Deformations Prior to the Brumadinho Dam Collapse Revealed by Sentinel-1 Insar Data Using SBAS and PSI Techniques

remote sensing

Article Deformations Prior to the Brumadinho Dam Collapse Revealed by Sentinel-1 InSAR Data Using SBAS and PSI Techniques

Fábio F. Gama 1,* , José C. Mura 1, Waldir R. Paradella 1 and Cleber G. de Oliveira 2

1 National Institute for Space Research (INPE), São José dos Campos, 12227-010 São Paulo, Brazil; [email protected] (J.C.M.); [email protected] (W.R.P.) 2 VISIONA Tecnologia Espacial, São José dos Campos, 12247-016 São Paulo, Brazil; [email protected] * Correspondence: [email protected]; Tel.: +55-12-32086517

Received: 17 August 2020; Accepted: 18 September 2020; Published: 9 November 2020

Abstract: Differential Interferometric SAR (DInSAR) has been used to monitor surface deformations in open pit mines and tailings dams. In this paper, ground deformations have been detected on the area of tailings Dam-I at the Córrego do Feijão Mine (Brumadinho, Brazil) before its catastrophic failure occurred on 25 January 2019. Two techniques optimized for different scattering models, SBAS (Small BAseline Subset) and PSI (Persistent Scatterer Interferometry), were used to perform the analysis based on 26 Sentinel-1B images in Interferometric Wide Swath (IW) mode, which were acquired on descending orbits from 03 March 2018 to 22 January 2019. A WorldDEM Digital Surface Model (DSM) product was used to remove the topographic phase component. The results provided by both techniques showed a synoptic and informative view of the deformation process affecting the study area, with the detection of persistent trends of deformation on the crest, middle, and bottom sectors of the dam face until its collapse, as well as the settlements on the tailings. It is worth noting the detection of an acceleration in the displacement time-series for a short period near the failure. The maximum accumulated displacements detected along the downstream slope face were 39 mm − (SBAS) and 48 mm (PSI). It is reasonable to consider that Sentinel-1 would provide decision makers − with complementary motion information to the in situ monitoring system for risk assessment and for a better understanding of the ongoing instability phenomena affecting the tailings dam.

Keywords: Brumadinho; tailings dam; SBAS; PSI; Sentinel-1B

1. Introduction and Context Mining is an important activity for the Brazilian economy, and iron ore is its main mineral commodity. The Vale S.A. mining company is the world’s largest producer of iron ore and pellets, which are raw materials that are essential to the manufacture of steel. In 2018, the Brazilian trade balance hit USD 239.8 billion, with a surplus around USD 58.6 billion, and iron ore was related to 8.4% of the total value of exports. However, the Brazilian Gross National Product grew only 1.1% in 2019, after a better performance of 1.3% in 2018. One of the main causes of this reduction was the economic impact due to the failure of a tailings dam at the Córrego do Feijão mine, near Brumadinho city (Southeast of Brazil), which posted a loss to Vale S.A. of USD 7.402 billion [1]. Dam-I collapsed on 25 January 2019 and ranks as the 5th largest failure ever in recorded history, based on deaths, runout, and released waste material [2]. According to the Brazilian National Mining Agency (ANM), a total of 769 tailings dams were identiﬁed in Brazil at the beginning of 2019, with an accumulation of 3.5 billion of m3 of mining waste,

Remote Sens. 2020, 12, 3664; doi:10.3390/rs12213664 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 3664 2 of 22 and 84 tailing dams were raised using the upstream method [3]. This method is the cheapest but the least resilient of construction types, due to the fact that water is the primary instability agent [4]. The upstream method was the same related to the tailings dam of Fundão in Mariana (Brazil) that collapsed on 5 November 2015 [5] and Dam-I in Brumadinho. Tailings dam failures are extremely detrimental to mine productivity. An evaluation of temporal trends in tailings dam incidents relative to the cyclical prices of mineral commodities was examined, and there appears to be a correlation between a mining boom cycle and an increased number of ruptures. Basically, during periods of price elevation of commodities, procedures for the licensing and construction of tailings dams are accelerated to take advantage of the price cycle, while during a lower price, there is a pressure to cut operational costs, with impact on the maintenance and security [6]. The most frequent causes of ruptures are overtopping and overflowing, slope foundation problems including settlement and slope instability, seepage and internal erosion (piping), structural failure due to the material used in the construction, and inappropriate maintenance [7]. Slope failures occur in a wide range of conditions and environments, and precursory signals prior to a rupture may not be so evident. Therefore, monitoring activities are mainly focused on measuring the ground movement for failure prediction and risk assessment. Different procedures have been applied for the monitoring of tailings dams using geotechnical (piezometers, extensometers, shape array accelerometers, inclinometers, water level indicators, etc.) and non-geotechnical instruments (total station/reflecting prisms, video monitoring, laser scanner, ground stability radar, etc.). Lower cost instruments, such as total station/prisms and extensometers, are normally used for primary monitoring, but once instability has been detected, the ground-based radar is the best monitoring alternative, providing real-time data from fast movements (mm/day to few tens of cm/day) to very slow movements (mm/month to mm/year). However, despite the accuracy and reliability of data provided from these instruments, many precursory signals of ruptures cannot be detected due to several factors such as field measurements restricted to the points or sectors of the structure, fieldwork that can be costly and time consuming, adverse climatic conditions, restrictions to accessibility, inadequate field of view of the instrument, economical constraints, etc. Differential Interferometric Synthetic Aperture Radar (DInSAR) technology proved to be a valuable tool to monitor displacements in open pit mines and tailings dams [8–13], as well as hydric dams [14–16]. A recent paper was published to assess pre-disaster scenarios and direct causes of the tailings dam collapse in Brumadinho through the use of optical satellites and Sentinel-1 InSAR products [17]. Our study differs from this first contribution since it was focused on the detection of ground deformation patterns located along the downstream slope face before the dam-1 failure with a higher amount of measurement points through the use of two DInSAR techniques (SBAS and PSI) and a high-resolution DEM for topographic phase correction. The advantages of DinSAR in relation to the previous procedures are that satellite monitoring can be made without fieldwork or ground equipment, providing a high accuracy of surface displacements, over a dense grid of measurement covering a large area and in all-weather conditions. However, since DInSAR is not a real-time technique, a complementary use with field monitoring schemes is necessary for operational planning and risk assessment. Particularly to tailings dams, the good results with DinSAR in Brazil were obtained using high-resolution SAR images. However, the small swath of most operational high-resolution systems implies a huge number of scenes to cover tailings dams located in many regions, a high cost of coverage, and limitations regarding data accessibility, which are the major obstacles for the operational use of this technology for systematic monitoring. The advent of the Sentinel-1 SAR mission with two twin sensors (A and B) opened new possibilities for DInSAR applications with favorable characteristics: regional coverage due to a large swath (width = 250 km), systematic and regular interferometric observation, rapid product delivery (typically in less than 3 h from scene acquisition), and free data access, thus providing the scientific community and public/private sectors interferometric data suitable for monitoring applications. The potential of Sentinel-1 interferometric data has been demonstrated for surface deformation monitoring [18,19], Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 21

Remote Sens. 2020, 12, 3664 3 of 22 In this paper, we present and discuss results of the evaluation of Sentinel-1B data, Interferometric Wide swath (IW) mode and 12-day frequency of acquisition, aiming at detecting groundlandslide deformation [20], continental prior to mapping the catastrophic of deformation Dam-I [21 failure.], and the Since prediction SBAS of (Small catastrophic BAseline slope Subset) failures, [23] and includingPSI (Persistent tailings Scatterer dams [22], Interferometry) among other applications. [24–26] techniques are two distinct conceptions of In this paper, we present and discuss results of the evaluation of Sentinel-1B data, multi-interferogram algorithms, we adopted the use of both techniques in this investigation to Interferometric Wide swath (IW) mode and 12-day frequency of acquisition, aiming at detecting optimize the extraction and to combine complementary information for monitoring ground ground deformation prior to the catastrophic Dam-I failure. Since SBAS (Small BAseline Subset) [23] displacement.and PSI (Persistent Scatterer Interferometry) [24–26] techniques are two distinct conceptions of Analysismulti-interferogram of the time algorithms, failure we prediction adopted the usewas of performed both techniques from in thisthe investigation SBAS and to optimizePSI ground displacementsthe extraction measurements, and to combine based complementary on the invers informatione of the for monitoringvelocity method ground displacement.[27]. Early-warning analysis ofAnalysis slope failures of the in time an open-pit failure prediction mine using was ground-based performed from radar the was SBAS conducted and PSI by ground [28] using this method.displacements A precursor measurements, detection based of landslide on the inverse failure of with the velocitySentinel-1 method data was [27]. achieved Early-warning using the inverseanalysis velocity of slope method failures in in[22]. an open-pitThe potential mine using of the ground-based inverse ve radarlocity was method conducted for predicting by [28] using slope failurethis from method. satellite A precursor InSAR detectiondata in different of landslide scenarios failure with was Sentinel-1 analyzed data in [29], was achievedand the usingauthors the also exploredinverse the velocity quality method of the prediction in [22]. The based potential on the of the relative inverse frequency velocity method distribution for predicting of the errors slope and failure from satellite InSAR data in diﬀerent scenarios was analyzed in [29], and the authors also the coefficient of determination R2 from the inverse velocity predictions. explored the quality of the prediction based on the relative frequency distribution of the errors and the coeﬃcient of determination R2 from the inverse velocity predictions. 2. The Study Area 2. The Study Area Dam-I was part of the Córrego do Feijão mine at the Paraopeba Complex located in the BrumadinhoDam-I municipality, was part of Minas the C óGeraisrrego doState, Feij ãBrazilo mine (Figure at the 1). Paraopeba Geologically, Complex the Córrego located in do the Feijão mineBrumadinho is located along municipality, the western Minas border Gerais State,of Serra Brazil do (FigureCurral1 ).of Geologically,the Quadrilátero the C Ferríferoórrego do (Iron Quadrangle),Feijão mine which is located is the along main the iron western mineral border province of Serra in Brazil. do Curral The of area the Quadrilis relatedátero to Ferrthe íPiedadefero syncline(Iron that Quadrangle), verges to which NNW. is theThe main occurrence iron mineral of high-grade province in magnetitic–martitic Brazil. The area is related accumulation to the Piedade (>64% syncline that verges to NNW. The occurrence of high-grade magnetitic–martitic accumulation (>64% Fe) Fe) of medium size (≈100 Mt) is controlled by the combination of folds and high-angle thrust faults of medium size ( 100 Mt) is controlled by the combination of folds and high-angle thrust faults [30]. [30]. The mine is characterized≈ by the occurrence of ironstones known as itabirites from the The mine is characterized by the occurrence of ironstones known as itabirites from the plataformal plataformalsequence sequence of the Minas of the Supergroup Minas Supergroup with Paleoproterozoic with Paleoproterozoic age (2.1–2.0 age Ga). (2.1–2.0 Massive Ga). and Massive soft iron and soft ironores wereores formedwere formed by the supergene by the supergene process acting process during acting the Neogene during over the products Neogene associated over products with associatedhydrothermal with hydrothe enrichmentsrmal [ 31enrichments]. [31].

