A comparison of search strategies to design the cokriging neighborhood for predicting coregionalized variables

Abstract

Cokriging allows predicting coregionalized variables from sampling information, by considering their spatial joint dependence structure. When secondary covariates are available exhaustively, solving the cokriging equations may become prohibitive, which motivates the use of a moving search neighborhood to select a subset of data, based on their closeness to the target location and the screen effect approximation. This paper investigates the efficiency of different strategies for designing a sub-optimal neighborhood wherein the simplification of the cokriging equations is challenging. To do so, five alternatives (single search, multiple search, strictly collocated search, multi-collocated search and isotopic search) are tested and compared with the reference unique neighborhood, through synthetic examples with different data configurations and spatial joint correlation models. The results indicate that the multi-collocated and multiple searches bear the highest resemblance to the reference case under the analyzed spatial structure models, while the single and the isotopic searches, which do not differentiate the primary and secondary sampling designs, yield the poorest results in terms of cokriging error variance.

Mineral resource estimation using a combination of drilling and IP&Rs data using statistical and cokriging methods

“…For more details see in section 4.2. Cokriging and presented explanations in (Wackernagal, 2003;Chiles and Delfiner, 2012;Madani and Emery, 2018).…”

“…By considering the data located in a neighborhood of the target location and data distribution situation, the search strategies to select neighboring data will be selected. There are some search strategies including: collected neighborhood, multi-collected neighborhood, full neighborhood (Chiles andDelfiner, 2012, Madani andEmery, 2018). In collected cokriging only retained secondary data are the ones available at the target location (Madani and Emery, 2018).…”

“…There are some search strategies including: collected neighborhood, multi-collected neighborhood, full neighborhood (Chiles andDelfiner, 2012, Madani andEmery, 2018). In collected cokriging only retained secondary data are the ones available at the target location (Madani and Emery, 2018). The collected cokriging and full cokriging have been used in this study.…”

Investigating the applicability of induced polarization method in ore modelling and drilling optimization: a case study from Abassabad, Iran

“…). There are some methods for coregiobalization model investigation such as linear model coregionalization (LMC), Markov‐type model and intrinsic linear model (Madani and Emery ). The LMC is an approach to simultaneously model direct and cross‐variogram in the multivariate setting, therefore it is suitable in cokriging and cosimulation (Goulard and Voltz ; Leuangthong et al .…”

“…For variography, cross‐variogram and cokriging processes, we used the SGeMS software. There are some search strategies including collected neighbourhood, multi‐collected neighbourhood and full neighbourhood for data selection in SGeMS (Chiles and Delfiner ; Madani and Emery ). Based on our data condition, we used full cokriging for modelling.…”