal baselines, which allowed generating a set of interferograms to perform the analysis. These interferograms were spatially filtered using multi-look processing and unwrapped using a Minimum Cost Flow (MCF) algorithm. Visual inspection was used to choose the best unwrapped interferograms. Due to the complex variation of topography in the study area (sup porting dikes with vertical walls and flat surface of the tailings storage), a high-resolution digital elevation model (DEM) was used to remove the topographic phase component for the SBAS analysis and to improve the geolocation of the deformation map. DEM was provided by the company VISIONA Space Technology with 1 m of spatial resolution, based on triplet of stereoscopic images with spatial resolution of 0.5 m, collected by the satellite Pleiades 1A on June 19th, 2016. 3
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Fig. 2. a) Prism installed; b) concrete base for Leica total station.
this is the case, then the measurement strategy based on the A-DInSAR may be presumed to be representative of the surface displacement expressed by prism values. 4. Results and discussion 4.1. SBAS processing Based on the stack of 46 co-registered SLC images, the interfero metric pairs were generated in relation to a reference image (11/07/ 2016), which perpendicular baseline was the lowest and this acquisition was nearly at the center of the time series, as illustrated in Fig. 5a. The interferograms were obtained with normal baseline restriction up to 15% of the critical baseline (6074.20 m) and temporal baseline of 45 days, totalizing 156 interferograms (Fig. 5b). Before the phase unwrapping process, they were spatially filtered by a complex multilook operation, with 4 looks in range and azimuth, and a coherence threshold of 0.40 was used. The tests indicated that the value 4 looks was sufficient to meet the Sbas processing, allowing to distinguish the main targets of interest. A spatial filter, corresponding 1200 m was used to remove the atmospheric phase artifacts. Distributed Ground Control Points (GCPs), in areas considered as stable, were selected assuming a zero-deformation reference for the refinement/re-flattening of the interferometric phase to perform the second inversion step. Atmospheric phase filtering and the estimate of the displacement time series were based on the same set of GCPs. The displacement rate in the area can be expressed through the
Fig. 3. Plot of acquisition of TerraSAR images and geotechnical surveying with prisms. Red line means the date of dam collapse. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4. Methodological workflow. 4
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Fig. 5. a) Acquisition time versus normal baseline. Yellow dot means reference image (11/07/2016), b) Interferometric pairs selected (solid lines). Yellow dot means reference image. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
velocity deformation map in LoS (Fig. 6a) computed with millimeter precision. Positive values correspond to surface motion toward the sat ellite (blue colors), negative values correspond to ground motion away from the satellite (red colors). The SBAS velocity deformation map (Fig. 6a) showed that great part of the study area was stable during the time span of the TSX-1 acquisi tions, particularly with a stability behavior for Sela, Tulipa and Selinha Dikes (green-yellowish regions). A noticeable subsidence was detected over the tailings inside of the Baia-3 Dike area, as well as along the access road on top of Auxiliary Dike 2 (yellow-reddish regions). The Standard Deviation of the velocity deformation map (Fig. 6b) showed an overall error between 0 to 3 mm/year for the SBAS mea surements (yellow-greenish regions), with the lowest values observed on top of the Sela, Tulipa and Selinha dikes, and on the mining headquarter (greenish regions). Some regions within the Baia-3 reservoir are related to very low values of Standard Deviation, and in some cases near zero. The site locations for the prisms (ST1, ST3, ST6, ST7), which were installed on the top of the Sela and Tulipa dikes, can be seen on the SBAS ground displacement map shown in Fig. 7a. The field picture of these supporting dikes is shown in Fig. 7b. The comparison of the in situ geodetic survey values (prisms ST1, ST3, ST6, ST7) to the SBAS corresponding MP can be seen in the plots of Fig. 8. It can be noticed a pattern of stability for all cases, with no
tendency for subsidence or uplift. The SBAS results showed a greater dispersion of measurements in relation to the total/station prism values, with a greater standard deviation too (Table 1). An important issue to consider is the error limit of �13 mm due to the total station accuracy, pointing out that all values do not extrapolate this stability acceptance limit. It was also detected subsidence related to settlements inside the reservoir of the Baia-3 dike, with displacement rate up to 150 mm/ year (Fig. 9a), and for the top of the Baia-3 dike with displacement rate up to 70 mm/year, respectively. In addition, in the Auxiliary Dike 2, which has an access road on its top, the accumulated displacement reached 86.81 mm, which corresponded to a deformation rate of 60.09 mm/year. Fig. 9b shows a view of Baia-3 reservoir during the field campaign. The Germano tailings dam is the biggest reservoir of the SAMARCO’s plant unit and the buttress sector holds an iron ore conveyor belt. This buttress sector was monitored by the geotechnical team of the company using 6 prisms (24ZI400, 24ZI401, 24ZI419, 24ZI430, 24ZI431, 24ZI435), that were installed on the higher, medium and lower part of the area. The location of each prism plotted over the SBAS deformation map can be seen in Fig. 10a, and a field picture of the iron ore conveyor belt on the buttress sector is shown in Fig. 10b. The behavior of the values of total station/reflective prisms and SBAS
Fig. 6. a) SBAS velocity deformation map, b) Standard deviation of the velocity deformation map. 5
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Fig. 7. a) Ground displacement map and the prims positions; b) Field view of the Sela and Tulipa Dikes.
