An official website of the United States government
The Global Industry Standard on Tailings Management (GISTM) promotes performance-based approaches in geotechnical assessments. Hence, characterizing the spatial variability of deposited tailings is expected to be a key input for some tailings storage facilities (TSFs); however, it has seldom been investigated. In this study, we assess the spatial variability of thickened and conventional tailings that have been deposited into the same TSF, providing a unique opportunity to investigate two tailings technologies. A dense array of 15 cone penetration tests (CPTus) has been conducted to collect data. The results were processed using traditional and machine learning-based methods for data detrending when deriving random fields. In terms of correlation lengths, we find similar ranges for the thickened and conventional tailings and similar distributions, likely influenced by the depositional processes. In contrast, the variance in the conventional tailings is higher, which we attribute to its segregating nature.
more »
« less
This content will become publicly available on October 1, 2025
CPTu-Based Spatial Variability Assessment of Thickened and Conventional Mine Tailings
The Global Industry Standard on Tailings Management (GISTM) promotes performance-based approaches in geotechnical assessments. Hence, characterizing the spatial variability of deposited tailings is expected to be a key input for some tailings storage facilities(TSFs); however, it has seldom been investigated. In this study, we assess the spatial variability of thickened and conventional tailings, which have been deposited into the same TSF, providing a unique opportunity to investigate two tailings technologies. A dense array of 15 cone penetration tests (CPTus) with an average offset of 1.5 m has been conducted to collect data. In addition to evaluating the spatial variability, the collected information is also used to assess the potential of machine learning (ML) for detrending when deriving random fields. Using a new proposed stationarity score, we find that an ML-based detrending outperforms traditional procedures for most scenarios. In terms of correlation lengths, we find similar ranges for thickened and conventional tailings (vertical: δwv ¼ 0.2–0.6 m, horizontal δwh ¼ 1.5–4.5 m)and similar distributions, likely influenced by the depositional processes. In contrast, the variance in the conventional tailings is higher, which we attribute to its segregating nature. Finally, by inspecting previous studies on natural soils, we find that the variability of mine tailings(δwh=δwv ¼ 2–21) resembles that observed in alluvial deposits, which we attribute to the parallels in the depositional process
more »
« less
- Award ID(s):
- 2145092
- PAR ID:
- 10597789
- Publisher / Repository:
- ASCE Journal of Geotechnical and Environmental Engineering
- Date Published:
- Journal Name:
- Journal of Geotechnical and Geoenvironmental Engineering
- Volume:
- 150
- Issue:
- 10
- ISSN:
- 1090-0241
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Wood researchers increasingly rely on remote‐sensing products to augment field information about wood deposits in river corridors. The availability of very high‐resolution (<1 m) satellite imagery makes capturing wood over greater spatial extents possible, but previous studies have found difficulty in automatically extracting wood deposits due to the challenge in distinguishing wood from spectrally similar corridor features such as sand. We also lack knowledge on the spectral properties of different wood deposit types in multiple depositional environments. In this work, we explore image classification work‐flows for four wood deposit types in three North American environments: in‐channel jams deposited in the Tatshenshini River in Alaska, USA; a wood raft on the Slave River in Northwest Territories, Canada; and wood deposited along a lakeshore and coastal embayment in the Mackenzie River Delta in Northwest Territories, Canada. We compare classification results of object‐based and pixel‐based image analysis with supervised [support vector machine (SVM)] and unsupervised (ISO clustering) classifiers. We evaluate several accuracy assessment parameters and achieve overall classification accuracies of 65–99%, showing automated image classification is a possible approach for analysing wood across larger areas. We also find that wood sensitivity in the classification ranged from 0 to 95%, indicating that some techniques are better suited to wood capture than others. We find that supervised classification produced more accurate wood maps, though there is large variation in classification outcomes across environments related to spatial arrangement of wood in the landscape. We discuss the influence of depositional environment on classification and provide recommendations for designing a wood classification work‐flow.more » « less
-
Abstract The detection of planetary transits in the light curves of active stars, featuring correlated noise in the form of stellar variability, remains a challenge. Depending on the noise characteristics, we show that the traditional technique that consists of detrending a light curve before searching for transits alters their signal-to-noise ratio and hinders our capability to discover exoplanets transiting rapidly rotating active stars. We presentnuance, an algorithm to search for transits in light curves while simultaneously accounting for the presence of correlated noise, such as stellar variability and instrumental signals. We assess the performance ofnuanceon simulated light curves as well as on the Transiting Exoplanet Survey Satellite light curves of 438 rapidly rotating M dwarfs. For each data set, we compare our method to five commonly used detrending techniques followed by a search with the Box-Least-Squares algorithm. Overall, we demonstrate thatnuanceis the most performant method in 93% of cases, leading to both the highest number of true positives and the lowest number of false-positive detections. Although simultaneously searching for transits while modeling correlated noise is expected to be computationally expensive, we make our algorithm tractable and available as theJAX-powered Python packagenuance,allowing its use on distributed environments and GPU devices. Finally, we explore the prospects offered by thenuanceformalism and its use to advance our knowledge of planetary systems around active stars, both using space-based surveys and sparse ground-based observations.more » « less
-
Abstract We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021,https://doi.org/10.1002/essoar.10506419.1) and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the numerical model. This includes trying the parameterization in a different flow regime from that in which it was trained, at different spatial resolutions, and with other differences, all to test generalization. We assess whether tuning is possible, which is a common practice in GCM development. We find the parameterization, without modification or special treatment, to be stable and that the action of the parameterization to be diminishing as spatial resolution is refined. We also find some limitations of the machine learning model in implementation: (a) tuning of the outputs from the parameterization at various depths is necessary; (b) the forcing near boundaries is not predicted as well as in the open ocean; (c) the cost of the parameterization is prohibitively high on central processing units. We discuss these limitations, present some solutions to problems, and conclude that this particular ML parameterization does inject energy, and improve backscatter, as intended but it might need further refinement before we can use it in production mode in contemporary climate models.more » « less
-
Static liquefaction has been associated with the failure of several tailing storage facilities (e.g., the Brumadinho failure in 2019) and has been a persistent topic of discussion in the mining and tailings communities. Experimental studies have suggested that the onset of static liquefaction is dependent on the initial state (void ratio and confinement) and fabric anisotropy. In this context, traditional constitutive models developed under the critical state theory (CST) have been used to investigate the onset of static liquefaction for several complex loading paths. However, these models do not capture the effect of soil fabric anisotropy (inherent and induced) that are relevant in field conditions. In this study, the Anisotropic Critical State Theory (ACST) framework is used to assess the onset of static liquefaction in particulate materials, incorporating the effects of inherent and induced fabric. Our assessments derive an analytical criterion to assess static liquefaction that can be applied to screening assessments. The derived analytical criterion is a function of material properties, state, and fabric anisotropy, which couple the effects of fabric and loading direction. The use of the derived criteria in particulate materials is illustrated, and the implications of assessing the static liquefaction of mine tailings under generalized loading is also discussed.more » « less
