13 February 2019
Pre-failure movement analysis of the Su Village (Sucun) landslide in China
Back in April 2018 I wrote about an analysis of the Su Village (also known as Sucun) landslide in Zhejiang Province, China in 2016. This landslide, which was caught on video, killed 27 people. The analysis in Landslides published last year (Ouyang et al. 2018) indicated that some pre-failure deformation of the landslide had been detected. A new paper, also published in Landslides (Ouyang et al. 2019), examines this aspect of the landslide in more detail, considering in particular the degree to which pre-failure deformation could be detected using satellite imagery.
The paper includes the image below of the landslide, showing clearly the source zone for the failure (marked with the green arrows), the landslide track (yellow) and the deposition zone (in green again). The area in which the landslide impacted upon the village can be seen in the lower left corner of the image:-
The authors used Sentinel-1A SAR (radar) imagery to investigate pre-failure deformation of the Su Village landslide, based upon an analysis of the 36 images using Persistent Scatterer Interferometry. The image below shows the points that were analysed across the landslide and its adjacent slopes, with the colours indicating the magnitude of the movement detected:-
My interpretation of the is that the yellow and green dots show no movement beyond error in the technique, whereas the orange and red dots show detectable deformation. The failure initiated in the source area shown in the first image, which then caused the rest of the slope to collapse, so it is reassuring that the detectable pre-failure deformation is situated high in the slope. The highest rates are in the section of the slope that collapsed, close to the crown of the landslide. But note also that significant deformation is seen in adjacent slopes that, at the time of analysis, had not failed. Thus, the InSAR results do not give a spatial indication of the likely form of the final failure (whilst also suggesting that there may be other hazardous areas on this slope).
The displacement – time series graph also shows the results of this analysis, based on the points shown in the image above:-
There is a mixed picture here. The good news is that statistically significant movement was detected in the areas that failed, whilst the unfailed areas displayed no trend (with some noise). But the detected movement is linear with time in all three points with above-error deformation, meaning that no inferences could be drawn to indicate time of failure. Thus, whilst the analysis shows that the slope was creeping, it could not be used as a warning system.
This paper is another very useful contribution towards the use of InSAR data for rock slope failure detection in natural slopes. But, once again, it shows that this is a very complex and challenging problem, and that our satellite based systems do not yet have the maturity to be used operationally in many cases as yet.
Ouyang, C., Zhao, W., Xu, Q. et al. 2018. Failure mechanisms and characteristics of the 2016 catastrophic rockslide at Su village, Lishui, China. Landslides. https://doi.org/10.1007/s10346-018-0985-1
Ouyang, C., Zhao, W., An, H. et al. 2018. Early identification and dynamic processes of ridge-top rockslides: implications from the Su Village landslide in Suichang County, Zhejiang Province, China. Landslides. https://doi.org/10.1007/s10346-018-01128-w.