The Influence of Sample Numbers and Distribution on the Assessment of AMD Potential

Author(s): 
Andrew Garvie and Danny Kentwell
Date: 
Monday, November 20, 2017
First presented: 
9th Australian Workshop on Acid and Metalliferous Drainage
Type: 
Published paper
Category: 
Geochemistry

The number and distribution of waste rock samples geochemically characterised before and during mine operation impacts the ability to accurately represent the waste characteristics and to predict the potential for acid and metalliferous drainage (AMD). Numerous regulatory and industry bodies recommend the number of samples that are to be characterised. Commonly, the practitioner is advised to take account of the complexity of the geology and the scale of the mine. However, in many instances, the scientific and statistical basis for the recommended numbers is either not provided or is not clear. Consequently, there is ambiguity as to how the recommendations should be applied to a particular mine at a particular stage of development.

This paper demonstrates how conclusions regarding the potential for AMD production at a mine can depend on the number of samples characterised and the specific samples selected for characterisation. Data from three mines are used to illustrate the impact of various sample numbers on preliminary conclusions related to the potential for AMD.

Feature Author

Andrew Garvie

Andrew Garvie has more than 24 years’ experience providing scientific and technical assessments in acid and metalliferous drainage (AMD) and heap leach oxidation.  More recently he has undertaken assessments of self-heating and the potential for spontaneous combustion of coal wastes and carbonaceous black shales associated with sulfide minerals.  Studies have included the quantification of physical processes that support the oxidation of mine wastes and heap leach piles by measurement and predictive modelling.  He has assessed strategies used at mines to control oxygen supply and water flux into dumps and heap leach piles using the same methods.  Andrew’s experience includes use of geostatistics to assess the adequacy of sampling, geochemical characterisation to examine the potential of mine wastes to produce AMD, and assessment of contributors to pit lake water quality, including wall rock oxidation and in-pit waste rock disposal.  He has applied his understanding of the above processes to the development of conceptual waste landform closure strategies for the control of AMD production and spontaneous combustion.

Principal Consultant (Geoenvironmental)
PhD (Physics), MAusIMM
SRK Sydney
Danny Kentwell

Danny Kentwell is a geostatistician with a background in geological modelling, mine planning and surveying. He has 25 years’ international experience with varied commodities including gold, copper, mineral sands, iron ore, nickel laterites, nickel sulphides and phosphate. Danny’s skills cover, 3D modelling, Resource estimation, open pit optimisation scheduling and design. His geostatistical expertise includes standard and recoverable resource estimation techniques such as uniform conditioning, indicator kriging and conditional simulation as well as multivariate estimation and simulation. As a geostatistician and engineer, he has an excellent understanding of the advantages and limitations of different resource estimation techniques, their resulting block grade, tonnage and value curves and their use in mine planning. Danny also has experience in applying geostatistical techniques to waste characterisation and determination of sampling adequacy from very small data sets.

Principal Consultant (Geostatistics)
MSc (Mathematics & Planning; Geostatistics), FAusIMM
SRK Melbourne
SRK North America