10
distance from the complex to the block and the number of
drillings involved in obtaining grades for a specific block.
.These parameters add an additional layer of rigor to the
analysis, providing valuable information on the distribution
and quality of minerals in the project, thus contributing to
a more complete and reliable estimate of the resources.
With the abundant information collected, we are in a
position to evaluate the economic viability of the Project
through the analysis of the tonnage-grade curve. This
approach will allow us to determine the economic profit-
ability of the project by exploring the relationship between
the amount of tonnage extracted and the grades of the min-
erals present.
CONCLUSIONS
The modeling of the mineralized structures was carried
out by creating interpolation domains of gold (Au) and
silver (Ag) grades. These domains were customized based
on the specific geological information of each mineralized
structure, taking into account the identified faults, such as
Ninajassa, Uchuro and Azuca.
A categorization of the Spanish veins into families was
carried out, highlighting their importance due to their
significant contribution to the grades present in the min-
eralized structures. This clustering approach allowed a bet-
ter understanding and characterization of vein trends and
behaviors, providing valuable information for analysis and
decision making in the geological context of study.
The resulting categorization, recorded in the “CATEG”
field of the block model, assigns Measured Resources to
two thirds up to 40 meters, Indicated Resources to the last
third (40–60 meters), and classifies as Inferred Resources
the zones between drillholes in the area. mineralized. This
structured approach provides an efficient basis for manage-
ment and informed decision making in mining.
The variography of the project, evaluating priority
veins and systems. The lack of variographic robustness in
the Española B and Española A veins was highlighted due
to the scarcity of data. For the N-NW system, spanning
several veins, ranges of 80 meters for Ag and 40–45 meters
for Au were identified. These results offer a key view of the
spatial distribution of elements in the deposit, informing
about the geological variability in the study area.
Finally, the tonnage-grade curve vs the gold grade (Au)
equivalent of the resources was carried out, these were
grouped by indicated +measured +inferred resources,
resources, measured +indicated and inferred resources, this
separation is given by the level of geological knowledge and
data trust.
REFERENCES
Zaki, M. M., Chen, S., Zhang, J., Fan, F., Хорешок, A. A.,
Mahdy, M. A., &Salim, K. (2022). A novel approach
for resource estimation of highly skewed gold using
machine learning algorithms. Minerals, 12(7), 900.
doi.org/10.3390/min12070900.
De La Torre Palacios, L. (2019). Significance of the “cut
off-average grade- tonnage” sensitivity analysis in min-
ing projects. Dialnet. dialnet.unirioja.es/servlet
/articulo?co digo=7061644.
Bai, Y., Chunying, G., Zhu, P., Tian, J., &Zhang, H.
(2023). Three-Dimensional prediction and evalua-
tion of Baiyanghe uranium deposit in the Xuemistan
Volcanic Belt, Xinjiang. Minerals, 13(11), 1408. doi
.org/10.3390/min13111408.
Boumpoulis, V., Michalopoulou, M., &Depountis, N.
(2023). Comparison between different spatial inter-
polation methods for the development of sediment
distribution maps in coastal areas. Earth Science
Informatics, 16(3), 2069–2087. doi.org/10.1007
/s12145-023-01017-4.
Cantú-González, J. R., Soto- Villalobos, R., Benavides-
Bravo, F. G., Benavides-Ríos, Á. G., Ramírez-Trejos, P.
A., &Aguirre-López, M. A. (2024). A Power-.
Law exponential model for variograms with quick transi-
tion and known range: construction and application
to geostatistical time series. Journal of Computational
and Applied Mathematics, 436, 115405. doi
.org/10.1016/j.cam.2023.115405.
Karpekov, T. (2016). Mineral Resources Estimation with
Data and Parameter Uncertainty. University of Alberta,
35. doi.org/10.7939/r3vt1h12w
distance from the complex to the block and the number of
drillings involved in obtaining grades for a specific block.
.These parameters add an additional layer of rigor to the
analysis, providing valuable information on the distribution
and quality of minerals in the project, thus contributing to
a more complete and reliable estimate of the resources.
With the abundant information collected, we are in a
position to evaluate the economic viability of the Project
through the analysis of the tonnage-grade curve. This
approach will allow us to determine the economic profit-
ability of the project by exploring the relationship between
the amount of tonnage extracted and the grades of the min-
erals present.
CONCLUSIONS
The modeling of the mineralized structures was carried
out by creating interpolation domains of gold (Au) and
silver (Ag) grades. These domains were customized based
on the specific geological information of each mineralized
structure, taking into account the identified faults, such as
Ninajassa, Uchuro and Azuca.
A categorization of the Spanish veins into families was
carried out, highlighting their importance due to their
significant contribution to the grades present in the min-
eralized structures. This clustering approach allowed a bet-
ter understanding and characterization of vein trends and
behaviors, providing valuable information for analysis and
decision making in the geological context of study.
The resulting categorization, recorded in the “CATEG”
field of the block model, assigns Measured Resources to
two thirds up to 40 meters, Indicated Resources to the last
third (40–60 meters), and classifies as Inferred Resources
the zones between drillholes in the area. mineralized. This
structured approach provides an efficient basis for manage-
ment and informed decision making in mining.
The variography of the project, evaluating priority
veins and systems. The lack of variographic robustness in
the Española B and Española A veins was highlighted due
to the scarcity of data. For the N-NW system, spanning
several veins, ranges of 80 meters for Ag and 40–45 meters
for Au were identified. These results offer a key view of the
spatial distribution of elements in the deposit, informing
about the geological variability in the study area.
Finally, the tonnage-grade curve vs the gold grade (Au)
equivalent of the resources was carried out, these were
grouped by indicated +measured +inferred resources,
resources, measured +indicated and inferred resources, this
separation is given by the level of geological knowledge and
data trust.
REFERENCES
Zaki, M. M., Chen, S., Zhang, J., Fan, F., Хорешок, A. A.,
Mahdy, M. A., &Salim, K. (2022). A novel approach
for resource estimation of highly skewed gold using
machine learning algorithms. Minerals, 12(7), 900.
doi.org/10.3390/min12070900.
De La Torre Palacios, L. (2019). Significance of the “cut
off-average grade- tonnage” sensitivity analysis in min-
ing projects. Dialnet. dialnet.unirioja.es/servlet
/articulo?co digo=7061644.
Bai, Y., Chunying, G., Zhu, P., Tian, J., &Zhang, H.
(2023). Three-Dimensional prediction and evalua-
tion of Baiyanghe uranium deposit in the Xuemistan
Volcanic Belt, Xinjiang. Minerals, 13(11), 1408. doi
.org/10.3390/min13111408.
Boumpoulis, V., Michalopoulou, M., &Depountis, N.
(2023). Comparison between different spatial inter-
polation methods for the development of sediment
distribution maps in coastal areas. Earth Science
Informatics, 16(3), 2069–2087. doi.org/10.1007
/s12145-023-01017-4.
Cantú-González, J. R., Soto- Villalobos, R., Benavides-
Bravo, F. G., Benavides-Ríos, Á. G., Ramírez-Trejos, P.
A., &Aguirre-López, M. A. (2024). A Power-.
Law exponential model for variograms with quick transi-
tion and known range: construction and application
to geostatistical time series. Journal of Computational
and Applied Mathematics, 436, 115405. doi
.org/10.1016/j.cam.2023.115405.
Karpekov, T. (2016). Mineral Resources Estimation with
Data and Parameter Uncertainty. University of Alberta,
35. doi.org/10.7939/r3vt1h12w