FigureFigure 1. ( 1.a) (aStudy) Study area area locationlocation in in Minas Minas Gerais Gerais state/ Brazil;state/Brazil; (b) Location (b) Location of Paraopeba of IronParaopeba Complex Iron in the Brumadinho municipality; (c) Paraopeba Iron Complex showing tailings Dam I and hydric Complex in the Brumadinho municipality; (c) Paraopeba Iron Complex showing tailings Dam I and Dam VI, ancillary mining structures such as ore treatment installation (ITM), rail network, access roads, hydric Dam VI, ancillary mining structures such as ore treatment installation (ITM), rail network, and a Sentinel-1B descending orbit ground track. access roads, and a Sentinel-1B descending orbit ground track.

The Córrego do Feijão mine was owned and developed by Ferteco Mineração S.A. until 27 April 2001, when it was acquired by Vale S.A. The production started at Córrego do Feijão mine in 1963 and Dam-I, placed in service in 1976, was specifically built to store its sinter tailings. In 2018, this

Remote Sens. 2020, 12, 3664 4 of 22

Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 21 The Córrego do Feijão mine was owned and developed by Ferteco Mineração S.A. until 27 mineApril produced 2001, when 8.5 million it was acquired tons of byiron Vale ore, S.A. which The production corresponded started to at 2% Córrego of the do Vale’s Feijão iron mine production in 1963 [32]. andDam-I Dam-I, was placed constructed in service using in 1976, the was upstream specifically method built toin store 15 stages its sinter between tailings. 1976 In 2018, and this 2013. mine Using this method,produced the 8.5 milliondam was tons constructed of iron ore, which to a maximum corresponded he toight 2% of the86 Vale’sm and iron a crest production length [ 32of]. 720 Dam-I m, with an intendedwas constructed maximum using capacity the upstream of 12 method million in 15cubi stagesc meters, between which 1976 andwere 2013. the Using capacity this method,and height essentiallythe dam attained was constructed at failure. to a maximumDam-I was height inactive of 86 mat andthe atime crest lengthof the offailure, 720 m, withdisposal an intended operations havingmaximum ceased capacityin 2015, of and 12 millionit was cubicin the meters, process which of being were decommissioned. the capacity and height essentially attained at failure. Dam-I was inactive at the time of the failure, disposal operations having ceased in 2015, and it was in the process of being decommissioned. The Dam-I Rupture TheAt about Dam-I Rupture12:28 p.m. local time on 25 January 2019, Dam-I suffered a failure, and within minutes the impoundedAt about material 12:28 p.m. had local liquefied, time on resulting 25 January in 2019, a catastrophic Dam-I suffered mudflow a failure, that and traveled within minutesrapidly into the catchmentthe impounded downstream material had along liquefied, the resultingCórrego indo a catastrophicFeijão stream mudflow (Figure that 2). traveled The rapidlywaste material into reachedthe catchmentthe confluence downstream with Paraopeba along the Có Riverrrego doin Feijhoursão streamand extending (Figure2). Thefor more waste than material 300 reached km toward the theSão confluence Francisco with River. Paraopeba Approximately River in hours 9.7 andmil extendinglion cubic for meters more than of 300waste km towardmaterial the S(almostão approximatelyFrancisco River. 75% Approximately of the pre-failure 9.7 million volume) cubic were meters involved of waste materialin the failure. (almost approximatelyBy the end of 75% March of the pre-failure volume) were involved in the failure. By the end of March 2020, a total of 259 people 2020, a total of 259 people had been confirmed dead, and 11 bodies were still missing. The mudflow had been confirmed dead, and 11 bodies were still missing. The mudflow also destroyed some parts of also destroyed some parts of the Córrego do Feijão district, including a nearby inn and several rural the Córrego do Feijão district, including a nearby inn and several rural properties, as well as sections properties,of a railway as well bridge as andsections about of 100 a mrailway of railway bridge track. and Agricultural about 100 areas m of in therailway valley track. below Agricultural the dam areaswere in the also valley damaged. below the dam were also damaged.

Figure 2. Pleiades satellite images taken just before and after the tragedy, showing the water Figure 2. Pleiades satellite images taken just before and after the tragedy, showing the water from from Dam-VI, the tailings from Dam-I, and the Corrégo do Feijão stream. Dam-VI, the tailings from Dam-I, and the Corrégo do Feijão stream. The water analysis from the Paraopeba River indicated high values of lead and mercury beyond theThe acceptable water analysis level, asfrom well the as Paraopeba the presence River of nickel, indicated cadmium, high and values zinc. of In lead addition, and mercury a large area beyond the acceptable(around 269 level, ha) along as well watercourses as the presence was destroyed of nickel, by cadmium, the flux of wasteand zinc. material, In addition, including a nativelarge area (aroundAtlantic 269forest ha) along vegetation watercourses and permanent was protectiondestroyed areas by the [33]. flux In the of rescuewaste of material, victims, the including removal ofnative Atlanticmud forest was carried vegetation out by and excavating permanent machines protection and visual areas location [33]. ofIn bodiesthe rescue by firefighters of victims, with the sni removalffer of mud was carried out by excavating machines and visual location of bodies by firefighters with sniffer dogs. Ground-penetrating radar (GPR) was also used aimed at mapping areas for future excavation and redirecting the rescue team [34]. A technical report produced by an expert panel contracted by Vale S.A. with full details and analyses on the Dam-I failure was presented on 12 December 2019 [35]. According to this report, analysis from the site-monitoring videos indicated that slope failure started from the dam crest and extended to an area just above the first raising. A mud wave up to 30 m was released, and the slope collapse was complete in less than 10 s, with the stored tailings flowing out of the dam in less than 5

Remote Sens. 2020, 12, 3664 5 of 22 dogs. Ground-penetrating radar (GPR) was also used aimed at mapping areas for future excavation and redirecting the rescue team [34]. A technical report produced by an expert panel contracted by Vale S.A. with full details and analyses on the Dam-I failure was presented on 12 December 2019 [35]. According to this report, analysis from the site-monitoring videos indicated that slope failure started from the dam crest and extended to an area just above the first raising. A mud wave up to 30 m was released, and the slope collapse was complete in less than 10 s, with the stored tailings flowing out of the dam in less than 5 min. The conclusions of the panel’s report pointed out that a combination of six causes for the failure led to flow liquefaction of the tailings: the design resulting in a steep upstream slope; the water management within the tailings impoundment allowing water to get close to the crest; the setback in the design pushing the upper portions of the slope over weaker fine tailings; the lack of significant internal drainage resulting in a high water level in the dam; the high iron content in the waste material that was potentially very brittle if triggered to become undrained, and the intense season rainfall. The monitoring system for Dam-I included a combination of instruments related to surface measurements technique at discrete points (total station/reflective prisms, inclinometers, piezometers), and over areas (Light Detection and Ranging-Lidar, seismograph, ground-based SAR), among other instruments. According to data from four seismographic stations near the study region, no vibrations were recorded on the day of the failure, and thus earthquakes and blastings were not triggers of the failure. Even though a ground-based SAR was in operation at Dam-I, according to the panel’s report, none of these instruments detected any significant clear trend of displacements, including the last months before the failure. A post-failure satellite InSAR analysis using data from distinct sources (Sentinel-1, TerraSAR-X, COSMO-Skymed) was also included in the final report covering the one-year period prior to failure. No information was provided regarding details of processing and modeling, which were used in this back-analysis. The results indicated slow and continuous small deformations in the range of 16 to 32 mm/year, particularly at the toe of the dam, starting from Mach/April 2018 with some acceleration during the wet season. Such deformations were interpreted as not being precursory signals of the failure.

3. Materials and Methods

3.1. Satellite Data To carry out this research, 26 SLC images from Sentinel-1B were used, at IW mode, VV polarization, descending orbits, 32.56◦ incidence angle, 12-day revisit, over a monitoring period of 11 months, from 16 March 2018 to 22 January 2019 (Table1). The orbit files were updated using the European Space Agency (ESA) repository of Sentinel-1 auxiliary files (Precise Orbit Ephemerides files, POEORB) for all S1-B scenes in order to calculate the interferograms with more precise orbital data. A WorldDEM DSM (Digital Surface Model) product provided by AIRBUS DEFENCE & SPACE Company, with 12 m spatial resolution and absolute vertical accuracy better than 4 m (LE 90), was used to remove the topographic phase component during the DInSAR processing. This WorldDEM was obtained on 11 October 2018 before the Dam-I collapse. A WorldView-3 image, acquired on 6 February 2018, with 30 cm of resolution, was used as an image background for the analysis of the results.

