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Q&A: Can we predict tailings storage facility failures?

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Mining & Resources / Tailings Storage Facilities

Q&A: Can we predict tailings storage facility failures?

Highlights

  • Lead author of a study collaboration between the University of Nottingham and Durham University, Stephen Grebby speaks to Mining Technology, about how the Brumadinho tailings dam collapse could have been foreseen weeks in advance with different monitoring technology.
  • Learn about Decipher’s InSAR module
  • K2fly acquires Decipher’s Mining solutions business

 

A new study has suggested that the Brumadinho dam collapse could have been foreseen weeks in advance with different monitoring technology. Matthew Hall (Mining Technology) speaks to Stephen Grebby, the lead author on the study, about what this could mean for the industry.

A collaboration between the University of Nottingham and Durham University has found that it may be possible to predict a dam burst by using satellite radar imaging to monitor for small ground movements in and around dams.

The study used satellite imagery to analyse the Brumadinho dam in the lead up to its collapse. One of Brazil’s worst environmental disasters, the January 2019 collapse of a tailings dam at Vale’s Córrego do Feijão iron ore mine in Brazil killed 270 people. The incident, which followed another Vale-owned dam collapse in 2015, brought new attention to the safety and monitoring processes of tailings dams.

Find out about the 57 major tailings dam failures (2000 – 2020)

Matthew Hall: What are the current methods used to monitor the stability of dams, and how effective are they?

Stephen Grebby: Current standard monitoring techniques typically include an array of ground-based sensors and monitoring techniques, such as traditional surveying, ground-based radar, and inclinometers for measuring movement of the dam, and piezometers for measuring the water level and water pressure within the tailings.

Although these techniques provide crucial information on the stability of dams, they only really provide measurements at several specific locations on the dam and tailings, which may make it difficult to spot signs of distress.

 

MH: Can you explain the method you used to show the Brumadinho dam collapse was foreseeable?

SG: We applied a two-stage approach to investigating the Brumadinho dam failure: first, we extracted data on the movements within the dam and tailings structure, then we analysed this data to determine whether there was any anomalous precursory motion that is indicative of a potential failure.

In the first stage, we applied a technique called Interferometric Synthetic Aperture Radar (InSAR) to satellite data acquired by the Sentinel-1 mission, in order to map how the entire dam and tailings structure was moving during the 17 months preceding the collapse.

InSAR - Decipher Tailings Monitoring - Wesfarmers
Decipher’s InSAR module

Learn more about Decipher’s InSAR module

By studying the observed movement, we found that different parts of the Brumadinho dam were moving at different rates, some of which accelerated during the two months prior to collapse. Analysing the velocities at which these areas were moving and how they changed over time then enabled us to predict the possible time of failure.

In this case, we found that if the dam was being systematically monitored using our technique, the failure date could have been predicted to within a week of it happening. Crucially, this prediction would have been possible around 40 days prior to the collapse, allowing time for a warning to be raised that the dam was becoming unstable.

 

MH: Why has this method not previously seen widespread use in the mining industry?

SG: The general use of InSAR for monitoring ground motion is not new, although the application to monitoring dams is relatively new, having only really emerged about five or so years ago. There are some mining companies that do indeed make use of this technology in their monitoring, however, there are a couple of issues that are likely responsible for limiting its wider uptake.

Firstly, the satellite data has to be processed using sophisticated computer algorithms in order to obtain ground motion data. Mining companies wouldn’t typically have the in-house capabilities to do this, so they would need to obtain the processed ground motion data from specialists InSAR data providers. Once they have the ground motion data, the next challenge is then knowing how to interpret the observed motion in terms of the risk of failure.

 

MH: What were the tell-tale signs in the weeks before the Brumadinho collapse that the dam was at risk?

SG: It is not uncommon for tailings dams to move as they compact and consolidate after new material is added, or when the material swells and shrinks as it dries out following contact with moisture. This motion tends to decrease with time as the tailings stabilise, and it shouldn’t accelerate as that’s a sign that the dam is becoming unstable.

