## 4 Results and Discussion
### Estimated Resources  
Deposit resources were estimated using the average abundance method and polygon method with assumptions of marginal cut-off values: Ni \(\geq\) 0.5%; Ni \(\geq\) 0.7%; Ni \(\geq\)1.0% and Ni \(\geq\) 1.0%, \(\geq\)100 kg/m\({}^{2}\). Laterites with Ni% \(\geq\) 0.9% are considered to be economic [9]. Semivariograms were performed to determine variability. Semivariograms (Figures 8 and 9) obtained for the distribution of various deposit parameters are random in the vast majority of cases, which is probably related to the high variability of deposit parameters in lateritic  
Figure 7: Enrichment/depletion in selected oxides and elements relative to the bedrock (fresh harzburgite). deposits [22]. Only in the case of Ni content in limonite does the obtained variogram indicate a partly non-random distribution. In the average abundance method, assuming a random distribution of deposit parameters, the resources were calculated as the product of the average abundance and the area of the deposit area according to:  
\[Q=\overline{q}\cdot F \tag{1}\]  
Figure 8: Semivariogram of Ni abundance [kg/m\({}^{2}\)] in all studied horizons of the deposit (**left**) and in limonite (**right**). Numbers next to curve nodes indicate number of pairs for which the given quantity was calculated. Figure 9: Semivariogram of Co abundance [kg/m\({}^{2}\)] in deposit balance zone for Co (limonite horizon and transition zone—right) and in limonite horizon. where:  
\(Q\)--resources [t];  
\(\overline{q}\)--abundance [%];  
\(F\)--the area of the calculation field [m\({}^{2}\)]. The average abundance is measured as the product of known deposit parameter values according to the formula:  
\[\overline{q}=m\cdot\gamma_{o}\cdot p\cdot 0.01 \tag{2}\]  
where:  
\(\overline{q}\)--abundance [%];  
\(m\)--thickness [m];  
\(\gamma_{o}\)--bulk density [t/m\({}^{3}\)];  
\(p\)--useful component [%]. Area boundary (Figure 10) of the geologically recognized area was determined, i.e., by the slope criterion calculated using RockWorks 16 software (Golden, CO, USA), on the basis of SRTM data with 30 m resolution. Negative criteria were slope gradient greater than 25\({}^{\circ}\) and distance from the survey point > d/2 (outside the internal area defined by the line connecting the external reconnaissance points), where d is the average distance between reconnaissance points (for adjacent holes). Value of deposit parameters for individual drill holes are presented in Table 2. The results of the calculations (Ni and Co) with a breakdown into individual horizons for filed and different cut-off criteria are shown in the Table 8.  
Figure 10: Boundary of the deposit using the cut-off criterion \(\geq\) 0.7%. In order to verify the applied methodology (average abundance method), calculations using the polygon method (Boldyrev) were also carried out for the same fields and different cut-off criteria. The determination of polygons was performed using GIS software--QGIS (Geographic Information System) and Computer Aided. The density of an exploratory grid covering the tenement area in block B was validated to assess the inferred resource within the survey area. Estimation of inferred resource (Table 9) volume was performed by using the triangular grouping method (Figure 11). The partial and total results of the calculations (Ni and Co) are given in the Table 12.