3.2. Methodological Approach Multi-temporal satellite SAR acquisitions have improved the capability for detecting temporal change of deformation phenomena. Time-series of SAR images gathered on the same area, with a small diﬀerence in the geometry acquisition, allows the generation of a stack of diﬀerential interferograms from which the analyses of the temporal ground displacement can be performed in the line of sight (LoS) direction. Remote Sens. 2020, 12, 3664 6 of 22

Table 1. Acquisition time of the Sentinel-1B data.

Acquisition Date Acquisition Date 1 20180316 14 20180831 2 20180328 15 20180912 3 20180409 16 20180924 4 20180421 17 20181006 5 20180503 18 20181018 6 20180515 19 20181030 7 20180527 20 20181111 8 20180608 21 20181123 9 20180620 22 20181205 10 20180702 23 20181217 11 20180714 24 20181229 12 20180726 25 20190110 13 20180819 26 20190122

3.2.1. DInSAR Analysis Time-series analysis based on multiple SAR acquisitions can be performed by a set of differential interferograms generated from the co-registered image stack. The phase components of each interferogram pixel are related to the topography, ground displacement, atmosphere, orbit error, and noise. A digital elevation model is used to remove the topographic phase component, which is the remaining phase related to the elevation model error. Multi-referenced and master-referenced are the two ways to build a stack of interferograms, and different types of techniques are established to carry out the DInSAR analysis, the SBAS, and the PSI. While PSI extracts the phase change by analyzing stable and dominant scatterers (persistent), the SBAS technique allows measuring the surface movements related to distributed scatterers with homogeneous characteristics providing a better spatial coverage [36]. Considering natural terrain, where distributed scatterers are dominant, PSI techniques can exhibit a low density of measurement points (radar targets), while SBAS can show better spatial coverage. However, PSI analysis can be more accurate over the sparse grid of measured points (MPs), while the SBAS approach relies on which interferograms are considered, how they have been filtered, and the number of subsets selected. The higher the average coherence of the interferograms, the more similar the results of both techniques [37]. The SBAS technique explores the concept of Small BAseline Subsets [23], which seeks to extract information based on every single interferogram available. The algorithm accounts for spatial decorrelation phenomena, partitioning the dataset into several subsets, each consisting of the images acquired from orbits close to each other, discarding the interferograms corresponding to large baselines [38]. After phase unwrapping of the stack interferograms, the estimation of the physical quantities of interest, such as the topographic errors and the ground displacement, are achieved by applying the Single Value Decomposition (SVD) algorithm, which provides the average velocity of the displacement showing its temporal evolution. In this work, SBAS analysis was performed with SARSCAPE software version 5.4.1, which was developed by the SARMAP AG Company [37]. A stack of 26 co-registered SLC images was used with a reference scene dated on 31 August 2018, which is located around the center of the time-series. Multi-look filtering using 2 looks in azimuth and 4 looks in range was applied in each SLC image. The stack of interferograms was built considering a constrain of 45% of the critical baseline (4966.68 m) and 80 days of temporal baseline, which showed to be the best setup to get measurements on the dam surface. Based on that, 129 interferograms were generated, wherein two of them were discarded due to phase unwrapping error during the inspection of the phase images. The spatial phase unwrapping processing of the interferograms was carried out by using the algorithm Minimum Cost Flow (MCF). We selected 13 Ground Control Points (GCP) in stable areas to estimate and remove the remaining phase constants and the phase ramp from the unwrapped phase stack. A low-pass filter Remote Sens. 2020, 12, 3664 7 of 22

covering a window of 1200 1200 m was applied on the interferograms to remove the atmospheric × phase components and perform the second inversion of the SBAS processing step. Figure3 presents the relative spatial position of each time acquisition (green dots), and the solid lines represent the interferograms built according to the constraints describe above. The yellow dot represents the Remotereference Sens. 2020 scene, 12, x FOR for the PEER co-registration REVIEW chosen on the middle of the time-series. 7 of 21

FigureFigure 3. Interferometric 3. Interferometric pairs pairs (solid (solid lines)lines) chose chose for for SBAS SBAS (Small (Small BAseline BAseline Subset) Subset) analysis; analysis; green dots green dots correspondcorrespond to theto the S1-B S1-B scenes scenes and yellow and dotyellow corresponds dot corresponds to the reference to scenethe reference for the co-registration scene for the (31 August 2018). co-registration (31 August 2018). The PSI technique works on a stack of differential interferograms master-referenced in fullThe resolution,PSI technique using works pixels withinon a thestack resolution of differenti cell thatal interferograms exhibit stable amplitude master-referenced backscattering in full resolution,properties using (persistent pixels scatterer,within the PS) resolution and coherent ce phasell that [24 exhibit–26]. At stable each pixelamplitude of the stackbackscattering of the propertiesco-registered (persistent images, scatterer, the PS candidates PS) and arecoherent estimated phase based [24–26]. on the amplitudeAt each pixel dispersion of the index stack [24 of], the co-registeredas well as images, with pixels the PS presenting candidates low are spectral estima diversityted based [25 on]. the In amplitude general, a PSdispersion does not index suffer [24], as wellspatial as with decorrelation, pixels presenting even when low the baselinesspectral arediversity larger than [25]. the In criticalgeneral, one, a allowingPS does thenot use suffer of long spatial decorrelation,baselines [39 even]. It iswhen normally the assumedbaselines that are the larger PS deformation than the rate critical is linear, one, because allowing generally, the use the timeof long baselinesdependence [39]. It of is the normally deformation assumed is not knownthat the a priori.PS deformation Deviations fromrate thisis linear, model because (residual generally, phase) are the assumed to be due to the atmospheric phase contribution, the nonlinear phase displacement component, time dependence of the deformation is not known a priori. Deviations from this model (residual and the phase noise. The topographic phase error can be estimated practically as a linear function with phase) are assumed to be due to the atmospheric phase contribution, the nonlinear phase the perpendicular baselines. The removal of the atmospheric-related phase can be accomplished by displacementfiltering the component, residual phase, and taking the advantagephase noise. of the The spatial topographic correlation andphase temporal error decorrelationcan be estimated of practicallythe atmospheric as a linear phase [40function]. with the perpendicular baselines. The removal of the atmospheric-relatedIn this work, phase PSI analysis can be was accomplished performed using by filtering the Interferometry the residual Point phase, Target taking Analysis advantage (IPTA) of the spatialmodule correlation approach [and25] implementedtemporal deco inrrelation GAMMA of RS the software. atmospheric The stack phase of [40]. 25 co-registered SLC imagesIn this waswork, built PSI based analysis on a master-referencedwas performed using image the acquired Interf onerometry 31 August Point 2018, Target considering Analysis the low (IPTA) moduleperpendicular approach baseline [25] implemented dispersion nearly in GAMMA at the center RS of thesoftware. time-series. The Thestack stack of of25 24 co-registered interferometric SLC pairs presents a diversity of baseline values from 50 up to 180 m, as shown in Figure4. In order images was built based on a master-referenced imag− e acquired on 31 August 2018, considering the low toperpendicular make the IPTA baseline approach dispersion more efficient nearly for PSIat analysisthe center with of S1 the IW data,time-series. we pre-estimated The stack the of 24 height corrections and linear deformation rates, using a multi-reference stack with multi-look of 3 1 interferometric pairs presents a diversity of baseline values from −50 up to 180 m, as shown in× Figure (3 in range), by performing the SVD algorithm to convert the multi-reference stack phases to a single 4. In order to make the IPTA approach more efficient for PSI analysis with S1 IW data, we reference time-series [41]. The phase related to the linear deformation rates and topographic phase pre-estimatederrors previously the height found corrections were subtracted and linear in modulus deformation 2π from rates, the PSusing phase, a multi-reference resulting in a residual stack with multi-lookwrapped of phase.3 × 1 (3 in range), by performing the SVD algorithm to convert the multi-reference stack phases to a single reference time-series [41]. The phase related to the linear deformation rates and topographic phase errors previously found were subtracted in modulus 2π from the PS phase, resulting in a residual wrapped phase. The residual ground displacement was estimated through a linear regression between the time and phase variation for each PS using the IPTA approach, considering limited deformation rates from −30 up to 30 mm/year. The phase standard deviation from the linear regression error was selected as 1.35 radians, allowing the detection and rejection of points not suitable for PSI analysis. Atmospheric phase delay may account for most of the linear regression deviation (residues) related to the deformation. Its components were strongly attenuated by using a spatial filter of 200 × 200 samples, considering its characteristic of being spatially correlated and temporally uncorrelated. After removing the atmospheric phase component (residues spatially filtered) and noise phase (residues temporarily filtered), the remaining phase accounts for the residual linear and nonlinear deformation phase and topographic error. After a stepwise iteration of the PSI analysis, the outputs corresponding to the linear deformation rates and the topographic error of each PS were added to

Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 21 the multi-referenced stack analysis results previously mentioned, providing the final ground displacement estimation and the final topographic error, pointing out that only the PS that fulfilled Remote Sens. 2020, 12, 3664 8 of 22 the PSI processing constraint was considered [10].

