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Figure E.1 Comparison of LGS domain generated with alternative modelling approaches COMPARISON OF ESTIMATION DOMAINS

Figure 70 E.1 Comparison of LGS domain generated with alternative modelling approaches COMPARISON OF ESTIMATION DOMAINS

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Figure 1.1 Alternative geological interpretations implications on mineral resource estimates.
Figure 1.1 Alternative geological interpretations implications on mineral resource estimates.
hand, gradual change of grades represents gradational contacts (Figure 2.1). characterized by abrupt changes in mineralization grades across contact (Figure 2.1). On the other
hand, gradual change of grades represents gradational contacts (Figure 2.1). characterized by abrupt changes in mineralization grades across contact (Figure 2.1). On the other
Figure 2.2 An illustration of higher uncertainty near contact (Rossi & Deutsch, 2014) important. of contacts through geologic interpretational and conditional simulation techniques is substantially Spatial data availability compounded with intrinsic variability usually results in higher
Figure 2.2 An illustration of higher uncertainty near contact (Rossi & Deutsch, 2014) important. of contacts through geologic interpretational and conditional simulation techniques is substantially Spatial data availability compounded with intrinsic variability usually results in higher
Figure 2.3 Common representation of histogram (left) and probability plot (right) (Rossi & Deutsch, 2014) presented by histograms.
Figure 2.3 Common representation of histogram (left) and probability plot (right) (Rossi & Deutsch, 2014) presented by histograms.
Figure 2.4 Cell declustering technique (left), plot of declustered mean vs. cell size (right) (Rossi & Deutsch, 2014) The weight calculated in this way are greater than zero and sum to one. The estimate of weights obtained by cell declustering method is not unique and depends
Figure 2.4 Cell declustering technique (left), plot of declustered mean vs. cell size (right) (Rossi & Deutsch, 2014) The weight calculated in this way are greater than zero and sum to one. The estimate of weights obtained by cell declustering method is not unique and depends
Figure 2.5 Polygonal declustering technique and declustering weights (Abzalov, 2016) by which declustering weights are assigned proportional to the area of polygons.
Figure 2.5 Polygonal declustering technique and declustering weights (Abzalov, 2016) by which declustering weights are assigned proportional to the area of polygons.
Figure 2.6 Data pairing and h-scatterplots for three separation distances (Abzalov, 2016) “h” along a particular direction. These diagrams are known as h-scatterplots (Figure 2.6).
Figure 2.6 Data pairing and h-scatterplots for three separation distances (Abzalov, 2016) “h” along a particular direction. These diagrams are known as h-scatterplots (Figure 2.6).
Nugget effect, sill and range are the principal features of the variogram (Figure 2.9).
Nugget effect, sill and range are the principal features of the variogram (Figure 2.9).
Figure 2.9 Principal components of a variogram (Rossi & Deutsch, 2014) At the origin where the separation distance (h) is zero the value of the semivariogram is
Figure 2.9 Principal components of a variogram (Rossi & Deutsch, 2014) At the origin where the separation distance (h) is zero the value of the semivariogram is
representing variogram values for different distances (Figure 2.11). Figure 2.11 Variogram map (left) and contour map(right) showing anisotropy directions (Abzalov, 2016) alternative tool to quantify spatial anisotropy is the variogram map which is a 2D diagram
representing variogram values for different distances (Figure 2.11). Figure 2.11 Variogram map (left) and contour map(right) showing anisotropy directions (Abzalov, 2016) alternative tool to quantify spatial anisotropy is the variogram map which is a 2D diagram
Figure 2.13 represents three hypothetical error distributions. Mean of the residuals for the
Figure 2.13 represents three hypothetical error distributions. Mean of the residuals for the
and variance of error distribution are good checks on symmetry and spread, respectively. It is also preferable that the error distribution is symmetric and has a small spread. Mediar for the histogram on the right so no bias is evident. Mean absolute error and mean squared error are two summary statistics incorporating both bias
and variance of error distribution are good checks on symmetry and spread, respectively. It is also preferable that the error distribution is symmetric and has a small spread. Mediar for the histogram on the right so no bias is evident. Mean absolute error and mean squared error are two summary statistics incorporating both bias
between drillholes or linked to form wireframes (Figure 2.16). Inverse distance weighted is a weighted estimation method where weights are inversely
between drillholes or linked to form wireframes (Figure 2.16). Inverse distance weighted is a weighted estimation method where weights are inversely
The kriging system can be written in matrix notation as:
The kriging system can be written in matrix notation as:
Figure 2.17 Comparison of interpolation and simulation estimates (Rossi & Deutsch, 2014) characteristics (i.e., reproduces the variogram).
