Public remotely sensed data raise concerns about history of failed Jagersfontein dam

Torres-Cruz, Luis Alberto; O'Donovan, Christopher

doi:10.1038/s41598-023-31633-5

Cited by 9 publications

(1 citation statement)

References 14 publications

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“…Due to mining being one of the oldest industries, over 30,000 TSFs are estimated to exist worldwide, a quarter of which are abandoned and not monitored at all [3]. Even relatively well-monitored TSFs can sometimes fail, resulting in socio-environmental tragedies such as the Brumadinho failure in 2019 [4] and, most recently, the Jagersfontein failure in 2022 [5]. Unfortunately, the number of catastrophic TSF failures is increasing [6].…”

Section: Introductionmentioning

confidence: 99%

Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure

Bayaraa,

Rossi,

Kalaitzis

et al. 2023

Remote Sensing

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There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards this ambition. However, extracting meaningful information and interpreting the deformation patterns from InSAR data can be a challenging task. One approach to address this challenge is through the use of data science techniques. In this study, the representation of InSAR metadata as Entity Embeddings within a Deep Learning framework (EE-DL) is investigated for modelling the spatio-temporal deformation response. Entity embeddings are commonly used in natural-language-processing tasks. They represent discrete objects, such as words, as continuous, low-dimensional vectors that can be manipulated mathematically. We demonstrate that EE-DL can be used to predict anomalous patterns in the InSAR time series. To evaluate the performance of the EE-DL approach in SAR interferometry, we conducted experiments over a mining test site (Cadia, Australia), which has been subject to a TSF failure. This study demonstrated that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compared the results with deformation predictions from common baseline models, the Random Forest model and Gaussian Process Regression (GPR). Both EE-DL and GPR greatly outperform Random Forest. While GPR is also able to predict displacement patterns with millimetric accuracy, it detects a significantly lower number of anomalies compared to EE-DL. Overall, our study showed that EE-DL is a promising approach for building early-warning systems for critical infrastructures that use InSAR to predict ground deformations.

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Section: Introductionmentioning

confidence: 99%

Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure

Bayaraa,

Rossi,

Kalaitzis

et al. 2023

Remote Sensing

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Perspectivas sobre la recopilación y evaluación crítica de las características de rotura y desbordamiento históricos en presas de estériles: Implicaciones en la modelización numérica

Adria,

Ghahramani,

Rana

et al. 2023

Mine Water Environ

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Numerical models are used for detailed and site-specific tailings dam breach analyses (TDBAs) to estimate the downstream inundation and deposition resulting from a potential breach at a tailings dam. The results of TDBAs are key inputs into risk assessments, consequence classification, and emergency planning. This paper describes the research and development of a database of 12 tailings dam breach events with a specific focus on observations that are needed for numerical modelling, in conjunction with an assessment of existing dam breach conventions to improve consistency in reporting. The characteristics relevant to modelling include outflow volumes, breach processes, breach geometries, and runout observations local to the downstream area. This study and the new database shed light on the diversity of outflow materials, facility arrangements, breach processes, and downstream environments that affect the breach development and tailings runout. Familiarity with case studies is a crucial element of expert judgement for forward-analysis TDBAs, which this database supports. The database can also be used to define model inputs for back-analysis of additional tailings dam breach events, and simultaneously provides calibration or validation constraints with the runout observations. Continued review and critical assessments are needed to reduce uncertainties and to enhance case history data availability and quality in this database.

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Major recent failures in Brazilian mine waste containment facilities, current cases of maximum emergency level and imminent risk of rupture, and a brief sustainability analysis

Fraga Filho,

Aleixo

2024

Environ Sci Pollut Res

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Public remotely sensed data raise concerns about history of failed Jagersfontein dam

Cited by 9 publications

References 14 publications

Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure

Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure

Perspectivas sobre la recopilación y evaluación crítica de las características de rotura y desbordamiento históricos en presas de estériles: Implicaciones en la modelización numérica

Major recent failures in Brazilian mine waste containment facilities, current cases of maximum emergency level and imminent risk of rupture, and a brief sustainability analysis

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