FigureFigure 4. Interferometric 4. Interferometric pairs pairs selected selected forfor PSI PSI (Persistent (Persistent Scatterer Scatterer Interferometry) Interferometry) analysis; analysis; green dots green dots correspondcorrespond to to the the S1-B S1-B scenes scenes and yellowand yellow dot corresponds dot corresponds to the reference to the scenereference (31 August scene2018). (31 August 2018). The residual ground displacement was estimated through a linear regression between the time and phase variation for each PS using the IPTA approach, considering limited deformation rates from 30 up − 3.2.2.to Failure 30 mm/ year.Prediction The phase standard deviation from the linear regression error was selected as 1.35 radians, allowingPrediction the of detection the time and of rejection a geomechanical of points not failure suitable is for a major PSI analysis. concern Atmospheric in the field phase of geological delay may risk management.account for The most interpretation of the linear regression of monitoring deviation (residues)data is one related of the to the main deformation. points of Its emphasis components when were strongly attenuated by using a spatial filter of 200 200 samples, considering its characteristic trying to predict the time of geomechanical failure (Tf)× or to assess the probability of an imminent of being spatially correlated and temporally uncorrelated. After removing the atmospheric phase collapse. Although a universal law that successfully accomplishes this goal for all the types of failure component (residues spatially filtered) and noise phase (residues temporarily filtered), the remaining mechanismsphase accounts does not for exist, the residuala good number linear and of em nonlinearpirically deformation derived methods phase and and topographic equations have error. been producedAfter ain stepwise the last iterationdecades. of Among the PSI them, analysis, the theeffe outputsctiveness corresponding of the inverse to velocity the linear method deformation has been demonstrated;rates and theit has topographic become a errorwidely of applied each PS tool were for added failure to prediction, the multi-referenced and its simplicity stack analysis of use is its mostresults powerful previously feature mentioned, [27]. providing the final ground displacement estimation and the final topographicThe accuracy error, of the pointing analytical out thatmethod only is the heavil PS thaty influenced fulfilled the by PSI the processing precision constraintand frequency was of the monitoringconsidered [data10]. [42]. The inverse velocity plot observed in [43] is in fact predominantly linear for landslides where the generation of a new shear surface and crack propagation are the dominant 3.2.2. Failure Prediction processes. In [44], they also found that the inverse velocity plot often approaches linearity, especially during thePrediction final stages of the of failure; time of ath geomechanicaley also highlighted failure the is aneed major to concern seek consistent in the field linear of geological trends in the data riskand management.their changes. The The interpretation authors of [45] of monitoring suggested datathat isthe one linear of the fitting main procedure points of emphasis should only when trying to predict the time of geomechanical failure (Tf) or to assess the probability of an include data following the identification of Onset of Acceleration (OOA) points. In addition, imminent collapse. Although a universal law that successfully accomplishes this goal for all the types inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity of failure mechanisms does not exist, a good number of empirically derived methods and equations plotshave are processes been produced that inmay the lastbe hampered decades. Among by the them, noise the present effectiveness in the of measurements; the inverse velocity therefore, method data smoothinghas been is demonstrated;a very important it has phase become of a inverse widely appliedvelocity tool analyses for failure [27]. prediction, and its simplicity of useThe is T itsf prediction most powerful can be feature performed [27]. by extrapolating the trend of the inverse velocity–time plot toward zero;The accuracythe point of where the analytical the extrapolation method is heavilyof this plot influenced intersects bythe the precision time axis and is an frequency estimation of theof time the monitoringof failure. The data coefficient [42]. The inverseof determination velocity plot R2 observedon the inverse in [43 ]velocity is in fact plot predominantly and the relative frequencylinear distribution for landslides of where the predicted the generation time and of a newreal failure shear surface times provide and crack a propagationmeasure of areconfidence the of thedominant prediction. processes. This In [approa44], theych also was found evaluated that the inversein this velocity research plot oftenusing approaches the trend linearity, of slope displacement,especially duringvelocity, the and final acceleration stages of failure; from they SBAS also and highlighted PSI results the needto determine to seek consistent whether linear detected trends in the data and their changes. The authors of [45] suggested that the linear fitting procedure deformations could anticipate the time of the Dam-I collapse. should only include data following the identification of Onset of Acceleration (OOA) points. In addition, A set of MPs localized on the face of the dam presenting significant ground displacement were inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity selectedplots to are carry processes out the that prediction may be hampered analysis byin both the noise results. present As pointed in the measurements; out in [27], the therefore, noise can hamperdata the smoothing prediction; is a very the importantdisplacement phase time-series of inverse velocity of each analyses MP selected [27]. was filtered by a moving average window of 3 points. The identification of the OOA point on the displacement time-series is an important issue to perform the inverse velocity method; in our study we selected this point

Remote Sens. 2020, 12, 3664 9 of 22

The Tf prediction can be performed by extrapolating the trend of the inverse velocity–time plot toward zero; the point where the extrapolation of this plot intersects the time axis is an estimation of the time of failure. The coefficient of determination R2 on the inverse velocity plot and the relative frequency distribution of the predicted time and real failure times provide a measure of confidence of the prediction. This approach was evaluated in this research using the trend of slope displacement, velocity, and acceleration from SBAS and PSI results to determine whether detected deformations could anticipate the time of the Dam-I collapse. A set of MPs localized on the face of the dam presenting significant ground displacement were selected to carry out the prediction analysis in both results. As pointed out in [27], the noise can

Remotehamper Sens. the 2020 prediction;, 12, x FOR PEER the REVIEW displacement time-series of each MP selected was filtered by a moving9 of 21 average window of 3 points. The identification of the OOA point on the displacement time-series is an basedimportant on the issue observation to perform of the the inverse SBAS velocityand PSI method;results that in our presented study we a selectedsignificant this increment point based in onthe displacementthe observation gradient of the SBASon the and crest PSI and results the thatface presentedof the dam. a significant increment in the displacement gradient on the crest and the face of the dam. 4. Results 4. Results

4.1.4.1. SBAS SBAS Analysis Analysis SBASSBAS measurements are are di ﬀdifferentialerential with with respect resp toect a referenceto a reference point and point their and dispersion, their dispersion, which is whichexpressed is expressed by standard by deviation standard of deviation the estimated of averagethe estimated displacement average rates, displacement and it increases rates, with and the it increasesdistance towith the the reference distance point. to the The reference results ofpoint. the deformation The results of velocity the deformation (mm/y) can velocity be visualized (mm/y) on can a behigh-resolution visualized on Wordview a high-resolution color composite Wordview scene colo (Figurer composite5a). Positive scene values (Figure correspond 5a). Positive to motion values correspondtoward the to satellite, motion and toward negative the satellite, values correspond and negative to motionvalues correspond away from theto motion satellite. away The from highest the satellite.values of The deformation highest values were recorded of deformation on the tailings were (reddishrecorded colors), on the while tailings lower (reddish values werecolors), detected while lowerfor the values top and were middle detected sectors for of the the top dam and slope middle (yellowish sectors colors). of the The dam bottom slope of(yellowish the dam showedcolors). theThe bottomlowest valuesof the ofdam deformation showed (greenishthe lowest color). values The of standard deformation deviation (greenish of the deformationcolor). The velocitystandard deviation(Figure5b) of is the associated deformation with a velocity dominance (Figure of points 5b) withis associated low values with (greenish a dominance colors), of i.e., points points with where low valuesthe precision (greenish of colors), the estimated i.e., points displacement where the rateprecision is higher, of the while estimated sparse displacement points with rate high is valueshigher, while(reddish sparse colors) points are with indicative high values of lower (reddish precision. colors) are indicative of lower precision.

(a) (b)

FigureFigure 5. 5. MapMap of of the the displacement displacement rate rate ( (aa)) and and the standard deviationdeviation ((bb)) fromfrom thethe SBASSBAS analysis; analysis; whitewhite circles circles represent the analyzed pointspoints inin thethedam dam reservoir reservoir ( Rs(Rs),), crest crest (Ts (Ts) and) and bottom bottom face face (Bs (Bs). ).

TheThe SBAS resultsresults of of the the accumulated accumulated deformation deformatio (mm)n (mm) over over a 3D a perspective 3D perspective view ofview Dam-I of usingDam-I usingWorldDEM WorldDEM and a Worldview and a Worldview image are shownimage inare Figure shown6. It in can Figure be noticed 6. It that can the be maximum noticed that values the maximum values of deformation occurred on the tailings (reddish dots) and on the crest of the dam, intermediate values on the dam slope (yellowish dots), while the stable regions (greenish dots) were spread in some parts of the dam slope and some mining structures.The results obtained showed a reasonable density of MPs with an average of 2259.24 MPs/km2 along the downstream dam face.

Remote Sens. 2020, 12, 3664 10 of 22

of deformation occurred on the tailings (reddish dots) and on the crest of the dam, intermediate values on the dam slope (yellowish dots), while the stable regions (greenish dots) were spread in some parts of the dam slope and some mining structures.The results obtained showed a reasonable density of 2 RemoteMPs Sens. with 2020 an, average12, x FOR ofPEER 2259.24 REVIEW MPs /km along the downstream dam face. 10 of 21

