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ENVI Inform in Action: Monitoring Tailings Dams

Nate Oscar

Problem:

Mining for minerals creates large quantities of hazardous waste, called tailings. These are stored in tailings ponds that are retained through the construction of a dam near the mine site. On a number of occasions, tailings dams have suffered high profile failures, leading to loss of life and causing large scale environmental impact. For example, in 2019, a tailings dam at the Brumadinho mine in Brazil failed, resulting in the loss of 270 lives and the release of 12 million cubic meters of tailings[1]. In addition to the wide-spread devastation and ecological impact, the mining company was fined an estimated $17 billion.

Monitoring is complicated by the fact that there are so many dams often located in remote areas, making them difficult and costly to survey. With tailing dams living in perpetuity and operators facing increased operational pressures, many countries around the world are introducing legislation to force mining companies to monitor tailings dams for potential issues to ensure the safety to communities and the environment surrounding these facilities. Notable legislation includes the global tailings standard which was released on August 5, 2020, and is supported by a coalition of investors with over $24 trillion under its management.

 

Solution

Research has shown that tailings dams exhibit distinctive instability prior to failure. This provides an opportunity for warning and intervention. Satellite monitoring of ground movement using Synthetic Aperture Radar (SAR) and advanced processing can detect instability and provide stakeholders with early-warning signs.

Satellite Applications Catapult (SAC), a not-for-profit, innovation and technology organization transforming the way the world uses satellite technologies, partnered with NV5 Geospatial as part of its work with the Church of England Pension Board, to show that space-based data could be used to create a ground-motion monitoring service that detects instability around mine tailings dams.

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ENVI Inform automatically monitors an area by processing and analyzing large volumes of data, extracting actionable information, and delivering it through a dashboard customized for each customer.

SAC employed ENVI® Inform to generate a historical baseline of the motion around each tailings dam, typically based on the previous three years. To do this, the system retrieved SAR data from an online archive and processed it using ENVI® SARscape®, the leading SAR processing and analysis software.

As new satellite data is made available, ENVI Inform automatically retrieves, processes, and analyzes the data, to provide an up-to-date assessment of ground displacement. The results are published to the customer’s GIS system and analyzed using web-based dashboards. By comparing new information against the historical baseline, stakeholders can assess if new movement is a normal occurrence or a cause for concern. This information enables surveyors to prioritize on-site surveys and perform other mitigation efforts.

Results

This showed how ENVI Inform can successfully be used to create a monitoring service to automatically process and analyze SAR data and identify sub-centimeter ground motion around 50 mine tailings dams globally. The scalable approach using ENVI Inform allows for dramactically scaling to automatically monitor many more tailings dams worldwide.

The benefit of ENVI Inform is that once an area of interest (AOI) is identified, information can be continuously updated in near-real time. This greatly impacts an organization's ability to monitor infrastructure by condensing the timeframe from data retrieval to delivering actionable intelligence to decision makers and teams in the field. This type of automated, scalable service is useful for monitoring a wide variety of asset types including highways, bridges, tunnels, subways, railways, buildings, and more.

Learn more about ENVI Inform and see if an automated monitoring service would help your mission.

 

[1] “Brumadinho dam disaster” page from Wikipedia.

 

 

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