Fig. 8. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding SBAS measurements, as well as the standard deviation observed: a) ST1, b) ST3, c) ST6 and d) ST7. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS. Table 1 Statistic test. Prism Id
Distance from PS to Prism (m)
Prisms Standard deviation (mm)
SBAS Standard deviation (mm)
ST1 ST3 ST6 ST7 24ZI400 24ZI401 24ZI419 24ZI430 24ZI431 24ZI435
2.97 0.59 2.95 2.57 0.76 1.88 4.97 3.43 3.13 4.87
0.343 0.509 0.718 0.640 3.318 2.935 2.969 4.405 4.497 3.760
1.169 1.599 1.344 1.376 3.284 3.103 3.445 1.964 2.383 2.732
Significance level: p < 0.05. 6
Wilcoxon test results N (cases)
T (W test)
p-value
μ1 ¼ μ2
9 9 9 9 15 5 15 15 15 12
10 21 3 15 43 7 48 28 41 31
0.139 0.859 0.021 0.374 0.334 0.893 0.495 0.069 0.281 0.530
Ok Ok Ok Ok Ok Ok Ok Ok Ok Ok
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Fig. 9. a) Ground displacement map of Baia-3 dike; b) View of Baia-3 dike during the field work.
measurements for the Germano buttress sector is illustrated in the plots of Fig. 11. The in situ geodetic survey and SBAS measurements values did not extrapolate the stability limit of the company (�13 mm), but a higher variance for the prism results was noticed, oscillating around zero value of deformation. For the reference location 24ZI400, a subsidence trend was characterized by both systems of deformation measurements (Fig. 11a). Wilcoxon test was used for the statistical validation, in order to verify if the SBAS measurements would provide a similar displacement infer ence to the in situ geodetic measurements. Table 1 shows the Wilcoxon results for the SBAS and prisms data, which pointed out similarity of the data for all cases by this test criterion.
acquisition. Table 2 shows the 46 interferometric pairs and the perpendicular baseline size (Bperp). The lowest observed value was 41.649 m and the highest value was 601.216 m. All interferograms were processed with 2 looks in azimuth and 1 look in range (in order to preserve the details of the small structures), coherence threshold of 0.58, and 1200 m of the atmospheric filter. PSI technique also requests one reference point or GCP (Ground Control Point), which was selected on a stable area, using the interferometric phase image of the reference image (master). To perform the atmospheric filtering and estimate the displacement time series, the same GCP that was used during the SBAS processing was chosen. PSI deformation map in LoS is shown in Fig. 13a. Regions that move away from the satellite are represented by reddish color (negative values), the regions with motion toward the satellite are in blueish colors (positive values), while greenish color represents stable regions. Standard deviation map of the average displacement rates shows errors between 0 to 2 mm/year for PS, with very low values for the top of the dikes and the mining headquarter (Fig. 13b). The map of deforma tion (Fig. 14a) showed that for the top of the Sela/Tulipa, Selinha dikes, and the mining headquarter, the average LoS velocity is very close to zero. In addition, few PS were detected within the tailings storage in the
4.2. PSI processing PSI processing was carried out based on 46 TerraSAR-X images and DEM obtained from the satellite Pleiades data. PSI technique also re quests an image to be used as reference, and in order to compare both ADInSAR results, it was chosen the same master image used during the SBAS processing (11/07/2016). Fig. 12a shows the relative position of the satellite during the acquisitions and corresponding dates. Fig. 12b shows the 46 possible pairs from the reference image by the dates of
Fig. 10. a) Prims location over the SBAS displacement map of Germano dam; b) View of the iron ore conveyor belt on the Germano dam buttress sector. 7
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Fig. 11. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding SBAS measurements: a) 24ZI400, b) 24ZI401, c) 24ZI419, d) 24ZI430, e) 24ZI431, f) 24ZI435. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS.