Figure 2.17 Comparison of interpolation and simulation estimates (Rossi & Deutsch, 2014) characteristics (i.e., reproduces the variogram).
V, G stands for increasingly larger support.
V, G stands for increasingly larger support.
Figure 2.19 Misclassification due to information effect (Vann et al., 2003) validation techniques can be utilized in examination of goals of the study as a goal-oriented toc
Figure 2.19 Misclassification due to information effect (Vann et al., 2003) validation techniques can be utilized in examination of goals of the study as a goal-oriented toc
Figure 2.20 Relationship between exploration results, mineral resources and ore reserves (JORC Code, 2012) and measured in order of increasing level of geological knowledge and confidence (Figure 2.2 In all of the jurisdictions mentioned above, resources are classified as inferred, indicated
Figure 2.20 Relationship between exploration results, mineral resources and ore reserves (JORC Code, 2012) and measured in order of increasing level of geological knowledge and confidence (Figure 2.2 In all of the jurisdictions mentioned above, resources are classified as inferred, indicated
Volume is the first aspect considered in resource classification and it represents a certain
Volume is the first aspect considered in resource classification and it represents a certain
Local geology of the study area comprises a variety of rock types that formed as a result of
Local geology of the study area comprises a variety of rock types that formed as a result of
Figure 3.2 Geological Map of the Study Area
Figure 3.2 Geological Map of the Study Area
ophiolitic mélange, respectively. contact of Paleozoic metamorphic rocks and carbonaceous units belonging to Upper Cretaceous
ophiolitic mélange, respectively. contact of Paleozoic metamorphic rocks and carbonaceous units belonging to Upper Cretaceous
Figure 3.4 SE-NW geological cross section southwest of the deposit. Middle Miocene volcanics are the second most abundant unit and particularly observable towards
Figure 3.4 SE-NW geological cross section southwest of the deposit. Middle Miocene volcanics are the second most abundant unit and particularly observable towards
Figure 4.1 Drillhole Locations the drill grid is adjusted to NE-SW trend based on the attitude of the deposit. regular grid with an approximate grid resolution of 25 m. Considering the attitude of the deposit,
Figure 4.1 Drillhole Locations the drill grid is adjusted to NE-SW trend based on the attitude of the deposit. regular grid with an approximate grid resolution of 25 m. Considering the attitude of the deposit,
Figure 4.2 Validation of drillholes collar positions
Figure 4.2 Validation of drillholes collar positions
Table 4.1 Summary statistics for all the raw and composite data for the raw data due to non-regular sample lengths ranging from 0.4 m to 9.0 m.
Table 4.1 Summary statistics for all the raw and composite data for the raw data due to non-regular sample lengths ranging from 0.4 m to 9.0 m.