Figure 6. 3D view of Dam-I showing the accumulated displacement from SBAS processing analysis Figure 6. 3D view of Dam-I showing the accumulated displacement from SBAS processing analysis represented by colored dots. represented by colored dots. The time-series displacements in the graphs of Figure7 show the di fference in deformation behaviorThe time-series for three selected displacements points located in the at graphs the downstream of Figure slope 7 show face the slope difference and within in thedeformation tailings. behaviorAn almost for three similar selected deformation points trend,located with at the movements downstream away slope from face the slope satellite and (negative within the values), tailings. Anwas almost detected similar at the deformation top and bottom trend, of the with dam. movements This trend is away characterized from the by satellite a first period (negative with avalues), slow waslinear detected displacement at the top gradient and bottom from the of beginningthe dam. This of the trend coverage is characterized up to late August by a 2018,first period which waswith a slowfollowed linear bydisplacement a phase of relativegradient steadiness from the beginning from August of the 2018 coverage until the up beginning to late August of October 2018, 2018, which wasand followed finally a secondby a phase period of with relative a slightly steadiness higher gradientfrom August of linear 2018 displacement until the upbeginning the last Sentinel-1of October 2018,imagery and finally on 22 January a second 2019 period (three with days a before slightly the higher failure). gradient The profile of linear for measured displacement deformation up the in last Sentinel-1the tailings imagery is also linear,on 22 withJanuary a higher 2019 gradient (three days of acceleration before the as failure). compared The to theprofile dam facefor measured sectors. deformationAn increase in in the the tailings velocity is of also deformation linear, with was a identifiedhigher gradient for the of bottom acceleration sector andas compared tailings from to the damthe face beginning sectors. of An December increase 2018 in the up velocity to the end of deformation of the SAR coverage. was identified Results for with the SBAS bottom processing sector and indicated a movement away from the sensor with a maximum accumulated displacement of 39 mm tailings from the beginning of December 2018 up to the end of the SAR coverage. Results with− SBAS (average rate of 46 mm/year) for the top of the dam, of 31 mm (average rate of 36 mm/year) for the processing indicated− a movement away from the− sensor with a maximum− accumulated bottom of the dam, and 79 mm average (average rate = 92 mm/year) for the tailings. displacement of −39 mm −(average rate of −46 mm/year) −for the top of the dam, of −31 mm (average Analyzing the SBAS deformation velocity for each sector of Figure7, it can be noted that the crest rate of −36 mm/year) for the bottom of the dam, and −79 mm average (average rate = −92 mm/year) deformation rate in sector A was the highest ( 50.71 mm/year), while in sector B, the rate was low for the tailings. − tending to stability; however, in sector C, the deformation velocity increases, almost reaching the value of sector A. In turn, the bottom of the dam presented a moderate value of deformation velocity in

sector A, the stability of sector B, and the greatest deformation acceleration ( 60.59 mm/y) for sector C. − The linear regression of the base data showed a stronger acceleration in sector C; this is probably due to the fact that the dam swelled on the slope before collapsing. Table2 shows the SBAS deformation rate for each sector deﬁned in Figure7.

Figure 7. Time-series of SBAS line of sight (LoS) displacements for selected measured points (MPs) located at three selected points of the dam, as presented in Figure 5a, showing the highest cumulative displacements for the entire period of monitoring and the linear regression of the data (letters A, B, and C are the displacement trends as discussed in the text).

Analyzing the SBAS deformation velocity for each sector of Figure 7, it can be noted that the crest deformation rate in sector A was the highest (−50.71 mm/year), while in sector B, the rate was

Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 21

Figure 6. 3D view of Dam-I showing the accumulated displacement from SBAS processing analysis represented by colored dots.

The time-series displacements in the graphs of Figure 7 show the difference in deformation behavior for three selected points located at the downstream slope face slope and within the tailings. An almost similar deformation trend, with movements away from the satellite (negative values), was detected at the top and bottom of the dam. This trend is characterized by a first period with a slow linear displacement gradient from the beginning of the coverage up to late August 2018, which was followed by a phase of relative steadiness from August 2018 until the beginning of October 2018, and finally a second period with a slightly higher gradient of linear displacement up the last Sentinel-1 imagery on 22 January 2019 (three days before the failure). The profile for measured deformation in the tailings is also linear, with a higher gradient of acceleration as compared to the dam face sectors. An increase in the velocity of deformation was identified for the bottom sector and tailings from the beginning of December 2018 up to the end of the SAR coverage. Results with SBAS processing indicated a movement away from the sensor with a maximum accumulated displacement of −39 mm (average rate of −46 mm/year) for the top of the dam, of −31 mm (average

Remoterate Sens. of2020 −36, 12mm/year), 3664 for the bottom of the dam, and −79 mm average (average rate = −9211 mm/year) of 22 for the tailings.

FigureFigure 7. Time-series 7. Time-series of SBAS of SBAS line ofline sight of sight (LoS) (LoS) displacements displacements for selected for selected measured measured points points (MPs) (MPs) locatedlocated at three at three selected selected points points of the of dam, the dam, as presented as presented in Figure in Figure5a, showing 5a, showing the highest the highest cumulative cumulative displacementsdisplacements for thefor entirethe entire period period of monitoring of monitoring and theand linear the linear regression regression of the of data the (lettersdata (letters A, B, A, B, andand C are C theare displacementthe displacement trends trends as discussed as discussed in the in text). the text).

Analyzing the SBASTable deformation 2. SBAS deformation velocity for rate each for eachsector sector. of Figure 7, it can be noted that the crest deformation rate in sector A was the highest (−50.71 mm/year), while in sector B, the rate was SBAS: Crest (Ts) SBAS: Bottom (Bs)

Sector A 50.71 mm/y 25.25 mm/y − − Sector B 13.78 mm/y 2.74 mm/y − − Sector C 48.07mm/y 60.59 mm/y − −

4.2. PSI Analysis The result of the PSI analysis presents a pattern of ground displacement rate as shown in Figure8a, and its corresponding standard deviation of the velocity is shown in Figure8b. The displacement in the tailings can be attributed to settlements, and the maximum accumulated value during the monitoring period was 67 mm. A pattern of ground deformation can be noticed spread on the slope face of Dam-I, − with a maximum accumulation of 48 mm. The standard deviation of the deformation rates depends − on the distance from the reference point, which in this case was about 1400 m from the Dam-I center, but it also depends on the deformation behavior in terms of rate and nonlinearity. Figure8b shows higher values in the tailing reservoir, close to 8 mm/year, and values from 2 to 6 mm/year along the downstream slope face of Dam-I. The distribution of the MPs with accumulated deformations depicted over a 3D perspective view of using WorldDEM and a Worldview image is presented in Figure9. It can be observed that the maximum values of deformations, around –70 mm, were detected on the tailings (reddish dots). Some sectors of the dam face presented MPs with deformations up to 40 mm (orangish dots). − Stability was detected for some sectors of the mining structures (greenish dots). The results obtained showed a reasonable density of MPs with an average of 1174.80 MPs/km2 on the slope face. Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 21

low tending to stability; however, in sector C, the deformation velocity increases, almost reaching the value of sector A. In turn, the bottom of the dam presented a moderate value of deformation velocity in sector A, the stability of sector B, and the greatest deformation acceleration (−60.59 mm/y) for sector C. The linear regression of the base data showed a stronger acceleration in sector C; this is probably due to the fact that the dam swelled on the slope before collapsing. Table 2 shows the SBAS deformation rate for each sector defined in Figure 7.

Table 2. SBAS deformation rate for each sector.

SBAS: Crest (Ts) SBAS: Bottom (Bs) Sector A −50.71 mm/y −25.25 mm/y Sector B −13.78 mm/y −2.74 mm/y Sector C −48.07mm/y −60.59 mm/y

(a) (b)

Figure 8. MapsMaps of of the the displacement displacement rate rate (a (a) )and and the the standard standard deviation deviation (b (b) )from from the the PSI PSI analysis; analysis; white circles circles represent represent the the analyzed analyzed points points in in the the dam dam reservoir reservoir ( Rp(Rp),), crest crest ( Tp(Tp)) and and bottom bottom face face ( Bp(Bp).). Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 21 The distribution of the MPs with accumulated deformations depicted over a 3D perspective view of using WorldDEM and a Worldview image is presented in Figure 9. It can be observed that the maximum values of deformations, around –70 mm, were detected on the tailings (reddish dots). Some sectors of the dam face presented MPs with deformations up to −40 mm (orangish dots). Stability was detected for some sectors of the mining structures (greenish dots). The results obtained showed a reasonable density of MPs with an average of 1174.80 MPs/km2 on the slope face.

FigureFigure 9. View9. View in 3D in of 3D Dam-I of Dam-I showing showing the accumulated the accumulated displacement displacement from PSI from analysis PSI representedanalysis byrepresented colored dots. by colored dots.

Time-seriesTime-series displacements displacements in in the the graphs graphs of of Figure Figure 10 10 are are related related to to three three selected selected points points with with the maximumthe maximum LoS cumulative LoS cumulative displacements, displacements, which which are locatedare located at the at bottomthe bottom and and top top of the of the face face slope andslope within and the within tailings. the tailings. Differences Differences in the deformation in the deformation profiles profiles can beseen can be for seen the damfor the face, dam which face, are which are given by a first gradient of continuous linear deformation from the beginning of given by a first gradient of continuous linear deformation from the beginning of monitoring until late monitoring until late August 2018, a second period of relative steadiness from 31 August 2018 up to August 2018, a second period of relative steadiness from 31 August 2018 up to 6 October 2018, and a last 6 October 2018, and a last period of more intense deformation from the beginning of October 2018 period of more intense deformation from the beginning of October 2018 until the end of the coverage until the end of the coverage (22 January 2019). During this last period, an increase of acceleration (22 January 2019). During this last period, an increase of acceleration was recorded particularly for the was recorded particularly for the bottom of the dam starting from the beginning of December 2018 bottomup to ofthe the end dam of startingthe SAR fromcoverage. the beginning The deformation of December profile 2018 for upthe totailings the end is ofcharacterized the SAR coverage. by a Thelinear, deformation continuous, profile and more for the intense tailings gradient is characterized of acceleration by when a linear, compared continuous, to the points and more located intense at gradientthe face of slope. acceleration The maximum when cumu comparedlative displacements to the points locatedand average at the rates face with slope. PSI processing The maximum for cumulativethe crest, bottom, displacements and tailings and averagewere −42 ratesmm (rate with = PSI −53 processing mm/year), for−48 themm crest, (−50 mm/year), bottom, and and tailings −67 weremm 42(−78 mm mm/year), (rate = respectively.53 mm/year), 48 mm ( 50 mm/year), and 67 mm ( 78 mm/year), respectively. − − − − − −

Figure 10. Time-series PSI LoS displacements for three selected points located at three sectors of Dam-I as presented in Figure 8a showing the highest cumulative displacements for the entire period of monitoring and the linear regression of the data (letters A, B, and C are the deformation trends as discussed in text).

Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 21

Figure 9. View in 3D of Dam-I showing the accumulated displacement from PSI analysis represented by colored dots.

Time-series displacements in the graphs of Figure 10 are related to three selected points with the maximum LoS cumulative displacements, which are located at the bottom and top of the face slope and within the tailings. Differences in the deformation profiles can be seen for the dam face, which are given by a first gradient of continuous linear deformation from the beginning of monitoring until late August 2018, a second period of relative steadiness from 31 August 2018 up to 6 October 2018, and a last period of more intense deformation from the beginning of October 2018 until the end of the coverage (22 January 2019). During this last period, an increase of acceleration was recorded particularly for the bottom of the dam starting from the beginning of December 2018 up to the end of the SAR coverage. The deformation profile for the tailings is characterized by a linear, continuous, and more intense gradient of acceleration when compared to the points located at the face slope. The maximum cumulative displacements and average rates with PSI processing for Remotethe crest, Sens. bottom,2020, 12, 3664and tailings were −42 mm (rate = −53 mm/year), −48 mm (−50 mm/year), and13 of−67 22 mm (−78 mm/year), respectively.

Figure 10. Time-seriesTime-series PSI PSI LoS LoS displacements displacements for for three three se selectedlected points points located located at three sectors of Dam-I as presented in Figure8 8aa showingshowing thethe highesthighest cumulativecumulative displacementsdisplacements forfor thethe entireentire periodperiod of monitoring and the linear regression of the data (letters A, B, and C are the deformation trends as discussed in text).

The PSI deformation velocity behavior of sectors of Figure 10 showed the highest values of deformation rate for sector C, with similarity between the crest ( 64.49 mm/y) and the base dam − ( 65.10 mm/y) values. For sector A, the crest value ( 46.97 mm/y) was lower than the dam base − − ( 63.08 mm/y). In its turn, the base dam value for sector A ( 63.08 mm/y) were similar to those of − − sector C ( 64.49 mm/y). This similar trend can be observed by the regression for crest and base behavior − of this period, showing that they were practically parallels. Table3 shows the PSI deformation rate for each sector delimited in Figure 10.

Table 3. PSI deformation rate for each sector.

PSI: Crest (Tp) PSI: Base (Bp) Sector A 46.97 mm/y 63.08 mm/y − − Sector B 11.48 mm/y 0.84 mm/y − − Sector C 64.49 mm/y 65.10 mm/y − −

4.3. Failure Prediction Analysis In an attempt to predict the date of the dam failure based on SBAS and PSI results, 21 MPs (not coincident) located on the crest and along the dam face presenting meaningful ground displacement were selected to perform the inverse of the slope velocity versus time procedure. The first analysis was performed on the average of these 21 MPs of both results, as shown in Figures 11a and 12a for SBAS and PSI, respectively; 17 December 2018 was considered our OOA point based on the displacement behavior shown in Figure7, Figure 10, Figure 11a, and Figure 12a, where most graphs show the inflection point in the displacement, which is considered the onset of the final acceleration phase. In order to estimate the date of the rupture, a linear regression was applied on the inverse of the velocity data, and its intersection with the time axis provided the predicted time failure (Tpf). The coefficient of determination R2 shows how adjusted the linear regression was with the inverse velocity data. Figure 11b shows both graphs, the inverse of the velocity and the linear regression, with an adjusted R2 equal to 0.88, and with the interception found 10 days after the actual time failure (Taf). It is worth pointing out that this result was obtained for the average displacement calculated over 21 MPs. Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 21

The PSI deformation velocity behavior of sectors of Figure 10 showed the highest values of deformation rate for sector C, with similarity between the crest (−64.49 mm/y) and the base dam (−65.10 mm/y) values. For sector A, the crest value (−46.97 mm/y) was lower than the dam base (−63.08 mm/y). In its turn, the base dam value for sector A (−63.08 mm/y) were similar to those of sector C (−64.49 mm/y). This similar trend can be observed by the regression for crest and base behavior of this period, showing that they were practically parallels. Table 3 shows the PSI deformation rate for each sector delimited in Figure 10.

Table 3. PSI deformation rate for each sector.

PSI: Crest (Tp) PSI: Base (Bp) Sector A −46.97 mm/y −63.08 mm/y Sector B −11.48 mm/y −0.84 mm/y Sector C −64.49 mm/y −65.10 mm/y

4.3. Failure Prediction Analysis In an attempt to predict the date of the dam failure based on SBAS and PSI results, 21 MPs (not coincident) located on the crest and along the dam face presenting meaningful ground displacement were selected to perform the inverse of the slope velocity versus time procedure. The first analysis was performed on the average of these 21 MPs of both results, as shown in Figures 11a and 12a for SBAS and PSI, respectively; 17 December 2018 was considered our OOA point based on the displacement behavior shown in Figures 7, 10, 11a, and 12a, where most graphs show the inflection point in the displacement, which is considered the onset of the final acceleration phase. In order to estimate the date of the rupture, a linear regression was applied on the inverse of the velocity data, and its intersection with the time axis provided the predicted time failure (Tpf). The coefficient of determination R2 shows how adjusted the linear regression was with the inverse velocity data. Figure 11b shows both graphs, the inverse of the velocity and the linear regression, with an adjusted R2 equal to 0.88, and with the interception found 10 days after the actual time failure (Taf). ItRemote is worth Sens. 2020 pointing, 12, 3664 out that this result was obtained for the average displacement calculated14 over of 22 21 MPs.

(a) (b) Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 21 Figure 11. SBAS averageaverage displacementdisplacement ( a()a) and and inverse inverse of of the the velocity velocity graph graph with with linear linear regression regression (b). (b).

The same analysis as previously described was performed based on the average of 21 MPs from the PSI results. The profile of the averaged displacements can be seen in Figure 12a. Figure 12b exhibits the graphs of the inverse of the velocity and the linear regression, with an adjust R2 equal to 0.91, with the interception occurring 8 days after the rupture.

(a) (b)

Figure 12. PSI average displacement ( a) and inverse of the velocity graph with linear regressionregression ((bb).).

The sameresults analysis of the inverse as previously of the describedvelocity analysis was performed shown in based Figures on the11b averageand 12b ofreveal 21 MPs that from the thetendency PSI results. of the The inverse profile velocity of the averaged plot does displacements not agree with can bethe seen linear in Figure regression, 12a. Figure despite 12 bthe exhibits high 2 thevalue graphs of R2; of this the behavior inverse ofcan the be velocity related andto the the low linear sampling regression, frequency with an(12 adjust days of R theequal Sentinel-1 to 0.91, withrevisit the time), interception the nature occurring of the 8 final days acceleration after the rupture. phase (Dam-I suffered an abrupt rupture event accordingThe results to the ofpanel’s the inverse report of[35]), the and velocity the number analysis of shown displacement in Figures samples 11b and analyzed. 12b reveal that the tendencyThe analysis of the inverse was also velocity conducted plot does at each not agree of the with separate the linear 21 MPs regression, from the despite SBAS theresults, high valueusing 2 ofthe R inverse; this behavior velocity can method, be related which to the provided low sampling the frequencypredict time (12 daysfailure of the(Tpf) Sentinel-1 for each revisit point. time), By thesubtracting nature of the the Tpf final from acceleration the actual phase time (Dam-I of failur sueff ered(Taf), an we abrupt got a rupturedistribution event of according the prediction to the panel’serrors, as report shown [35 ]),in andFigure the 13a, number where of a displacement positive error samples indicates analyzed. that the failure occurred earlier than the estimate.The analysis The wasR2 coefficients also conducted distributions at each of from theseparate the inverse 21 MPs velocity from predictions the SBAS results, can be using seen the in inverseFigure 13b. velocity method, which provided the predict time failure (Tpf) for each point. By subtracting the Tpf from the actual time of failure (Taf), we got a distribution of the prediction errors, as shown in Figure 13a, where a positive error indicates that the failure occurred earlier than the estimate. The R2 coefficients distributions from the inverse velocity predictions can be seen in Figure 13b. The same analysis using PSI results related to 21 MPs with meaningful ground displacement, which are located on the crest and along the dam face, showed a distribution of the prediction errors as illustrated in Figure 14a; the R2 coefficients distribution is shown in Figure 14b. The relative frequency distribution of the prediction errors (Tpf–Taf) shown in Figures 13a and 14a reveal that the accelerating trends of displacement from 17 December 2018 to 22 January 2019 were not strong enough to predict the failure time. The reliability of the predictions appears to be quite low in both results; the distribution of the errors is visibly scattered, although in the neighborhood of 5days from the rupture, PSI analysis presents(a) a maximum frequency of two repetitions.(b) Despite the low confident prediction, the R2 distribution shown in Figures 13b and 14b presents quite good adjustments Figure 13. SBAS relative frequency distribution of the errors (a) and R2 coefficient from the inverse with the linear regressions and low standard deviation. velocity predictions (b).

The same analysis using PSI results related to 21 MPs with meaningful ground displacement, which are located on the crest and along the dam face, showed a distribution of the prediction errors as illustrated in Figure 14a; the R2 coefficients distribution is shown in Figure 14b.

(a) (b)

Remote Sens. 2020, 12, x FOR PEER REVIEW 14 of 21

(a) (b)

Figure 12. PSI average displacement (a) and inverse of the velocity graph with linear regression (b).

