Authors :
Lawrence Sarpong; Abass Aliu
Volume/Issue :
Volume 11 - 2026, Issue 8 - August
Google Scholar :
https://tinyurl.com/488kdccn
Scribd :
https://tinyurl.com/4bm9yrek
DOI :
https://doi.org/10.38124/ijisrt/26aug190
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Tailings storage facilities (TSFs) pose significant geotechnical and environmental risks that requires advanced and
reliable monitoring systems capable of detecting early signs instability. This systematic review synthesizes evidence from 26
studies published between 2020 and 2026 to evaluate recent advances in geotechnical monitoring technologies for tailings
storage facilities (TSFs), with a particular focus on their application within the United States. Five major technology
categories were identified: satellite InSAR (Sentinel-1, SBAS-InSAR), UAV-based imaging and photogrammetry, distributed
and point sensors (DAS, MEMS, ERT), AI and machine learning for predictive analytics, and integrated visualization early
warning systems. Satellite InSAR provides millimeter-scale deformation monitoring at 6-12-day intervals but cannot capture
subsurface changes. UAV imaging achieves sub-centimeter erosion mapping but is weather-dependent. DAS and ERT offer
high-resolution internal monitoring but require permanent installations. AI classifiers have demonstrated >85% accuracy
for hazard prediction, yet require large labeled datasets and face interpretability challenges. U.S.-specific adoption remains
limited due to economic barriers, regulatory fragmentation, and workforce expertise gaps, despite climate-induced stressors
that increase failure risks. The review concludes that no single monitoring technology can adequately address all TSF safety
challenges. Future priorities include prospective validation of integrated systems at U.S. TSFs, development of interpretable
AI models, shared industry data repositories, and performance-based regulatory frameworks. Without deliberate
investment, U.S. TSFs will remain vulnerable to preventable failures.
Keywords :
Tailings storage facilities; geotechnical monitoring; Interferometric Synthetic Aperture Radar (InSAR); Unmanned Aerial Vehicle (UAV); Technology
References :
- Acharya, P., Beier, N. A., & Liu, F. (2024). Cloud-based geotechnical monitoring for tailings storage facilities using Sentinel remote sensing images. Remote Sensing Applications: Society and Environment.
- Armah, A., Whajah, J., & Annankra, J. A. (2025). The geomechanical behavior of mine waste rock slopes under climate-induced stressors: A global perspective. Journal of Engineering and Computer Sciences, 4(9), 454–466.
- Arthur, A. A., & Annankra, J. A. (2025). Investigating advanced technologies to enhance worker safety in hazardous mining environments: A review.
- Arthur, A. A., Annankra, J. A., & Yakin, Z. (2025). Examining the role of AI and machine learning in improving hazard detection and predictive analytics for accident prevention in mining operations. World Journal of Advanced Engineering Technology and Sciences, 15(3), 640–646.
- Atif, I., Ashraf, H., Cawood, F. T., & Mahboob, M. A. (2020). A conceptual digital framework for near real-time monitoring and management of mine tailings storage facilities. In Proceedings of the International Conference on Mining Engineering (pp. 245–256). Springer. https://doi.org/10.1007/978-3-030-60839-2_27
- Barzegar, M., Blanks, S., Sainsbury, B. A., Ranjith, P. G., & Haque, A. (2022). MEMS technology and applications in geotechnical monitoring: A review. Measurement Science and Technology, 33(4), 042002. https://doi.org/10.1088/1361-6501/ac4f00
- Cacciuttolo, C., Guzmán, V., Catriñir, P., Atencio, E., Valderrama, P., & Pastor, A. (2023). Low-cost sensor technologies for monitoring sustainability and safety issues in mining activities: Advances, gaps, and future directions. Sensors, 23(15), 6846. https://doi.org/10.3390/s23156846
- Clarkson, L., & Williams, D. (2021). Catalogue of real-time instrumentation and monitoring techniques for tailings dams. Mining Technology, 130(2), 87–104. https://doi.org/10.1080/25726668.2021.1874094
- Dimech, A., Bussière, B., Cheng, L. Z., Aubertin, M., & Chapuis, R. P. (2024). Monitoring moisture dynamics in mine tailings cover systems using time-lapse electrical resistivity tomography. Canadian Geotechnical Journal, 61(5), 735–750. https://doi.org/10.1139/cgj-2023-0112
- Djanetey, G. E., & Yakin, Z. (2025). Ground control systems for deep underground mining: Advancing safety and resource recovery in high-stress mining environments. World Journal of Advanced Research and Reviews, 27(1), 2134–2142.
- Fetell, R. H. (2024). Comparison of finite element methods and satellite InSAR for monitoring deformations of a large tailings dam (Doctoral dissertation, University of Nevada). ProQuest Dissertations Publishing.
- Ghahramanieisalou, M., Sattarvand, J., Gomez, J. A., & Nasategay, F. (2023). Tackling geotechnical risks in tailings dams using high-resolution UAV imaging and advanced image processing. Journal of Geotechnical Monitoring, 12(3), 211–228.