Table 4.2 Summary statistics for raw and composite data in length-weighted scenario
Table 4.2 Summary statistics for raw and composite data in length-weighted scenario
Table 4.3 Summary statistics for primary protolith and ore types It is clear based on all these observations and analysis that the database is composed of a
Table 4.3 Summary statistics for primary protolith and ore types It is clear based on all these observations and analysis that the database is composed of a
enriched zone during geological logging. Figure 4.4 Estimation domains in a SE-NW cross-section
enriched zone during geological logging. Figure 4.4 Estimation domains in a SE-NW cross-section
all composites. is indicated. Based on that the capped assay values only represent a small percentage (0.64%) of
all composites. is indicated. Based on that the capped assay values only represent a small percentage (0.64%) of
grades for gold, silver, copper and zinc near the oxide-sulfide boundary.
grades for gold, silver, copper and zinc near the oxide-sulfide boundary.
one of the cross-sections along the chosen orientation. Digitized contacts along NW-SE cross-sections belonging to the estimation domain:
one of the cross-sections along the chosen orientation. Digitized contacts along NW-SE cross-sections belonging to the estimation domain:
Figure 5.2 Digitized contacts along orthogonal directions (left) and solid model of HGS zone generated by explicit modelling approach (right) lines. Figure 5.2B is an illustration of the solid model belonging to high-grade sulfide zone
Figure 5.2 Digitized contacts along orthogonal directions (left) and solid model of HGS zone generated by explicit modelling approach (right) lines. Figure 5.2B is an illustration of the solid model belonging to high-grade sulfide zone
Figure 5.3 Solid model of HGS zone generated by explicit modelling (left) and implicit modelling (right) approaches
Figure 5.3 Solid model of HGS zone generated by explicit modelling (left) and implicit modelling (right) approaches
Therefore, indicator variogram results are considered as satisfactory for subsequent analysis. noteworthy that this approach was effective to capture the major attitude of the mineralization.
Therefore, indicator variogram results are considered as satisfactory for subsequent analysis. noteworthy that this approach was effective to capture the major attitude of the mineralization.
Figure 5.5 Solid Models for HGS zone generated with explicit modelling, implicit modelling and indicator kriging approaches Sorresponding to each estimation domain are generated as separate 3D wireframes.
Figure 5.5 Solid Models for HGS zone generated with explicit modelling, implicit modelling and indicator kriging approaches Sorresponding to each estimation domain are generated as separate 3D wireframes.
Figure 5.6 Bar chart of the volumes for HGS solids generated with alternative approaches below presents the volumes for HGS domain for alternative modelling approaches. larger than volumes generated by both explicit and implicit modelling approaches. Figure 5.
Figure 5.6 Bar chart of the volumes for HGS solids generated with alternative approaches below presents the volumes for HGS domain for alternative modelling approaches. larger than volumes generated by both explicit and implicit modelling approaches. Figure 5.
10m is decided as the horizontal block dimensions. Moreover, vertical block dimension is chosen
10m is decided as the horizontal block dimensions. Moreover, vertical block dimension is chosen
orientations is consistent with the main attitude of the deposit.
orientations is consistent with the main attitude of the deposit.
Final step in the geostatistical analysis of the data is back-transformation of the outcomes.
Final step in the geostatistical analysis of the data is back-transformation of the outcomes.
procedure in a cross-section. nteger values. Figure 6.3 is an illustration for the outcomes of mentioned resource classification As presented in the cross-section, such a procedure resulted in resources classified as
procedure in a cross-section. nteger values. Figure 6.3 is an illustration for the outcomes of mentioned resource classification As presented in the cross-section, such a procedure resulted in resources classified as
Figure 6.4 Histogram of Cu grade estimates for original model vs. alternative models indicates consistent results achieved by original resource model and two alternative models.
Figure 6.4 Histogram of Cu grade estimates for original model vs. alternative models indicates consistent results achieved by original resource model and two alternative models.
performance of the estimation method with respect to alternatives.
performance of the estimation method with respect to alternatives.
Figure 6.6 Swath plot of Cu grades for original model vs. alternative models
Figure 6.6 Swath plot of Cu grades for original model vs. alternative models
Figure 6.7 Visual validation of resource model in the input data. igure suggests that block grade estimates largely capture the grades and the spatial relationships
Figure 6.7 Visual validation of resource model in the input data. igure suggests that block grade estimates largely capture the grades and the spatial relationships
kriging particularly evident in the histogram for high-grade sulfide.
kriging particularly evident in the histogram for high-grade sulfide.