Fig. 12. a) Acquisition time versus relative position, yellow dot means reference image (11/07/2016); b) Interferometric pairs selected (solid lines). Yellow dot means reference image. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 8
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Table 2 The acquisitions dates of the master and slave TerraSAR-X images, perpendicular baseline (Bperp) and time interval relative to the master image. Pair
Master*
Slave*
Bperp (m)
Pair
Master*
Slave*
Bperp (m)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619
20151112 20151123 20151204 20151215 20151226 20160106 20160117 20160128 20160915 20160926 20161007 20161018 20161029 20161109 20161120 20161201 20161212 20161223 20170103 20170114 20170125 20170216 20170227
212.725 258.581 313.201 366.006 156.075 126.177 290.914 208.349 227.984 216.419 180.147 266.808 129.147 178.348 240.259 205.336 294.991 492.267 156.442 285.458 41.649 601.216 395.017
24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 -
20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 20160619 –
20170310 20170321 20170401 20170412 20170423 20170504 20170515 20160208 20160219 20160301 20160312 20160323 20160403 20160414 20160517 20160528 20160608 20160630 20160711 20160722 20160813 20160904 –
468.023 510.871 438.414 340.791 267.996 257.546 375.692 261.188 171.216 21.098 182.875 91.320 552.851 401.005 71.182 88.652 206.383 68.547 102.861 85.539 204.825 173.997 –
Obs *: yyyymmdd.
Fig. 13. a) PSI deformation map, b) Standard deviation of the deformation map.
Baia-3 dike (Fig. 14b). The comparison of PS measurements to four corresponding reflecting prisms values (ST1, ST3, ST6, ST7), located on the Sela and Tulipa dikes (Fig. 14a), can be seen in more details in the plots of Fig. 15. The PS values and the standard deviation showed a higher dispersion in relation to the geodetic field data. No evidence of subsidence or uplift was detected taking into account both sources of deformation measure ments. Thus, a pattern of stability was well characterized for all cases, since all values did not extrapolate the stability limit (�13 mm). PSI results for the Germano main wall area (Fig. 16a) showed that the number of PS was lower than the SBAS measurement points, but it was still possible to provide information related to the surface stability. Fig. 16b shows a view of the Germano buttress, which was partially ~o collapse due to damage caused by mudslides reinforced after the Funda on the base of this structure. PS and the reflective prisms results are compared in Fig. 17, which mean values did not extrapolate the stability limit of the company (�13 mm). However, for the case of the prism location #24ZI400
(Fig. 17a), a subsidence trend was detected for both orbital and in situ measurements, while for the remaining cases, the values varied without a clear subsidence tendency. Statistical validation (Wilcoxon test) for the prims and PS measure ment data pointed out a similarity for all cases (Table 3). Standard de viation of PS was higher than the prims data for the Sela and Tulipa dikes, but for the Germano buttress the behavior was the opposite. 4.3. Pluviometry With an automatic meteorological station in the SAMARCO mining unit (Fig. 18a), it was possible to obtain information related to precip itation during the period of the TerraSAR coverage. The mensal accu mulated precipitation plotted in Fig. 18b shows the rain variation during the seasons, with the lowest values for the winter and the highest values occurring during summer time. The PSI and SBAS results for all Terra SAR acquisitions related to the Baia-3 dike (tailings), Auxiliar dike-2 and mining headquarter were also plotted in Fig. 18b. The result for the 9
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Fig. 14. a) Ground displacement map of Sela & Tulipa dikes; b) Ground displacement map of Baia-3 dike.
Fig. 15. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding PSI measurements: a) ST1, b) ST3, c) ST6, d) ST7. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS.
mining headquarter was indicative of an overall stability independent of the precipitation, while for the Auxiliary dike-2 and Baia-3 dike the results were more sensitive.