- Koperska, W., Stefaniak, P., Stachowiak, M., Anufriiev, S., Jachnik, B., & Kaczmarek, Ł. (2024). Spatial and temporal analysis of surface displacements for tailings storage facility stability assessment. Applied Sciences, 14(22), 10715. https://doi.org/10.3390/app142210715
- Le Borgne, V., Siamaki, A., Smith, I., & Brown, T. (2022). Review of modern recommendations for monitoring of tailings storage facilities. In Proceedings of the Tailings and Mine Waste Conference.
- Lumbroso, D., Collell, M. R., Petkovšek, G., Goff, C., & McElroy, C. (2021). DAMSAT: An eye in the sky for monitoring tailings dams. Mine Water and the Environment, 40(1), 35–50. https://doi.org/10.1007/s10230-020-00727-1
- Nasategay, F. F. U. (2020). Detection and monitoring of tailings dam surface erosion using UAV and machine learning (Doctoral dissertation, University of Arizona). ProQuest Dissertations Publishing.
- Nie, W., Luo, M., Wang, Y., & Li, R. (2022). 3D visualization monitoring and early warning system of a tailings dam: Case study of Zijinshan gold-copper mine. Frontiers in Earth Science, 10, 800924. https://doi.org/10.3389/feart.2022.800924
- Opara, S. U., Aliu, A., Oduro, A. A., & Anyomi, L. E. (2026). Geological risk assessment for carbon storage integrity in depleted U.S. oil and gas reservoirs. EPRA International Journal of Research and Development (IJRD), 11(3). https://doi.org/10.36713/epra26774
- Ouellet, S. (2024). Advancing tailings dam performance monitoring with distributed acoustic sensing (Master’s thesis, University of Calgary).
- Ouellet, S. M., Dettmer, J., Olivier, G., DeWit, T., Mikesell, T. D., & Lato, M. (2022). Advanced monitoring of tailings dam performance using seismic noise and stress models. Communications Earth & Environment, 3, 289. https://doi.org/10.1038/s43247-022-00629-w
- Ouellet, S., Dettmer, J., Mikesell, T. D., Lato, M., Olivier, G., & DeWit, T. (2025). Tailings dam performance monitoring by combining coda wave interferometry with distributed acoustic sensing. Journal of Geotechnical and Geoenvironmental Engineering, 151(3), 04025012. https://doi.org/10.1061/JGGEFK.GTENG-13066
- Quansah, E. A., & Aliu, A. (2025). Review of the discrepancy between feasibility-stage financial models and actual mine economic outcomes. Sarcouncil Journal of Multidisciplinary, 5(12), 27–35.
- Quansah, E. A., & Yakin, Z. (2025). Probabilistic and stochastic mine planning: A review of methods for managing mine project uncertainties. Journal of Engineering and Computer Sciences, 4(12), 39–46.
- Rana, N. M., Delaney, K. B., Evans, S. G., Deane, E., & McDougall, S. (2024). Application of Sentinel-1 InSAR to monitor tailings dams and predict geotechnical instability. Bulletin of Engineering Geology and the Environment, 83, 45. https://doi.org/10.1007/s10064-024-03680-3
- Xie, W., Wu, J., Gao, H., Chen, J., & He, Y. (2023). SBAS-InSAR-based deformation monitoring of a tailings dam: A case study. Sensors, 23(24), 9707. https://doi.org/10.3390/s23249707
- Yator, K. Y., & Aliu, A. (2026). Advanced geospatial and computational analytics for predicting subsidence and slope failure in U.S. mining regions.
Tailings storage facilities (TSFs) pose significant geotechnical and environmental risks that requires advanced and
reliable monitoring systems capable of detecting early signs instability. This systematic review synthesizes evidence from 26
studies published between 2020 and 2026 to evaluate recent advances in geotechnical monitoring technologies for tailings
storage facilities (TSFs), with a particular focus on their application within the United States. Five major technology
categories were identified: satellite InSAR (Sentinel-1, SBAS-InSAR), UAV-based imaging and photogrammetry, distributed
and point sensors (DAS, MEMS, ERT), AI and machine learning for predictive analytics, and integrated visualization early
warning systems. Satellite InSAR provides millimeter-scale deformation monitoring at 6-12-day intervals but cannot capture
subsurface changes. UAV imaging achieves sub-centimeter erosion mapping but is weather-dependent. DAS and ERT offer
high-resolution internal monitoring but require permanent installations. AI classifiers have demonstrated >85% accuracy
for hazard prediction, yet require large labeled datasets and face interpretability challenges. U.S.-specific adoption remains
limited due to economic barriers, regulatory fragmentation, and workforce expertise gaps, despite climate-induced stressors
that increase failure risks. The review concludes that no single monitoring technology can adequately address all TSF safety
challenges. Future priorities include prospective validation of integrated systems at U.S. TSFs, development of interpretable
AI models, shared industry data repositories, and performance-based regulatory frameworks. Without deliberate
investment, U.S. TSFs will remain vulnerable to preventable failures.
Keywords :
Tailings storage facilities; geotechnical monitoring; Interferometric Synthetic Aperture Radar (InSAR); Unmanned Aerial Vehicle (UAV); Technology