Natural Hazards

, 59:1047 | Cite as

Persistent scatterer interferometry analysis of the embankment failure of a red mud reservoir using ENVISAT ASAR data

  • Gyula GrenerczyEmail author
  • Urs Wegmüller
Original Paper


On October 4, 2010, the embankment of No. 10 red mud—waste product during bauxite refining—reservoir of MAL Co. Ltd. alumina plant collapsed. Around 700 000 cubic meter of alkaline slurry with 11–13 pH value flooded three nearby settlements. Ten people were killed, 123 wounded, and about 350 houses were damaged or became uninhabitable in one of the biggest industrial and ecological catastrophe in modern history of Hungary. Synthetic aperture radar interferometry (InSAR) is the only geodetic technique that can reveal the past stability and motion history of the broken reservoir. We performed a persistent scatterer InSAR (PSI) analysis to contribute to the better understanding of the disaster. The key question we address in this study is whether there were signs prior to October 4th event which may have prognosticated the disaster. The PSI results could reveal if the dam was stable in the past without prior signs of the failure or there were definite indications of deformation before the collapse and proper monitoring of motions could have highlighted the risk and the disaster may have been prevented. This is a rare event and its PSI study is unique and may be critical in understanding it and helping to prevent other similar occurrences.


InSAR Remote sensing Space geodesy Dam failure Reservoir 



We are indebted to the European Space Agency for providing us the requested datasets.

Supplementary material

11069_2011_9816_MOESM1_ESM.docx (17 kb)
Supplementary material 1 (DOCX 16 kb)


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Satellite Geodetic ObservatoryInstitute of Geodesy Cartography and Remote SensingBudapestHungary
  2. 2.Research Group for Physical Geodesy and GeodynamicsHungarian Academy of SciencesBudapestHungary
  3. 3.Gamma Remote SensingGümligenSwitzerland

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