Akademik Veri Yönetim Sistemi
36th International Conference of Jangjeon Mathematical Society (ICJMS’2025), Xian, Çin, 20 - 25 Ağustos 2025, ss.1-5, (Tam Metin Bildiri)
The significant increase in demand for lead and zinc ore in
industrial sectors over recent years has accelerated scientific and technical
studies aimed at exploring existing and potential mineral deposits.
To sustainably meet this growing demand, it’s crucial to discover new
mineralizations and effectively and efficiently determine the potential
of existing deposits. In this context, the development and application
of various methodologies used in the characterization and resource estimation
processes of mineral deposits play a critical role. Among these
methodologies, geostatistical methods stand out as powerful tools for
understanding the spatial distribution and geochemical characteristics
of mineralization. Geostatistics is a mathematical and statistical discipline
developed for the analysis and modeling of spatially dependent
data. These methods are used to estimate the distribution of a variable
within a geological environment and to quantify the uncertainty
of these estimations. In this study, geochemical analysis data from the
Almila Pb-Zn deposit, located in the Alada˘g region of Kayseri, T¨urkiye,
a significant mining area, were evaluated using various geostatistical
methods. Within the scope of the study, descriptive statistical analyses
were performed to define the geochemical characteristics of the
deposit. Additionally, multivariate geostatistical techniques such as regression
analysis and Principal Component Analysis (PCA) were applied
to determine the relationships between different geochemical elements
and their contributions to mineralization. Through these comprehensive
geostatistical analyses, the mathematical explainability of the geochemical
processes influencing the formation of the Almila Pb-Zn deposit
was demonstrated, and significant insights into the deposit’s potential
and geochemical control factors were obtained. The findings will shed
light on future exploration and evaluation efforts, contributing to the
optimization of resource estimation processes.