A Simulated Annealing-Based Algorithm to Locate Additional Drillholes for Maximizing the Realistic Value of Information

Locating additional drillholes based on information gathered from the initial drilling is a very difficult decision-making step in the process of detailed explorations. The most appropriate locations for additional drillholes are those wherein the information gathered from drilling has more value compared to that from other locations. From among the common methods proposed in information systems for measuring the information value, use of the “realistic value” is a very practical one. The realistic value of information is derived from measuring the differences in the decision makers’ performances when provided with different information sets. On this basis, a mathematical model has been proposed in this paper for optimal location of additional drillholes where the information gathered from drillholes has the highest possible value. Due to the combinatorial nature of this model, use has been made of a simulated annealing-based algorithm for its solution. The proposed model has been applied in Sungun copper deposit for locating additional drillholes; results have revealed that the model is valid.

[1]  Ferenc Szidarovszky,et al.  Multiobjective observation network design for regionalized variables , 1983 .

[2]  Donald E. Scheck,et al.  Optimum locations for exploratory drill holes , 1983 .

[3]  R. Reese Geostatistics for Environmental Scientists , 2001 .

[4]  Ardeshir Hezarkhani,et al.  Determination of Realistic and Statistical Value of the Information Gathered from Exploratory Drilling , 2011 .

[5]  Ardeshir Hezarkhani,et al.  Optimally locating additional drill holes in three dimensions using grade and simulated annealing , 2012, Journal of the Geological Society of India.

[6]  Kalyan Saikia,et al.  Exploration drilling optimisation using geostatistics: a case in Jharia Coalfield, India , 2006 .

[7]  S. Soltani,et al.  Use of genetic algorithm in optimally locating additional drill holes , 2011 .

[8]  R. Lark,et al.  Geostatistics for Environmental Scientists , 2001 .

[9]  R. Bateman Orebody Modelling and Strategic Mine Planning.R. Dimitrakopoulos, Editor. Pp 402. The Australasian Institute of Mining and Metallurgy Spectrum Series 14. Second edition. 2007. ISBN 978-1-920806-76-7. Price (outside Australia) hardcopy A$80.00, CD-ROM A$60.00. , 2008 .

[10]  Constantino Tsallis,et al.  Optimization by Simulated Annealing: Recent Progress , 1995 .

[11]  Alfred Stein,et al.  Constrained Optimization of Spatial Sampling using Continuous Simulated Annealing , 1998 .

[12]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ardeshir Hezarkhani,et al.  Proposed algorithm for optimization of directional additional exploratory drill holes and computer coding , 2011, Arabian Journal of Geosciences.

[14]  Xin Xie Computer Applications in the Minerals Industry , 2001 .

[15]  Mark Gershon,et al.  Application of a new approach for drillholes location optimization , 1988 .

[16]  Sartaj Sahni,et al.  Experiments with Simulated Annealing , 1985, DAC 1985.

[17]  Richard Nötstaller United Nations International Framework Classification for Reserves/Resources-Solid Fuels and Mineral Commodities-final version , 1998 .

[18]  Alfred Stein,et al.  Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps , 2009 .

[19]  Ardeshir Hezarkhani,et al.  Additional Exploratory Boreholes Optimization Based on Three-Dimensional Model of Ore Deposit , 2009 .

[20]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .