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On 29 May 2020, a fuel tank in the Russian Arctic town of Norilsk lost pressure and released 21,000 tonnes of diesel into rivers and subsoil.  Greenpeace has compared the disaster to the devastating Exxon Valdez oil spill off Alaska in 1989 and estimated that the environmental damage will cost $1.4 billion.


It is believed that the tank collapsed due to a sudden failure in the supporting piles due to melting of the permafrost although there have also been concerns regarding the state of the equipment used for storage.


Terra Motion Limited were asked to investigate subsidence around the tank in the period leading up to the collapse using our APSIS© method.  APSIS© was selected as the investigation needed to ascertain if there were any signs of permafrost collapse in the surrounding area that may have helped predict the disaster.


We surveyed the site using freely available Sentinel-1 satellite radar data and the results were provided within one week of the request from the client.  Due to the latitude of the location, we did not use data from the winter period (November-March) as there is very little motion of note during that period of the year.  An animation of the motion is shown below.


As may be seen, there is very little of note until the beginning of the 2019 period when subsidence (red) can clearly be detected on the tank (image centre) but also in the open area just to its left.  Certainly from 2019 onwards, the incidence of subsidence across the entire area intensifies which may be a result of permafrost melt.  Both areas around and on the tank intensify and merge in the early 2020 period which could suggest that they may have the same cause.


Due to the novel ability of APSIS© to determine the motion of both infrastructure and the surrounding soils, along with the wider environment, it can form an important tool in determining if the state of ground motion surrounding the collapse could provide any evidence of the cause or precursors to the disaster.  Also, of utmost importance is the archive of Sentinel-1 data which allows at least a four-year retrospective analysis of any disaster anywhere in the world.

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