Joint simulation of coal quality attributes using a new simulated annealing-based method of diagonalisation

References

• L.E. De Souza and J.F.C.L. Costa, Sample weighted variograms on the sequential indicator simulation of coal deposits, Inter. J. of Coal Geology 112 (2013), pp. 154–163. doi:http://dx.doi.org/10.1016/j.coal.2012.12.005.
   Web of Science ®Google Scholar
• K. Saikia and B.C. Sarkar, Coal exploration modelling using geostatistics in Jharia coalfield, India, Inter. J. of Coal Geology 112 (2013), pp. 36–52. doi:http://dx.doi.org/10.1016/j.coal.2012.11.012.
   Web of Science ®Google Scholar
• M.E. Hohn and J.Q. Britton, A geostatistical case study in West Virginia: All coals are not the same, Inter. J. of Coal Geology 112 (2013), pp. 125–133. doi:https://dx.doi.org/10.1016/j.coal.2012.12.004.
   Web of Science ®Google Scholar
• E. Pardo-Igúzquiza, P.A. Dowd, J.M. Baltuille, and M. Chica-Olmo, Geostatistical modelling of a coal seam for resource risk assessment, Inter. J. of Coal Geology 112 (2013), pp. 134–140. doi:10.1016/j.coal.2012.11.004.
   Web of Science ®Google Scholar
• C.Ö. Karacan, A. Drobniak, and M. Mastalerz, Coal bed reservoir simulation with geostatistical property realizations for simultaneous multi-well production history matching: A case study from Illinois Basin, Indiana, USA, Inter. J. of Coal Geology 131 (2014), pp. 71–89. doi:http://dx.doi.org/10.1016/j.coal.2014.06.002.
   Web of Science ®Google Scholar
• C.Ö. Karacan and R.A. Olea, Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes, Inter. J. of Rock Mechanics and Mining Sci 59 (2013), pp. 1–14. doi:http://dx.doi.org/10.1016/j.ijrmms.2012.11.003.
   PubMed Web of Science ®Google Scholar
• A.E. Tercan and B. Sohrabian, Multivariate geostatistical simulation of coal quality data by independent components, Inter. J. of Coal Geology 112 (2013), pp. 53–66. doi:10.1016/j.coal.2012.10.007.
   Web of Science ®Google Scholar
• B. Sohrabian, E.A. Tercan, and R. Hasanpour, Assessment of uncertainty for coal quality-tonnage curves through minimum spatial cross-correlation simulation, Inter. J. of Mining & Geo-Eng. 52 (2018), pp. 75–86.
   Google Scholar
• U.A. Mueller and J. Ferreira, The U-WEDGE transformation method for multivariate geostatistical simulation, Math Geosci. 44 (4) (2012), pp. 427–448. doi:10.1007/s11004-012-9384-7.
   Web of Science ®Google Scholar
• A.J. Desbarats and R. Dimitrakopoulos, Geostatistical simulation of regionalized pore-size distributions using min/max autocorrelation factors, Math Geol. 32 (8) (2000), pp. 919–941. doi:10.1023/A:1007570402430.
   Google Scholar
• P. Goovaertz, Spatial orthogonality of principal components computed from coregionalized variables, Math Geol. 25 (3) (1993), pp. 281–302. doi:10.1007/BF00901420.
   Google Scholar
• V. Suro-Pérez, Indicator principal component kriging: The multivariate case, Geostatistics Tróia 92 (1993), pp. 441–454. doi:http://dx.doi.org/10.1007/978-94-011-1739-5_35.
   Google Scholar
• P. Byrne, R.L. Runkel, and K. Walton-Day, Synoptic sampling and principal components analysis to identify sources of water and metals to an acid mine drainage stream, Geol. Soc. Am. 24 (20) (2017), pp. 17220–17240. doi:10.1007/s11356-017-9038-x.
   Google Scholar
• G. Louloudis, C. Roumpos, K. Theofilogiannakos, and N. Stathopoulos, Multi-seam coal deposit modeling via principal component analysis & GIS, Minerals 9 (9) (2019), pp. 532. doi:10.3390/min9090532.
   Web of Science ®Google Scholar
• B. Sohrabian and Y. Ozcelik, Joint simulation of a building stone deposit using minimum/maximum autocorrelation factors, Constr. Build. Mater. 37 (2012), pp. 257–268. doi:10.1016/j.conbuildmat.2012.07.033.
   Web of Science ®Google Scholar
• C.Z.D. Silva and J.F.C.L. Costa, Minimum/Maximum autocorrelation factors applied to grade estimation, Rem: Revista Escola de Minas 67 (2) (2014), pp. 209–214. doi:10.1590/S0370-44672014000200013.
   Google Scholar
• A. Boluwade, Joint simulation of spatially correlated soil health indicators, using independent component analysis and minimum/maximum autocorrelation factors, ISPRS Int. J. Geo-Inf. 9 (1) (2020), pp. 30. doi:10.3390/ijgi9010030.
