Mining Goes Digital – Mueller et al. (Eds)
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
286
Incorporation of mineralisation risk into underground
mine planning
Rafael Campos Rosado
AngloGold Ashanti, Brasil
João Felipe C.L. Costa
Universidade Federal do Rio Grande do Sul, Brasil
Artur Almgren Saldanha
Geovariances, Chile
ABSTRACT: Underground mine planning aims at determining the amount and sequence
of extraction of the mineral resources in order to maximize the deposit profit. It involves
multiple sources of information which add risk to the developed plan. Among the risks that
can affect the financial return of a mining company, those associated with the geology of the
deposit stand out; more specifically the amount of metal contained within the deposits or
parts of it.
This risk is mainly due to the complexity of the geological process of the mineral deposits
allied to the sparse drilling spacing available used for constructing deterministic models to
determine the tonnages and contents of the variable of interest.
This paper aims at develop a methodology to quantify the risk related to the prediction of
metal contained in a stope. The risk will be measured based on mine planning constructed
through probabilistic models based on geostatistical simulations of the zinc content. Additionally, density is calculated and volume designed for each scenario simulated.
Each designed stope will have its metal content predicted by each simulated model after
volume correction using the regression error. Multiple simulations allow to calculate the probability of metal contained at each stope. Based on these results, a risk indicator is proposed
to flag the associated risk with the metal contained value in each stope. From this indicator, a
rank from low to high risk zones can be taken into account during mining scheduling.
1 INTRODUCTION
Like all economic activities, the ultimate goal of mining is to provide profits for investors.
In addition to economic and market parameters, the profit resulting from a mineral extraction enterprise is directly related to the quantity and quality of the mineral extracted. Therefore, a good knowledge of the deposit is a requirement for attracting investors.
The traditional methods of resources estimation are characterized by providing an unique
value for each point or block of the deposit. Thus, it is assumed that each mining block, for
example, has a determined value of metal contained in a certain volume of the space and all
the financial calculations of return of the investment and even the operational calculations for
adjustment of the treatment plant of that ore adopts this information as the ground truth.
Probabilistic models or geostatistical simulation methods were developed to solve this deficiency of the traditional resources estimation methods.
These methods differ in not providing a single modeling solution for the mineral deposit,
but infinite solutions equally likely to describe the behavior of the variable under study. In
this way, several geological models are generated that represent the deposit under study.
© 2019 Taylor & Francis Group, London, ISBN 978-0-367-33604-2
286
Incorporation of mineralisation risk into underground
mine planning
Rafael Campos Rosado
AngloGold Ashanti, Brasil
João Felipe C.L. Costa
Universidade Federal do Rio Grande do Sul, Brasil
Artur Almgren Saldanha
Geovariances, Chile
ABSTRACT: Underground mine planning aims at determining the amount and sequence
of extraction of the mineral resources in order to maximize the deposit profit. It involves
multiple sources of information which add risk to the developed plan. Among the risks that
can affect the financial return of a mining company, those associated with the geology of the
deposit stand out; more specifically the amount of metal contained within the deposits or
parts of it.
This risk is mainly due to the complexity of the geological process of the mineral deposits
allied to the sparse drilling spacing available used for constructing deterministic models to
determine the tonnages and contents of the variable of interest.
This paper aims at develop a methodology to quantify the risk related to the prediction of
metal contained in a stope. The risk will be measured based on mine planning constructed
through probabilistic models based on geostatistical simulations of the zinc content. Additionally, density is calculated and volume designed for each scenario simulated.
Each designed stope will have its metal content predicted by each simulated model after
volume correction using the regression error. Multiple simulations allow to calculate the probability of metal contained at each stope. Based on these results, a risk indicator is proposed
to flag the associated risk with the metal contained value in each stope. From this indicator, a
rank from low to high risk zones can be taken into account during mining scheduling.
1 INTRODUCTION
Like all economic activities, the ultimate goal of mining is to provide profits for investors.
In addition to economic and market parameters, the profit resulting from a mineral extraction enterprise is directly related to the quantity and quality of the mineral extracted. Therefore, a good knowledge of the deposit is a requirement for attracting investors.
The traditional methods of resources estimation are characterized by providing an unique
value for each point or block of the deposit. Thus, it is assumed that each mining block, for
example, has a determined value of metal contained in a certain volume of the space and all
the financial calculations of return of the investment and even the operational calculations for
adjustment of the treatment plant of that ore adopts this information as the ground truth.
Probabilistic models or geostatistical simulation methods were developed to solve this deficiency of the traditional resources estimation methods.
These methods differ in not providing a single modeling solution for the mineral deposit,
but infinite solutions equally likely to describe the behavior of the variable under study. In
this way, several geological models are generated that represent the deposit under study.