The results of the inverse(a) of the velocity analysis shown in Figures 11b (andb) 12b reveal that the tendency of the inverse velocity plot does not agree with the linear regression, despite the high valueFigure of R2 ;12. this PSI behavior average displacement can be related (a) andto the inverse low of sa thempling velocity frequency graph with (12 linear days regression of the Sentinel-1 (b). revisit time), the nature of the final acceleration phase (Dam-I suffered an abrupt rupture event The results of the inverse of the velocity analysis shown in Figures 11b and 12b reveal that the according to the panel’s report [35]), and the number of displacement samples analyzed. tendency of the inverse velocity plot does not agree with the linear regression, despite the high The analysis was also conducted at each of the separate 21 MPs from the SBAS results, using value of R2; this behavior can be related to the low sampling frequency (12 days of the Sentinel-1 the inverse velocity method, which provided the predict time failure (Tpf) for each point. By revisit time), the nature of the final acceleration phase (Dam-I suffered an abrupt rupture event subtracting the Tpf from the actual time of failure (Taf), we got a distribution of the prediction according to the panel’s report [35]), and the number of displacement samples analyzed. errors, as shown in Figure 13a, where a positive error indicates that the failure occurred earlier than The analysis was also conducted at each of the separate 21 MPs from the SBAS results, using the estimate. The R2 coefficients distributions from the inverse velocity predictions can be seen in the inverse velocity method, which provided the predict time failure (Tpf) for each point. By Figure 13b. subtracting the Tpf from the actual time of failure (Taf), we got a distribution of the prediction errors, as shown in Figure 13a, where a positive error indicates that the failure occurred earlier than 2 Remotethe estimate. Sens. 2020 ,The12, 3664 R coefficients distributions from the inverse velocity predictions can be seen15 of in 22 Figure 13b.

(a) (b)

Figure 13. SBAS relative frequency distribution of the errors (a) and R2 coefficient from the inverse velocity predictions (b). (a) (b) The same analysis using PSI results related to 21 MPs with meaningful ground displacement, 22 whichFigure are 13.located13. SBAS on relative the crest frequency and distributiondistalongribution the dam of thethe face, errorserrors showed ((aa)) andand RaR dicoecoefficientstributionﬃcient from of thethethe inverseinverse prediction velocity predictions (b). errorsvelocity as illustrated predictions in Figure (b). 14a; the R2 coefficients distribution is shown in Figure 14b.

The same analysis using PSI results related to 21 MPs with meaningful ground displacement, which are located on the crest and along the dam face, showed a distribution of the prediction errorsRemote Sens. as illustrated 2020, 12, x FOR in FigurePEER REVIEW 14a; the R2 coefficients distribution is shown in Figure 14b. 15 of 21

Figure 14. PSI relative frequency distribution of the errors (a) and R2 coefficient from the inverse velocity predictions (b).

The relative frequency distribution of the prediction errors (Tpf–Taf) shown in Figures 13a and 14a reveal that the accelerating trends of displacement from 17 December 2018 to 22 January 2019 were not strong enough to predict the failure time. The reliability of the predictions appears to be quite low in both results; the distribution(a) of the errors is visibly scattered,(b) although in the neighborhoodFigure 14. PSIof 5 relative days frequencyfrom the distributionrupture, PSI of theanalysis errors presents (a) and R 2a coemaximumfficient from frequency the inverse of two repetitions.velocity Despite predictions the ( blow). confident prediction, the R2 distribution shown in Figures 13b and 14b presents quite good adjustments with(a) the linear regressions and low standard (deviation.b) Figure 1515 shows shows the the PSI PSIand andSBAS SBAS results results and the and location the locationof two MPs, of twoBs and MPs, Bp, representing Bs and Bp, representingrespectively respectivelythe SBAS and the PSI SBAS points and PSIwith points the highest with the accumulated highest accumulated deformation deformation values on values the onbottom the bottomof the dam. of the Their dam. geographic Their geographic positions were positions different were for di bothfferent SBAS for and both PSI, SBAS which and were PSI, whichseparated were by separated 68 m, due by 68to m,the due different to the ditargetfferent identification target identification characteristics characteristics for which for whichthese thesetechniques techniques work. work. The behavior The behavior of these of these two two points points was was analyzed analyzed in inFigure Figure 16 16 in in relation relation to the precipitationprecipitation occurredoccurred inin thethe periodperiod fromfrom 0303 MarchMarch 20182018 toto 2222 JanuaryJanuary 2019.2019.

Figure 15. SBAS and PSI results combined and the locations of Bs and Bp points (black circles), which are Figure 15. SBAS and PSI results combined and the locations of Bs and Bp points (black circles), which the SBAS and PSI points with the highest accumulated deformation values on the dam bottom; SBAS MPs are the SBAS and PSI points with the highest accumulated deformation values on the dam bottom; are represented by square symbols, and PSI MPs are represented by round symbols. SBAS MPs are represented by square symbols, and PSI MPs are represented by round symbols.

An automatic meteorological station (#310900606A) from the Brazilian National Center for Monitoring and Early Warning of Disasters (CEMADEN) in the Brumadinho city, close to the study area, provided the rainfall information during the period of the Sentinel-1B coverage. The accumulated displacements behavior for the MPs Bs and Bp, not coincidentally located at the bottom of the dam (Figure 15), and the mensal accumulated precipitations are shown in Figure 16. It can be seen that there is a clear influence of the changes in the pluviometry favoring the acceleration of the movements: during the dry seasons (April to August), the variation of deformation is small or tending to stabilization, while during the wet seasons (September–March), an increment of deformation velocity can be noticed. According to [46], a review of rainfall data also revealed a gradual increment of precipitation for the quarter period of the wet season (October–December) in 2018 when compared to the previous three years (62% compared to 2015, 28% compared to 2016, and 15% compared to 2017). The accelerating trend of deformation for the PSI point (Bp) during the dry season can be related to deformation on the slope, as partly recorded by the ground-based SAR and survey data in Appendix D [35]. Due to the separation between Bs and Bp and the multi-looking processing performed in SBAS processing, this behavior was not detected with the Bs point during the dry period.

Remote Sens. 2020, 12, 3664 16 of 22 Remote Sens. 2020, 12, x FOR PEER REVIEW 16 of 21

Figure 16. Time-series of LoS displacement for two selected points located at the bottom of the dam Figure 16. Time-series of LoS displacement for two selected points located at the bottom of the dam showing the maximum cumulative displacements for the entire monitoring period and the accumulated showing the maximum cumulative displacements for the entire monitoring period and the pluviometric data. accumulated pluviometric data. An automatic meteorological station (#310900606A) from the Brazilian National Center for MonitoringPearson’s and correlation Early Warning tests of were Disasters performed (CEMADEN) between in the the Brumadinho paired variables city, close of the to the accumulated study area, provideddeformation the and rainfall the accumulated information duringprecipitation, the period for the of dry the Sentinel-1Band rainy seasons. coverage. We Thefound accumulated that for the displacementsSBAS technique, behavior the correlation for the MPswas Bssignificant and Bp, notwith coincidentally values of −0.9622 located for atthe the crest bottom of the of dam the damand (Figure−0.9415 15for), the and bottom the mensal of the accumulated dam during precipitations the rainy season, are shownwhile the in Figureresults 16 for. Itthe can PSI be technique seen that thereverified is a values clear influence of −0.9802 of for the the changes crest of in the dam pluviometry and −0.9237 favoring for the the bottom acceleration of the of dam. the movements: duringFor the the dry dry seasons season (April period, to August), the correlations the variation were of deformationlower between is small the orvariables, tending towith stabilization, values of while−0.8273 during for PSI the and wet seasons−0.9030 (September–March),for SBAS for the crest an increment of the dam. of deformation For the bottom velocity of canthe bedam, noticed. the Accordingcorrelation tovalue [46], was a review −0.6651 of rainfall SBAS and data − also0.7138 revealed for the a gradualPSI. Thus, increment the greatest of precipitation correlations for were the quarterobserved period between of the displacements wet season (October–December)and rainfall during the in rainy 2018 whenseason. compared to the previous three years (62% compared to 2015, 28% compared to 2016, and 15% compared to 2017). The accelerating 5. Discussion trend of deformation for the PSI point (Bp) during the dry season can be related to deformation on the slope,SBAS as and partly PSI recordedanalysis byprovided, the ground-based for each measurement SAR and survey point, data a intime-series Appendix of D deformation [35]. Due to thedescribing separation the betweenbehavior Bs of andLoS Bp di andsplacement the multi-looking velocity over processing the entire performed period of in monitoring. SBAS processing, More thisprecisely, behavior the wastemporal not detected series of with displacements the Bs point sh duringowed thean almost dry period. similar behavior for the crest and bottomPearson’s of the slope correlation face, with tests three were deformation performed phases between given the pairedby a first variables linear acceleration of the accumulated gradient deformation(March 2018–August and the accumulated 2018), a period precipitation, of relative for stabilization the dry and (August rainy seasons. 2018–October We found 2018), that forand the a SBAShigher technique, gradient theof correlationacceleration was (October significant 2018–January with values of2019).0.9622 The for thedeformation crest of the trend dam andfor − measurements0.9415 for the points bottom within of the damthe tailings during is the characterized rainy season, by while a linear the resultsand continuous for the PSI gradient technique of − verifiedacceleration values along of the0.9802 monitoring for the crest period. of the The dam maximum and 0.9237 accumulated for the bottom displacements of the dam. and velocity − − ratesFor were the higher dry season for PSI period, than for the SBAS. correlations An increase were of lowervelocity between for the thelast variables,40-day period with before values the of failure0.8273 was for PSIdetected and for0.9030 the forbottom SBAS of for the the dam crest with of the displacement dam. For the variations bottom of of the 10 dam, mm the(SBAS) correlation and 11 − − valuemm (PSI). was 0.6651Finally, SBAS the and deformation0.7138 for thetrend PSI. Thus,for me theasurements greatest correlations points within were observed the tailings between is − − displacementscharacterized by and a linear rainfall and during continuous the rainy gradient season. of acceleration along the monitoring period with both techniques. The negative values of the motions are indicative of the projection of the 5.displacement Discussion vector affecting the measurement point moving away from the satellite and sensitive to rangeSBAS variations and PSI along analysis to the provided, LoS, which for is each orientated measurement to WNW point, (Sentinel-1 a time-series descending of deformation mode). describingThe most the relevant behavior information of LoS displacement regarding the velocityfield monitoring over the system entire within period the of context monitoring. of this Moreresearch precisely, was published the temporal as an seriesappendix of displacements(D) of the panel´s showed report an [35]. almost It is similarrelated behaviorto the use for of the crestslope and stability bottom ground-based of the slope face, IBIS-FM with threeSAR deformationdeveloped by phases IDS givenGeoRADAR by a first [47,48], linear accelerationwhich was gradientinstalled (Marchat a distance 2018–August of the slope 2018), face a period ranging of relative from 730 stabilization to 1160 m (August (i.e., the 2018–October lower and upper 2018), part and of a higherthe dam, gradient respectively). of acceleration Despite (October the short 2018–January monitoring 2019). period The deformation (October trend2019–January for measurements 2019), a pointsdeformation within pattern the tailings was isdetected characterized using the by avery linear slow and movement continuous capacity gradient of ofthe acceleration instrument. along The results of measurements for five small areas located in the lower and middle part of the dam showed a similar trend of motions toward the sensor with displacements reaching up to 4 mm for a

