The amount and accuracy of nodule resources estimation in the Pacific Ocean are among the main factors conditioning the future exploitation. The estimates are based on the results of classical, direct seafloor sampling. Due to the large distance between sampling sites, the accuracy of assessing nodule resources in small parts of the deposit is low. The accuracy can be increased by using a large number of seafloor photographs taken along the route of the research vessel performing classic sampling. The study conducted for a part of the area administered by Interoceanmetal Joint Organization (IOM) included: (i) determining a model of the relationship between nodule abundance and seafloor nodule coverage using statistical methods, (ii) assessing the accuracy of nodule resources estimation using a geostatistical kriging procedure, (iii) proposing a preliminary classification of resources referring to International Seabed Authority (ISA) classification standards as material for further discussion. It was found that achievement of high accuracy in the estimation of nodule resources (with relative standard error <5%) in blocks planned for annual exploitation based on direct sampling is difficult. While the use of seafloor photographs increases the accuracy of estimating nodule resources, this improvement is not radical due to the unfavorable, preferential arrangement of photographic data.
Direct seafloor sampling using, e.g., box corers is insufficient to obtain an acceptable accuracy of nodule resource estimates in small parts of potential deposits. In order to increase the reliability of the estimates, it was rational to use the results of photographic surveys of the seafloor. However, the estimation of nodule abundance based on seafloor photographs is associated with a number of problems and limitations. The main goal of the study was a statistical analysis of the role and interrelationships of selected factors affecting the accuracy of nodule abundance assessment based on seafloor photographs from the H22 exploration block located in the Interoceanmetal Joint Organization (IOM) area in the Pacific. A statistically significant, but only moderately strong, correlation was found between the abundance of nodules and seafloor nodule coverage (quantitative variables), the nodule abundance and genetic type of nodules (ordinal variable estimated visually from photos), and between seafloor coverage with nodules and sediment coverage of nodules (ordinal variable estimated visually from photos). It was suggested that the nodule abundance could be effectively and more accurately predicted using a general linear model that includes both quantitative and ordinal variables.
The authors attempted to introduce some components of the Australasian JORC Code system to the categorization of Polish Cu-Ag and Zn-Pb ore resources. The proposed geostatistical method of resource categorization applies two criteria: continuity of deposit parameters described by semivariograms and permissible, relative standard error of resources estimation determined with the ordinary kriging procedure. Considering the first criterion, we propose the following values of autocorrelation coefficients, which define the ranges (distances) of the resources categories around the measurement sites (e.g., exploration wells): “measured” category (A + B in the Polish system) – the values of the autocorrelation coefficient from 1 to 2/3, “indicated” category (C1 in the Polish system) – the values of the autocorrelation coefficient from 2/3 to 1/3, “inferred” category (partly C2 in the Polish system) – the values of the autocorrelation coefficient from 1/3 to 1/20, “out-of-doors” category (partly D in the Polish system) – the values of autocorrelation coefficient from 1/20 to 0. The second criterion of resources categorization is based upon the relative, standard errors of resources estimations calculated for the parts of deposit defined with the first criterion. The following permissible values of errors determined as the errors of ordinary kriging have been proposed: “measured” category (A + B in the Polish system) – 10% error, “indicated” category (C1 in the Polish system) – 20% error, “inferred” category (partly C2 in the Polish system) – 30% error, “out-of-doors” category (partly D in the Polish system) – 50% error. It was found that the Polish metal ore deposits reveal low continuity of deposit parameters, as indicated by a high share of nugget variance in the overall variability of these parameters. Moreover, an inconsistency was observed between the semivariograms of deposit parameters based upon samplings of drill cores and underground mine workings, which results in extreme differences in the ranges of resources categories around the sampling sites. This, in turn, causes radical discrepancies in estimated resources. Thus, it was concluded that the sufficiently credible categorization of resources is only possible when a significant part of the deposit is explored with mine workings, in which the grid of sampling sites is much denser than that of exploration wells. It was proposed that the principal criterion of resources categorization should be the permissible error of estimations whereas the continuity of deposit parameters should only be a supplementary criterion.
Volumetric density of the detailed lithological units in Polkowice-Sieroszowice Cu-Ag deposit has been compared to the density of the three basic ore types. Eight diferrent lithologies of the Cu-Ag deposit have been taken into account. It appeared that the resources in them estimated on the basis of volumetric densities are approximately 3% higher than analogous estimates for volumetric densities attributed to the basic ore types. The correlation and regression analysis have shown that the porosity of rocks is the dominant factor affecting the volumetric density, whereas the Cu content plays a secondary role. Some of the lithologies have revealed some heterogeneity of spatial density that can be explained by the variability of mineral cement and porosity as well as the presence of non-copper heavy minerals (e.g. galena, pyrite). The knowledge of the density of individual lithologies enables more accurate estimation of their resources leading to more effective production.
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