Table 6.5 Updated process recoveries
Table 6.5 Updated process recoveries
include associated insurance, transportation and port charges.
include associated insurance, transportation and port charges.
Figure 6.10 An illustration of the ultimate pit limits on a SE-NW cross-section parameters using the Lerchs-Grossmann algorithm is presented in Figure 6.10. $14.5/t, respectively. (processing costs includes general and administrative costs).
Figure 6.10 An illustration of the ultimate pit limits on a SE-NW cross-section parameters using the Lerchs-Grossmann algorithm is presented in Figure 6.10. $14.5/t, respectively. (processing costs includes general and administrative costs).
ralculated as 4.33 and 30.49 million tons for oxide and sulfide zones, respectively (Table 6.7).
ralculated as 4.33 and 30.49 million tons for oxide and sulfide zones, respectively (Table 6.7).
Table 7.1 Measured and indicated resources for alternative scenarios
Table 7.1 Measured and indicated resources for alternative scenarios
Table 7.2 Measured, indicated and inferred resources suggested some preliminary sources for discrepancies. However, a further analysis is needed tc
Table 7.2 Measured, indicated and inferred resources suggested some preliminary sources for discrepancies. However, a further analysis is needed tc
Figure 7.1 Comparison of HGS domain generated with alternative modelling approaches the first and most important part of the local comparison. Visual inspection and comparison of estimation domains along cross-sections comprises
Figure 7.1 Comparison of HGS domain generated with alternative modelling approaches the first and most important part of the local comparison. Visual inspection and comparison of estimation domains along cross-sections comprises
approaches followed. comparison of average mean grade estimates for precious and base metals for alternative modelling utilized for local comparison purposes (Figure 7.2). The plots presented below facilitates local
approaches followed. comparison of average mean grade estimates for precious and base metals for alternative modelling utilized for local comparison purposes (Figure 7.2). The plots presented below facilitates local
Figure 7.3 Misclassification of the central block treatment charges among different material types, misclassification has pronounced economic
Figure 7.3 Misclassification of the central block treatment charges among different material types, misclassification has pronounced economic
Figure 7.4 Ultimate pit limits for alternative modelling approaches in section view degree of dissimilarity between the outcomes is clearly a function of the data density.
Figure 7.4 Ultimate pit limits for alternative modelling approaches in section view degree of dissimilarity between the outcomes is clearly a function of the data density.
Figure 7.5 Ultimate pit limits for alternative scenarios in map view
Figure 7.5 Ultimate pit limits for alternative scenarios in map view
ratio. domain modelling decision also has implications in terms of ore, waste tonnages and stripping
ratio. domain modelling decision also has implications in terms of ore, waste tonnages and stripping
the implications of the modelling choice in term of economic considerations. Figure 7.7 above clearly shows the significant discrepancies between the pit values of
the implications of the modelling choice in term of economic considerations. Figure 7.7 above clearly shows the significant discrepancies between the pit values of
Figure A.3 HGS-LGS contact plots
Figure A.3 HGS-LGS contact plots
Figure A.5 HGS-BW contact plots
Figure A.5 HGS-BW contact plots
Figure C.3 3D models of barren wall rock (BW)
Figure C.3 3D models of barren wall rock (BW)
Figure C.6 3D models of enriched zone (ENR)
Figure C.6 3D models of enriched zone (ENR)
Table D.1 Measured, indicated and inferred resources for oxide zone Table D.2 Measured, indicated and inferred resources for sulfide zone
Table D.1 Measured, indicated and inferred resources for oxide zone Table D.2 Measured, indicated and inferred resources for sulfide zone
Figure E.2 Comparison of Gossan zone generated with alternative modelling approaches
Figure E.2 Comparison of Gossan zone generated with alternative modelling approaches
Related topics:
Computer ScienceConditional SimulationGeological ModellingResource EstimationIndicator Kriging
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