The Windy Craggy Massive - Sulphide Deposit northwestern British Columbia, Canada

Downing, Bruce W.; Webster, Maud; Beckett, Robert J. G.

doi:10.4095/132327

Cited by 3 publications

(4 citation statements)

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“…This will provide basic pre-screening data to help identify samples most likely to require detailed geochemical ARD testwork. For the purpose of ARD domaining at early life-of-mine stages, calculating maximum potential acidity (MPA) from S Total is an appropriately conservative risk approach, minimising misclassification of PAF (potentially acid-forming) samples as NAF (non-acid forming) materials (Downing and Giroux, 1993;Borden, 2003). Disadvantageously, the turn-around time with off-site analyses is likely a number of days rather than hours as would be required to support best practice sampling.…”

Section: A New Architecture For Ard Predictionmentioning

confidence: 99%

“…The first insight into the likely magnitude of acid production of a sample is through measurement of total sulphur (S Total ) with examples given in Downing and Giroux (1993), Borden (2003), Weber et al (2005) and Hakkou et al (2009). Sulphur contents are commonly determined by placing a sample in high-temperature furnace and analysing the developed gases by infrared spectrometry.…”

Section: Sulphurmentioning

confidence: 99%

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A critical review of acid rock drainage prediction methods and practices

Parbhakar-Fox

Lottermoser

2015

Minerals Engineering

150

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Section: A New Architecture For Ard Predictionmentioning

confidence: 99%

Section: Sulphurmentioning

confidence: 99%

A critical review of acid rock drainage prediction methods and practices

Parbhakar-Fox

Lottermoser

2015

Minerals Engineering

150

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“…The incorrect estimation of maximum potential acidity (MPA) based on total sulfur data alone (Parbhakar-Fox and Lottermoser, 2015) is a significant source of error in ARD prediction (Downing andGiroux, 1993, Borden, 2003). Overestimation of the MPA can occur due to the presence of sulfate minerals which are incorporated into this calculation when based on total sulfur alone (Dold, 2017).…”

Section: Geoenvironmental Significance Of Mineralogical Domainingmentioning

confidence: 99%

Applications of hyperspectral mineralogy for geoenvironmental characterisation

Fox

Parbhakar-Fox

Moltzen

et al. 2017

Minerals Engineering

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“…ARD characterization is usually accomplished by gathering field data of different types, such as neutralization potential, total sulfide, sulfur (or rather, SO 4 -2 sulfur), carbonate and carbon dioxide, as well as water quality analyses and specific trace elements. Several authors have discussed the use of field samples and blocks models to characterize different aspects of ARDrelated problems, see for example Miller and Hertel (1997), Downing and Giroux (1993), and Downing and Madeisky (1997).…”

Section: Introduction and Objectivesmentioning

confidence: 99%

Geostatistical Modeling of Acid Rock Prediction Uncertainty

Rossi¹

2006

JASMR

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The estimation of actual or potential acid rock drainage (ARD) at mine sites is usually accomplished by sampling specific parameters that allow detection and prediction of the potential for ARD. The use of block models to estimate and describe the spatial extent of relevant variables is becoming more common, although quantification of the uncertainty associated with the problem is generally not available, yet it can be critical in an ARD characterization study.Uncertainties in sampling and analytical processes, in the characterization of the volumes and areas affected or potentially affected by ARD, in the interpolation of sampled values, and in the characterization of physical processes that allow prediction of fate and transport, are always present. It is unrealistic to pretend that the estimation process is error-free, and thus it follows that it is important to provide adequate models of uncertainty, in addition to reasonable estimates of ARD potential. The model of uncertainty can then be used to develop technical risk assessments, including false positives or negatives of certain variables exceeding (or not) certain thresholds.This paper outlines a stochastic method based on geostatistical conditional simulations that allows assessment and modeling of uncertainty in spatial modeling. This assessment is then translated into risk levels, allowing for a decision-making process that is based on levels of uncertainty. The concept of Loss Functions is illustrated with an example drawn from a porphyry Cu-Mo deposit in South America.

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The Windy Craggy Massive - Sulphide Deposit northwestern British Columbia, Canada

Cited by 3 publications

References 0 publications

A critical review of acid rock drainage prediction methods and practices

A critical review of acid rock drainage prediction methods and practices

Applications of hyperspectral mineralogy for geoenvironmental characterisation

Geostatistical Modeling of Acid Rock Prediction Uncertainty

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