Germano dam showed a tendency to an overall stability, even considering that the prims data were a little noisy, and a slow subsidence was detected on the ground at the position of the prism #24ZI400, with same behavior for corresponding SBAS and PSI measurements, but within the SAMARCO stability safety limit. The sector of the Germano ~o collapse, and the buttress (Fig. 16b) was reinforced after the Funda gravel that covered the ground surface was still accommodating in this part of the structure. The auxiliary dike-2 is also a road access for heavy trucks, and its pavement is frequently under maintenance, which may explain the surface displacement as illustrated in Fig. 18b. In the mining head quarter, the measurement values provided by both A-DInSAR techniques were very close (0.7 m) and so the behavior of the measurements were very similar. Finally, based on the plot of Fig. 18b, the pluviometry
4.4. Discussion The mining activities in the Germano mining plant were totally ~o dam. Thus, a gradual set interrupted after the rupture of the Funda tlement process occurred on the waste mining material in the Germano tailings dam, particularly on the dikes, mainly inside the Baia-3 dike, which accommodates fine tailings in the settlement process (Morgen stern et al., 2016). Comparing the results of the two A-DInSAR tech niques used, the SBAS was able to provide surface measurements on the tailings storage instead of the PSI technique. 10
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Fig. 16. a) Ground displacement map of the buttress Germano main wall, b) view of the buttress sector of the Germano dam.
Fig. 17. Temporal evolution of the LoS-projected deformation for four reflecting prisms and corresponding PSI measurements: a) 24ZI400, b) 24ZI401, c) 24ZI419, d) 24ZI430, e) 24ZI431, f) 24ZI435. All plots show the SAMARCO’s alert limit for deformation projected along the satellite LoS.
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Table 3 Statistical test. Prism Id
Distance from PS to Prism (m)
Prism Standard deviation (mm)
PSI Standard deviation (mm)
ST1 ST3 ST6 ST7 24ZI400 24ZI401 24ZI419 24ZI430 24ZI431 24ZI435
0.710 1.343 1.181 2.156 2.713 2.708 4.714 6.530 4.521 1.873
0.343 0.509 0.718 0.640 3.318 2.935 2.969 4.405 4.497 3.760
2.281 1.243 1.231 1.469 3.610 1.245 2.639 1.678 3.478 2.394
Wilcoxon test results N (cases)
T (W test)
p-value
μ1 ¼ μ2
9 9 9 9 15 5 15 15 15 12
21 11 17 18 44 5 23 58 2 35
0.178 0.173 0.515 0.594 0.363 0.500 0.036 0.910 0.001 0.754
Ok Ok Ok Ok Ok Ok Ok Ok Ok Ok
Significance level: p < 0.05.
Fig. 18. a) View of meteorological station in the site, b) Mensal accumulated precipitation during the TerraSAR-X coverage and the PSI and SBAS results.
effected the subsidence rate of the tailings storage (Baia-3 dike), with a reduction of the deformation velocity during the dry period (April to September).
sequential satellite SAR acquisitions and the ground deformation in formation is not “real time”. To move toward a “near-real time” moni toring, the use of SAR data constellation, with very short revisits time, would be a profitable alternative for monitoring purposes. A comple mentary use of space-based SAR information with field “real time” monitoring system is necessary for operation perspectives.
5. Conclusions This study demonstrated the capabilities and potential of SBAS and PSI techniques for monitoring linear and nonlinear ground deformations of the mining structures of the Germano tailings dam, with A-DInSAR results compatible with in situ geodetic measurements. The space-based SAR data were able to provide a synoptic view of the deformation affecting the area of interest, as well as its spatial-temporal evolution for alarming, planning and mining risk assessment. SBAS processing allowed the identification of a large number of widely distributed MP over a complex terrain, showing its relevancy on providing a high-resolution ground deformation measurement with millimetric accuracy, with values indicating an overall stability of the structures of this large tailing dam (main and auxiliary dikes, tailings reservoir, traffic access, etc.). With this technique it was possible to obtain measurements on the tailings of Baia-3 reservoir, task that would be very difficult to carry out using the classical methods of geodetic surveying with ground instru mentation. On the other hand, PSI technique result, which works with persistent targets, was not able to provide measurements within the tailings reservoir. Considering that in this research only ascending TerraSAR-X images were available, it would be interesting to use acquisitions based on ascending and descending SAR passes, in order to be possible to decompose the displacement in the vertical and horizontal components, instead of only one component extracted along the satellite LoS. Finally, it is important to mention that the A-DInSAR techniques depend on
Declaration of competing interest The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consul tancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Acknowledgements The authors are especially grateful to the SAMARCO’s geotechnical team for providing the geodetic in situ data and supporting the field campaign; to Visiona Tecnologia Espacial SA and AIRBUS DEFENCE AND SPACE for supplying the TSX images and the DEM, as well as MSc Alessio Cantone and BSc Marco Defilippi from SARMAP SA for the technical support during the SBAS/PSI processing. The second author would like to thank to the National Council for Scientific and Techno logical Development(CNPq) for a grant received during the investigation (Process # 304825/2014-0), that allowed the SARscape license upgrade for all the interferometric processing.
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Appendix A. Supplementary data
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