# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 3 Mine Planning Using GISs
### Open-Pit Optimization  
[PERSON] et al. [17] proposed a method to quantify the uncertainty that exists in the open-pit optimization results as a result of fluctuations in mineral prices due to changes in the world economy and mineral reserves. In this study, Monte Carlo simulations were used to generate multiple sets of mineral prices based on historical mineral price data, and the boundary optimization was repeatedly performed using this price. The results of the open-pit optimal boundary analysis are examined; 1 is assigned if a block exists within the optimal boundary, and 0 is assigned otherwise. This binarization is  
Figure 1: Schematic diagram of 3D hot-spot analysis for borehole data [16]. Figure 2: Kriging results of Au grade estimation using (**a**) all data or capped data with a (**b**) probability top-cut, (**c**) 95% percentile top-cut, (**d**) +2 standard deviation top-cut, and (**e**) hot-spot top-cut [16]. performed on all results of optimal boundary analysis. By integrating several binarized block models, a probability model was generated that quantitatively represented the uncertainty derived from changes in the mineral prices. Figure 3 shows an example of a probability model formation representing uncertainty. The results of a case study applying the proposed method to copper-zinc mixed deposits showed that significant uncertainty existed in the open-pit optimization results. Figure 4 shows the probability model representing the uncertainty in the pit optimization results analyzed through the case study. The method developed in this study was able to quantify the probability that each block representing deposits is included within the open pit optimal boundary when copper and zinc prices increased or decreased from the current reference prices (Figure 5) [17]. Figure 4: Probability model representing uncertainty in pit optimization results. (**a**) Case 1 considers the variation in copper prices only. (**b**) Case 2 considers the variation in zinc prices only [17]. Figure 3: Example of a probability model formation representing uncertainty: (**a**) binarized block model, (**b**) probability model formation using overlay analysis, and (**c**) sectional view of the probability model [17]. [PERSON] et al. [18] used GIS-based technology to develop overburden dumps and backfilled and mined-out zones as existing hilly topography; they also assessed soil erosion rates to identify dam locations that require monitoring. Soil erosion rates from the developed mining landscapes were evaluated using an empirical E30 model. A unique parametric change study was performed by altering the original normalized difference vegetation index values for estimating the soil erosion rate with different vegetation densities [18]. The case studies show that erosion rates increase in barren mining land, while the year-wise plantation decreases. The GIS-based approach presented could help assess the potential for soil erosion and taking the necessary steps before initiating the operation of the mining. [PERSON] and [PERSON] [19] developed a slope stability analysis tool that can be used to integrate slope stability analysis into the data management, ore reserve, and pit optimization processes of an open-pit mine. The module advanced in this study was successfully applied to the analysis of a surface mine. The case study included both kinematic and limit equilibrium stability analyses for bench and inter-ramp designs. In addition, this module utilized GIS-provided visualization tools and designated stability susceptibility zones along the pit. This approach can improve the optimized 3D pit configuration and contribute to a better understanding of the economic impact of various slope and pit designs. [PERSON] and [PERSON] [20] proposed a methodology that can rigorously determine the inter-ramp and bench-face slope orientations in a digital elevation model (DEM) of an open pit. They conducted a case study on Portage Pit in Canada and analyzed the GIS slope algorithm commonly used to evaluate the slope orientations in the DEM.