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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
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Comparative study between the topographic method (classical) and photogrammetric method (by drone) for the monitoring and data acquisition of mining works: Case of the SOMAÏR uranium mine (Arlit, North Niger)


Volume 38, Issue 3, January 2023, Pages 695–709

 Comparative study between the topographic method (classical) and photogrammetric method (by drone) for the monitoring and data acquisition of mining works: Case of the SOMAÏR uranium mine (Arlit, North Niger)

Ibrahim Elhadji Daou1, Souley Harouna2, Sanda Chékaraou Mahamane Moustapha3, Abdourazakou Maman Hassan4, Ali Hamani Amadou5, and Bintou Mandata Zabre6

1 Department of Mining Engineering and Environment, School of Mines, Industry and Geology of Niamey (EMIG), Niger
2 Department of Geosciences, School of Mines, Industry and Geology of Niamey (EMIG), Niger
3 Department of Geology, University of Agadez, Niger
4 Department of Geosciences, School of Mines, Industry and Geology of Niamey (EMIG), Niger
5 Department of Mining Engineering and Environment, School of Mines, Industry and Geology of Niamey (EMIG), Niger
6 Department of Mining Engineering and Environment, School of Mines, Industry and Geology of Niamey (EMIG), Niger

Original language: English

Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


The SOMAÏR open-pit uranium mine, commonly known as « Société des Mines de l’Aïr » (Arlit, Northern Niger), has been using the topographic method for several years to monitor and estimate mine production. However, the method has limitations and constraints in the implementation and reliability of the results. The company is considering the use of an innovative, more reliable and economical method. Thus, a pilot project using drones is being implemented. The objective of this work is to carry out a comparative study between the topographic method and the photogrammetric method for monitoring and acquiring data from mining operations. Thus, the data acquired by topometry using a total station, for the so-called classical method and by drone for the photogrammetric method, were analyzed and interpreted. These two (2) methods were used for the follow-up of the M4_Art North ore deposit and the G4_Taossa pit of the SOMAÏR mine. The results of the analysis and processing show that the data acquisition time by drone is relatively low (30 to 40 minutes) compared to that of the topographic surveys (21 to 60 minutes). However, data processing times for the photogrammetric method are relatively higher (50 to 60 minutes) than those for the conventional method (14 to 20 minutes). Nevertheless, this processing time of drone images can be improved with powerful computer equipment. In addition, the use of UAVs offers additional advantages in the monitoring of mining operations, particularly with regard to worker safety, precision in the calculation of dimensions, volumes and tonnages at the mining slice and at the overburden. Immediate analysis of the two methods shows the accuracy of the drone for the front survey and also shows all the details present on the ground, namely: the machines used, the purging products and other products or elements used. So, it would be wise to opt for the drone in downhole activities.

Author Keywords: SOMAÏR, topographic method, photogrammetric method, mine pit, ore pouring.


How to Cite this Article


Ibrahim Elhadji Daou, Souley Harouna, Sanda Chékaraou Mahamane Moustapha, Abdourazakou Maman Hassan, Ali Hamani Amadou, and Bintou Mandata Zabre, “Comparative study between the topographic method (classical) and photogrammetric method (by drone) for the monitoring and data acquisition of mining works: Case of the SOMAÏR uranium mine (Arlit, North Niger),” International Journal of Innovation and Applied Studies, vol. 38, no. 3, pp. 695–709, January 2023.