Debba, PraveshCarranza, EJMStein, AVan der Meer, FD2009-09-212009-09-212009-08Debba, P, Carranza, EJM, Stein, A and Van der Meer, FD. 2009. Optimal spatial sampling scheme to characterize mine tailings. 57th Biennial Session of the International Statistical Institute, Durban, South Africa, 16-22 August, 2009. pp 1-14http://hdl.handle.net/10204/361557th Biennial Session of the International Statistical Institute, Durban, South Africa, 16-22 August, 2009This research discusses a novice method for sampling geochemicals to characterize mine tailings. Researchers model the spatial relationships between a multi-element signature and abundance estimates of secondary iron-bearing minerals in mine tailings dumps. The multi-element signature was modeled through factor analysis of element contents of mine tailings samples, which were measured in a laboratory. Abundances of secondary iron-bearing minerals were estimated through unmixing of the hyperspectral image pixels at the locations where the samples were obtained. Derivation of the proposed optimal sampling scheme makes use of covariates of the spatial variable of interest, which are readily, but less accurately obtainable by using airborne hyperspectral data. The covariates are abundances of secondary iron-bearing minerals estimated through spectral unmixing. Spatial relationships between a multi-element signature and abundance estimates of secondary iron-bearing minerals were modeled through conventional kriging with external drift. Derived spatial relationship models are then used for sampling scheme optimization by means of simulated annealing, for surface characterization of the mine tailings dumps. Via simulated annealing (1) an optimal retrospective sampling scheme for a previously sampled area is derived having fewer samples but having almost equal mean kriging prediction error as the original ground samples and (2) an optimal prospective sampling scheme for a new unvisited area is derived based on the variogram model of a previously sampled area.enOptimal spatial sampling schemeSimulated annealingGeochemicalsUnmixingMine tailingsDigital airborne imaging spectrometerHyperspectral imageMine tailingConference PresentationDebba, P., Carranza, E., Stein, A., & Van der Meer, F. (2009). Optimal spatial sampling scheme to characterize mine tailings. http://hdl.handle.net/10204/3615Debba, Pravesh, EJM Carranza, A Stein, and FD Van der Meer. "Optimal spatial sampling scheme to characterize mine tailings." (2009): http://hdl.handle.net/10204/3615Debba P, Carranza E, Stein A, Van der Meer F, Optimal spatial sampling scheme to characterize mine tailings; 2009. http://hdl.handle.net/10204/3615 .TY - Conference Presentation AU - Debba, Pravesh AU - Carranza, EJM AU - Stein, A AU - Van der Meer, FD AB - This research discusses a novice method for sampling geochemicals to characterize mine tailings. Researchers model the spatial relationships between a multi-element signature and abundance estimates of secondary iron-bearing minerals in mine tailings dumps. The multi-element signature was modeled through factor analysis of element contents of mine tailings samples, which were measured in a laboratory. Abundances of secondary iron-bearing minerals were estimated through unmixing of the hyperspectral image pixels at the locations where the samples were obtained. Derivation of the proposed optimal sampling scheme makes use of covariates of the spatial variable of interest, which are readily, but less accurately obtainable by using airborne hyperspectral data. The covariates are abundances of secondary iron-bearing minerals estimated through spectral unmixing. Spatial relationships between a multi-element signature and abundance estimates of secondary iron-bearing minerals were modeled through conventional kriging with external drift. Derived spatial relationship models are then used for sampling scheme optimization by means of simulated annealing, for surface characterization of the mine tailings dumps. Via simulated annealing (1) an optimal retrospective sampling scheme for a previously sampled area is derived having fewer samples but having almost equal mean kriging prediction error as the original ground samples and (2) an optimal prospective sampling scheme for a new unvisited area is derived based on the variogram model of a previously sampled area. DA - 2009-08 DB - ResearchSpace DP - CSIR KW - Optimal spatial sampling scheme KW - Simulated annealing KW - Geochemicals KW - Unmixing KW - Mine tailings KW - Digital airborne imaging spectrometer KW - Hyperspectral image KW - Mine tailing LK - https://researchspace.csir.co.za PY - 2009 T1 - Optimal spatial sampling scheme to characterize mine tailings TI - Optimal spatial sampling scheme to characterize mine tailings UR - http://hdl.handle.net/10204/3615 ER -