Stochastic simulation for the quantification of mine spoil variability and rehabilitation decision making

Soil attributes, including those in mine spoil heaps, critically affect plant growth during land rehabilitation. Their characterization through a limited number of samples requires quantification of spatial variability, which is then used at various stages throughout the rehabilitation process and assists risk analysis and rehabilitation decision making. Stochastic simulation is a tool used for the quantification of uncertainty. This paper presents the suitability of stochastic simulation for the joint simulation of soil attributes and introduces a new computationally efficient method. The method is based on: (i) the Minimum/Maximum Autocorrelation Factors (MAF), involving the de-correlation of pertinent variables into spatially noncorrelated factors, and (ii) the simulation of MAF and back transformation to the conditional simulations of the correlated variables. MAF factors in point (ii) are simulated using the new Generalised Sequential Gaussian Simulation (GSGS) technique which is substantially more efficient that the traditional sequential simulation methods. The formulated approach is applied to mine spoil data, specifically electrical conductivity and pH, which typically contribute to restricted plant growth on spoils in Queensland coal mines. The results of the simulations are used to quantify risk of exceeding significant thresholds for each variable, thereby identifying problem rehabilitation areas. The case study demonstrates the practical aspects of the method as well as its use in planning rehabilitation strategies and predictions of future performance of the rehabilitation.