Equivalence assessment and leveling of geochemical datasets to generate integrated geochemical maps: Application to mineral exploration

Merging geochemical datasets of a few adjacent areas (areas with uniform geology) originated from two or more independent geochemical surveys can be highly beneficial to generate the integrated geochemical maps. To merge two or more independent datasets that are somehow linked together and create a new combined dataset, the significance of the shift between these datasets must be determined. Method of sampling, sample preparation, and chemical analysis are among the factors that critically impact on the measurement of the element concentration, thus explaining the inconsistency in the concentration of the same elements in various geochemical surveys. Therefore, studying the presence of a shift between datasets and leveling them as a pre-processing stage are necessary to combine geochemical datasets of adjacent areas and generate the integrated geochemical map. The purpose of assessing the equivalence is to recognize if there is a significant shift between values of the same variables (elements in geochemical surveys) in several datasets. The assessment is done by different methods, such as Quantile-Quantile Dispersion (QQD) diagram and a visual method dependent on expert's judgment to be introduced in this study. In this study, the Fisher test, T-student test, and Quantile-Value Diagram (QVD), a valid method showing the least shift between multiple datasets, were applied for the equivalence assessment between two or more datasets. Fisher and T-student tests were run to compare the variances and means of the same elements in two datasets from adjacent areas. The aim of the study was to generate an integrated geochemical map out of two independent geochemical surveys, in the north of Sarduiyeh and south of Rayen geological sheets with uniform geology (southern part of Urmia–Dokhtar metallogenic belt of Iran), to identify new anomalous areas. The equivalence assessment was performed for twelve elements, including Zn, Pb, Ag, Ni, Bi, Cu, As, Sb, Co, W, MO, and Mn. Based on QQD diagram method, some elements of the two databases such as Pb, Ni, and Bi were considered as equivalent, yet the QVD method showed a significant shift for them. Fisher and T-student tests confirmed the results of QVD. All the chemical elements of this survey exhibited shift in the adjacent maps and therefore required to be adjusted. For so doing, QQD diagram was drawn based on quantile pairs of shifted and reference datasets. A linear regression equation fitted in quantile pairs determined the relationship between the shifted and reference datasets. To adjust, it is important to select the reference dataset, which is the one with the best analysis method. Yet, it is difficult to select the reference dataset when the analysis method of the two geochemical surveys is the same. To timely address the problem, one of the two datasets was selected as the reference and an integrated geochemical map was separately generated for each element. It is deduced that if the dataset with larger quantile values is selected as the reference, more anomalies are shown, and vise verse. This approach, however, is based on the expert's judgment and may lead to unreal and unusual results. In another approach, which is more objective and cost-effective, the dataset, the median of which is close to the average of continental crust or the regional background if available, is used as the reference. This led to just one integrated geochemical map. Median Absolute Deviation (MAD) method was then applied to calculate the threshold of elements in integrated geochemical maps.