Remote Sens. 2020, 12, 3664 17 of 22 the monitoring period. The maximum accumulated displacements and velocity rates were higher for PSI than for SBAS. An increase of velocity for the last 40-day period before the failure was detected for the bottom of the dam with displacement variations of 10 mm (SBAS) and 11 mm (PSI). Finally, the deformation trend for measurements points within the tailings is characterized by a linear and continuous gradient of acceleration along the monitoring period with both techniques. The negative values of the motions are indicative of the projection of the displacement vector affecting the measurement point moving away from the satellite and sensitive to range variations along to the LoS, which is orientated to WNW (Sentinel-1 descending mode). The most relevant information regarding the field monitoring system within the context of this research was published as an Appendix (D) of the panel´s report [35]. It is related to the use of the slope stability ground-based IBIS-FM SAR developed by IDS GeoRADAR [47,48], which was installed at a distance of the slope face ranging from 730 to 1160 m (i.e., the lower and upper part of the dam, respectively). Despite the short monitoring period (October 2019–January 2019), a deformation pattern was detected using the very slow movement capacity of the instrument. The results of measurements for five small areas located in the lower and middle part of the dam showed a similar trend of motions toward the sensor with displacements reaching up to 4 mm for a two-week period prior to the failure (9 to 24 January 2019). This displacement rate is out of the minimum detectable velocity for the system according to [47,48]. The numeric comparison of deformation values measured by both systems was not carried out due to various aspects (viewing geometry, spatial resolution, time-scan, etc.), and particularly taking into account that the look azimuths for the ground-based SAR (NE-looking) and Sentinel-1 (WNW-looking) are quite distinct; in addition, we had Sentinel data only in descending orbit and thus could not extract vertical and horizontal displacement. However, they are concordant regarding the presence of movements along the dam face, with an acceleration in the short period near the failure. It is worth noting that the deformations provided by Sentinel-1 with LoS range lengthening (away from the satellite) and ground-based SAR with LoS range shortening (toward the sensor), for common sectors of the dam face, are compatible with the motion components of the rupture, which were oriented to WSW. The prediction analysis of the date failure carried out with 21 measured points on the crest and on the face of Dam-I, based on SBAS and PSI results, can be interpreted as follows: the average displacement shown in Figures 11a and 12a presents a persistent increase of the displacement prior to the rupture, which was more accentuated after 17 December 2018. The analysis of the inverse of the velocity conducted with the average data predicts the failure date about 10 days and 8 days after the disaster, for SBAS for PSI analysis, respectively, as can be seen in Figures 11b and 12b. The analysis of the inverse of the velocity conducted with these 21 points in both results presents the frequency distribution of the prediction errors according to the histograms shown in Figures 13a and 14a. The reliability of the predictions appears to be quite low in both results; the distribution of the errors is visibly scattered, although in the neighborhood of 5 days from the rupture, PSI analysis presents a maximum frequency of two repetitions. The R2 distribution shown in Figures 13b and 14b presents quite good adjustments with the linear regressions, which were calculated from the inverse velocity data, with low standard deviation. Based on the prediction distribution errors found with both techniques’ results, despite the good R2 distribution in both cases, it would not have been possible to identify the date of failure with reasonable confidence.

6. Conclusions In this research, the tailings Dam-I collapse occurred on 25 January 2019 were analyzed through the use of two independent DInSAR techniques (SBAS and PSI) applied to 26 descending IW mode images acquired by Sentinel-1B from 3 March 2018 to 22 January 2019. The main purpose of this analysis was to verify evidence of ground displacements and—if detected—to characterize its spatio-temporal evolution. Both techniques that take into account different models of ground scattering mechanisms revealed Remote Sens. 2020, 12, 3664 18 of 22 signals of deformation over the slope face and tailings in the months prior to the rupture. In general, SBAS technique has provided a better spatial coverage of measurements points along the downstream slope face when compared to the PSI results. However, despite a lower density of good radar targets, the PSI technique presented higher values of accumulated displacements. In this sense, SBAS and PSI techniques can be considered as complementary, since they provided convergent results that maximized the spatial coverage and displacement detection. By analyzing the temporal series of SBAS and PSI measurements for the upper and the lower face of the dams, an almost similar deformation behavior was characterized with three distinct phases: a first phase with a continuous linear acceleration gradient (March–August 2018), with maximum rates of 50.71 and 25.25 mm/y on the top and the base of the dam, respectively for SBAS and 46.97 and − − − 63.08 mm/y on the top and the base, respectively for PSI, a steadiness phase (August–September 2018) − and a final phase with linear acceleration again (October 2018–January 2019), with maximum rates of 48.07 and 60.59 mm/y on the top and the base, respectively for SBAS and 64.49 and 65.10 mm/y − − − − on the top and the base, respectively for PSI. It can be noticed that in the third phase, the agreement between the techniques results are more accentuated. An increment of deformation velocity for the bottom part of the dam was detected from the middle of December 2018 until the last scene acquisition, three days before the rupture. The values of the maximum accumulated displacements in this period for the dam face were in the range of 30 to 39 mm (SBAS), and 42 to 48 mm (PSI). The deformation in the tailings can be attributed − − − − to settlements, and the maximum accumulated displacements during the monitoring period were 79 (SBAS) and 67 mm (PSI). All the displacement measurements were associated with negative − − values (motion away from the satellite) and are projections along the WNW oriented LoS of the 3D displacement vector affecting the slope face, whose rupture was WSW oriented. The relationship between rainfall variation and acceleration of displacements, particularly during the wet period beginning in October 2018, is consistent with the presence of water as one of the main driving factors for the instability of the structure. The predictability of the failure was also investigated through the inverse velocity method using measured points from both techniques. Despite the coefficient of determination R2 being considered good (higher than 80%) as a quality index of the regressions, the confidence of these predictions was considered low, since the errors’ distributions (before and after the rupture) were not centered near the failure time with large gaps between the time predictions and the actual failure time. Three final remarks may be derived from the results. Firstly, the Sentinel-1 nominal resolution is 4 20 m (IW-mode), and even with this relative poorer spatial resolution when compared to × commercial systems, such as TerraSAR-X (3.3 1.7 m resolution, SM mode) and COSMO-SkyMed × (3 3 m resolution, SM mode), it was enough to provide good radar targets suitable for this × particular application. Secondly, Dam-I suffered an abrupt rupture event according to the panel´s report. The low frequency of Sentinel-1 acquisition can only provide detailed motion information under a synoptic view of the surface deformation phenomena affecting the area of interest, as well as its spatio-temporal evolution. However, it is not a real-time technique such as ground-based radar, which is a well-established technology to detect the fast deformation of the escalating failure process. Even though satellite DInSAR techniques were not synergistically used with in situ observations during the monitoring period prior to the failure and the reliability of orbital predictions was low, it is reasonable to consider that Sentinel-1 would still provide decision makers with independent motion information for risk awareness and for a better understanding of ongoing instability phenomena affecting the structure. Finally, due to the large number of tailings dams in vast regions of Brazil, unmonitored or with poorly monitoring systems, the use of Sentinel-1 with suitable requirements for DinSAR applications proved to be an effective tool for monitoring and risk assessment. There exists a significant demand in Brazil for deformation information related to tailings dam situations for a number of reasons (legal obligations, safety, environmental monitoring, etc.). Sentinel-1 interferometric data can be used for the systematic tracking of ground deformation of mining structures, providing Remote Sens. 2020, 12, 3664 19 of 22 key information to make crucial decisions regarding risks, or even mitigation, repairs, or emergency response in circumstances that can lead to loss of life and damages to infrastructure and the environment. In this sense, the use of the higher frequency acquisition of the Sentinel-1 constellation (6 days revisit) improves the possibility to track motions with short time lengths.

Author Contributions: F.F.G. and J.C.M. wrote the manuscript and were in charge of SAR processing. W.R.P. wrote the manuscript and carried out the analysis of the area and the data interpretation. C.G.d.O. provided data from Pleiades 1A images. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: The authors are grateful for the support of VISIONA TECNOLOGIA ESPACIAL S.A. and AIRBUS DEFENCE & SPACE companies for providing the WorldDEM DSM and Pleiades 1A images for the research and also to Thiago Gonçalves Rodrigues for providing the Figure2. The National Council for Scientific and Technological Development (CNPq) is also acknowledged for a grant (CNPq Process: # 304091/2019-7) received by the third author during the investigation. The paper benefited significantly from the journal’s reviewers. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations The following abbreviations are used in this manuscript:

DInSAR Diﬀerential Interferometric SAR PSI Persistent Scatterer Interferometry IPTA Interferometry Point Target Analysis SBAS Small BAseline Subset MCF Minimum Cost Flow SVD Singular Value Decomposition LoS Line-of-Sight

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