Remote sensing supported surveillance of tailings dams and impoundments: case Laiva mine

Transcription

1 Remote sensing supported surveillance of tailings dams and impoundments: case Laiva mine Anssi Rauhala, Anne Tuomela, Pekka M. Rossi Water Resources and Environmental Engineering, University of Oulu, Finland, Corine Davids Norut Northern Research Institute, Norway

2 Background We had an opportunity to perform measurements at the Laiva gold mine site and tailings impoundment. Located close to Raahe, around 65 km northwest from Oulu and 165 km northeast from Luleå. Mining began Q3/2011, but production has been on halt since Q1/2014 due to decline in gold prices. Current owner plans to re-open August

3 Background The impoundment is relatively small (~ 0.5 km 2 ) with tailings deposit thickness ranging from couple meters up to 10 m. Laiva mine utilizes paste technique in tailings deposition (dewatered to promote steeper deposition slope). However, fairly little research is available on the effects of cold climate on dewatered, thickened tailings. 3

4 Background Steeper deposition angles and cold climate with seasonal freezethaw effects can lead to for example stability issues Our aim was to gain knowledge on tailings behavior at cold climate and explore the applicability of Unmanned Aerial Vehicles (UAVs) in monitoring. 4

5 Field and laboratory measurements Field and laboratory testing was performed to characterize in situ conditions and tailings properties In situ: temperature loggers, frost depth pipes. Laboratory: particle-size analysis, soil suction tests, frost heave tests, rheometry. Both field and laboratory testing has produced wealth of information which gives general knowledge on the situation and can be utilized for example in modelling. 5

6 UAV measurements Fixed-wing UAVs and Structure from Motion (SfM) technique were utilized to produce orthomosaics and digital elevation models (DEMs). Surveys were performed during summers 2015, 2016, and 2017 to assess if UAVs are suitable for monitoring tailings settlement. The utilized equipment changed for each summer, so two surveys were performed during 2017 in order to assess the accuracy and reproducibility of the method and 2016 surveys operated by Norut June/August surveys operated by Maailmasta Oy 6

7 7 Structure from Motion

8 8 Structure from Motion

9 Structure from Motion E.g. during the June 2017 campaign, 894 images were taken. Black dots highlight the camera/plane location during the acquisition. Colors highlight the number of images covering each portion of the images. 9

10 10 Structure from Motion

11 Structure from Motion E.g. during the June 2017 campaign, 20 Ground Control Points were installed and their coordinates were measured with RTK GNSS. Colors and shapes of the points highlight the difference between the input GCPs and their location estimated from the images. 11

12 Structure from Motion Model produced with Agisoft Photoscan: 12

13 Surveys During the two 2017 surveys, the aim was to keep relevant parameters fixed in order to assess reproducibility of the measurements. 13

14 Surveys Total of 189 RTK GNSS checkpoints were collected on the road and dams surrounding the impoundment. These checkpoints were used in estimating data quality. 14

15 15 Results: DEMs

16 Results: DEMs Ground resolution improved (flight height, camera quality). 16

17 Results: DEMs Ground resolution improved (flight height, camera quality). Mean error (ME) compared to 189 checkpoints gives some indication of global misalignment after data co-registration. 17

18 Results: DEMs Ground resolution improved (flight height, camera quality). Mean error (ME) compared to 189 checkpoints gives some indication of global misalignment after data co-registration. Mean absolute error (MAE) and the other measures highlight the internal accuracy of different DEMs. 18

19 Results: DEMs Comparison between digital elevation models allows to track changes between different years. Can generate tailings subsidence maps. Stable service road surrounding the impoundment provides a baseline for accuracy assessment. 19

20 Results: Subsidence For the stable service road, the distribution of point to point differences is centered around 0 cm with middle 99% of values between -33 cm and 31 cm (stdev 11 cm). Service road Tailings surface 20

21 Results: Subsidence For the stable service road, the distribution of point to point differences is centered around 0 cm with middle 99% of values between -33 cm and 31 cm (stdev 11 cm). For the tailings surface, the point to point differences are centered around 29 cm with middle 99% between 3 cm and 68 cm (stdev 13 cm). Service road Tailings surface 21

22 Results: Practical accuracy 6-8/2017 Between June-August 2017, the differences in the road are centered around -1 cm with noticeable secondary peak around -12 cm. - Secondary peak caused by cumulative error, seen in northern side of the impoundment. Service road Tailings surface 22

23 Results: Practical accuracy 6-8/2017 Between June-August 2017, the differences in the road are centered around -1 cm with noticeable secondary peak around -12 cm. - Secondary peak caused by cumulative error, seen in northern side of the impoundment. 23 On the tailings surface, the distribution is centered around 0 cm with middle 99% of the values between -12 cm and 13 cm (stdev 4.7 cm). Service road Tailings surface

24 Conclusions The accuracy of the DEMs improved considerably with improved camera quality and ground control network. Comparison of the 2015 and 2017 DEMs indicated general trend of subsidence (mean 29.4 cm) in the tailings surface. - Cannot tell what part of the subsidence is from tailings settlement, what part from the compaction of the underlying peat layer, etc. Comparison of the two 2017 DEMs indicated that the practical accuracy limit was around 5-10 cm. As such, the method can be used to track displacements in the decimeter range. - Enhancing the ground resolution even further (e.g., lowering the flight altitude) might allow measurement of even smaller displacements. 24

25 Conclusions Results from the UAV campaign were published in Remote Sensing journal s special issue: The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring - Open access: Wealth of laboratory and field data was produced in addition to UAV measurements and has been utilized in modelling. - Geothermal behaviour (R. Knuttson) - Geomechanical behaviour (R. Tolonen) 25

26 THANK YOU! Open access article: Rauhala A. et al. 2017, UAV Remote Sensing Surveillance of a Mine Tailings Impoundment in Sub-Arctic Conditions, Remote Sensing 9(12),