However, our InSAR-derived ground motion data revealed that some parts of the dam and tailings began accelerating during the two months preceding the collapse, coinciding with a period of increased rainfall. We identified this accelerated motion as a precursor to the dam collapse.

 

MH: Is this method applicable to monitoring all tailings dams, or are there restrictions on where it might be effective?

SG: In theory, it is possible to implement this globally because satellites such as Sentinel-1 are acquiring the necessary radar data at least every 12 days all over the world. However, there a few specific circumstances where its use could be limited.

Firstly, in some cases, the terrain may obscure what can be seen by the satellite – for example, a steep-sided slope may cast a shadow that obscures part of a mine – but this can often be mitigated against if there is data from another satellite track with a different viewing perspective.

Secondly, the presence of water on the surface of the tailings beach can limit the number and spatial distribution of measurements that can be obtained across the dam structure. Finally, dams that undergo very sudden collapses following rapid rates of movement can be somewhat challenging to monitor, depending on how often the data is acquired by the satellite.

 

MH: What are your hopes with this new study – is this a method that could see adoption soon in the industry to prevent future dam collapses?

SG: It’s clear that this InSAR approach provides complementary information to that which can be obtained using the standard ground-based sensors and monitoring techniques. We therefore hope that the mining sector views this technique as a valuable addition to the monitoring toolbox to help avert similar disasters in the future.

I think the key to increasing the uptake is to try to provide the necessary information in a form that is user-friendly, perhaps as some form of automated early warning service that can predicts the risk of imminent collapse. This is something that our team is now turning our attention to.

Originally published by Mining Technology. 

 


Monitor, measure and detect deformation of land surfaces over time with Decipher

Module - InSAR (1)

 

 

Access our easy to use cloud solution so you can monitor, measure and detect deformation of land surfaces over time and be automatically alerted to customer defined exceedances to a precision of 1-2 millimetres.

Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique used to detect, monitor and measure the magnitude of deformation in land surfaces over time utilising Synthetic Aperture Rader (SAR) imagery. This imagery can be sourced from radar equipped satellites at regular intervals with active sensors capable of seeing through rain & cloud at any time of the day or night with millimetre accurate changes.

Understanding the extent and magnitude of such movement such as landforms created by erosion, helps to identify and manage risk of potential failures and provide assurance that the area is safe and stable. Where ground based monitoring is not available, InSAR provides a means of remotely monitoring landform features such as stockpiles, landfill, infrastructure, urban and tailings facilities.

Working with 3VG to utilise radar signals from earth-orbiting satellites, Decipher provides a way to visualise displacement (cm) on an interactive map to enable users to identify specific areas of deformation across a broad range of interest. Decipher analysis tools also allow users to then chart the details along a transect line or a single point over time. This also includes the ability to view historical imagery to show areas of deformation.

 

Want to see more? Experience an interactive demo of our Tailings Monitoring & Governance solution today

 

 

FAQ:

What are the current methods used to monitor the stability of dams, and how effective are they?

According to Stephen Grebby, Current standard monitoring techniques typically include an array of ground-based sensors and monitoring techniques, such as traditional surveying, ground-based radar, and inclinometers for measuring movement of the dam, and piezometers for measuring the water level and water pressure within the tailings.

How could InSAR help detect the Brumadinho tailings dam collapse?

According to Stephen Grebby, they applied InSAR to satellite data acquired by the Sentinel-1 mission, in order to map how the entire dam and tailings structure was moving during the 17 months preceding the collapse. By studying the observed movement, they found that different parts of the Brumadinho dam were moving at different rates, some of which accelerated during the two months prior to collapse. Analysing the velocities at which these areas were moving and how they changed over time then enabled them to predict the possible time of failure. In this case, they found that if the dam was being systematically monitored using this technique, the failure date could have been predicted to within a week of it happening. Crucially, this prediction would have been possible around 40 days prior to the collapse, allowing time for a warning to be raised that the dam was becoming unstable.

 


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