   Web of Science ®Google Scholar
• B. Sohrabian, H. Hosseinzadeh Gharehgheshlagh, S. Soltani-Mohammadi, and J. Abdollahi Sharif, Evaluation of tailings from a porphyry copper mine based on joint simulation of contaminants, Nat. Resour. Res. 29 (2) (2020), pp. 983–1005. doi:10.1007/s11053-019-09517-1.
   Web of Science ®Google Scholar
• T. Xie and D.E. Myers, Fitting matrix-valued variogram models by simultaneous diagonalization (Part I: Theory), Math Geol 27 (7) (1995), pp. 867–875. doi:10.1007/BF02087100.
   Google Scholar
• S. De Iaco, M. Palma, and D. Posa, Choosing suitable linear coregionalization models for spatio-temporal data, Stochastic Environ. Research and Risk Assessment 33 (7) (2019), pp. 1419–1434. doi:http://dx.doi.org/10.1007/s00477-019-01701-2.
   Web of Science ®Google Scholar
• J. Vargas-Guzmán and R. Dimitrakopoulos, Computational properties of min/max autocorrelation factors, Comput. Geosci. 29 (6) (2003), pp. 715–723. doi:10.1016/S0098-3004(03)00036-0.
   Web of Science ®Google Scholar
• M. Joho and K. Rahbar (2002). Joint diagonalization of correlation matrices by using Newton methods with application to blind signal separation. Sensor Array and Multichannel Signal Processing Workshop Proceedings, 403–407. 10.1109/sam.2002.1191070.
   Google Scholar
• P. Tichavsky and A. Yeredor, Fast approximate joint diagonalization incorporating weight matrices, IEEE Trans. Signal Process. 57 (3) (2009), pp. 878–891. doi:10.1109/TSP.2008.2009271.
   Web of Science ®Google Scholar
• J.F. Cardoso and A. Souloumiac, Jacobi angles for simultaneous diagonalization, SIAM J. on Matrix Analysis and Applications 17 (1) (1996), pp. 161–164. doi:10.1137/S0895479893259546.
   Web of Science ®Google Scholar
• B. Sohrabian and A.E. Tercan, Multivariate geostatistical simulation by minimising spatial cross-correlation, C.R. Geosci. 346 (3–4) (2014), pp. 64–74. doi:10.1016/j.crte.2014.01.002.
   Web of Science ®Google Scholar
• A.Y. Zomaya and R. Kazman, Simulated Annealing Techniques. Algorithms and Theory of Computation Handbook: General Concepts and Techniques, Chapman & Hall/CRC, 2010.
   Google Scholar
• A.E. Tercan, Importance of orthogonalization algorithm in modeling conditional distributions by orthogonal transformed indicator methods, Math Geol 31 (2) (1999), pp. 155–173. doi:10.1023/A:1007557701073.
   Google Scholar
• S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, Optimization by simulated annealing, Science 220 (4598) (1983), pp. 671–680. doi:https://doi.org/10.1126/science.220.4598.671.
   PubMed Web of Science ®Google Scholar
• A.J. Monticelli, R. Romero, and E.N. Asada, Fundamentals of Simulated Annealing, Modern Heuristic Optimization Techniques, John Wiley & Sons, 2007.
   Google Scholar
• M.H. Raoufi, A. Farasat, and M. Mohammadifard, Application of simulated annealing optimization algorithm to optimal operation of intelligent well completions in an offshore oil reservoir, J. of Petroleum Exploration and Production Tech. 5 (3) (2014), pp. 327–338. doi:10.1007/s13202-014-0142-x.
   Google Scholar
• Soltani-Mohammadi, S., Safa, M. & Mokhtari, H. (2016). Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization. Computers & Geosciences, 95, 146–155.
   Web of Science ®Google Scholar
• S. Soltani-Mohammadi, M. Safa, and H. Mokhtari, Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization, Computers & Geosciences. 95 (2016). pp. 146–155.
   Web of Science ®Google Scholar
• A. Zameer, S.M. Mirza, and N.M. Mirza, Core loading pattern optimization of a typical two-loop 300 Mwe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA(SA) schemes, Ann. Nucl. Energy. 65 Available at, (2014), pp. 122–131. http://dx.doi.org/10.1016/j.anucene.2013.10.024.
   Web of Science ®Google Scholar
• A.E. Tercan, B. Ünver, and M.A. Hindistan, 2011. Lignite resource estimation by unwrinkling. 22nd World Mining Congress and Exh., 11–16 September 2011: Proceedings Book, vol. 11, Aydoğdu ofset, Ankara-Turkey (2011), pp. 139–142
   Google Scholar
• K.Y. Lee and M.A. El-Sharkawi, Modern Heuristic Optimization Techniques : Theory and Applications to Power Systems, Piscataway N.J., Hoboken N.J, 2008.
   Google Scholar
• A. Soares, Direct sequential simulation and cosimulation, Math Geol 33 (8) (2001), pp. 911–926. doi:10.1023/A:1012246006212.
   Google Scholar