Determination of geospatial criteria and prediction of potential areas for porphyry copper deposits in Kazakhstan

Determining spatial parameters related to mineral deposits is crucial for analysing mineral prospectivity. These spatial parameters, called Geospatial Criteria (GC), can serve as input data for modelling mineral prospectivity. Porphyry Copper (Cu) deposits are the major source of the world’s Cu supply, and reliance on these deposits has significantly increased over the past decade. Kazakhstan produces 2%–4% of global Cu production, with most sources are porphyry Cu deposits. This study applies data-driven and knowledge-based overlay techniques to define region-scale GC for porphyry Cu deposits in Kazakhstan to be used for mineral prospectivity modelling. The spatial attributes of highly prospective porphyry Cu deposits in Kazakhstan are systematically integrated into a Geographic Information System (GIS) to identify and evaluate the GC. Regional spatial datasets, including tectonics, lithological, geochronological, gravity, and magnetic data related to porphyry Cu deposits in Kazakhstan are comprehensively compiled and integrated within the GIS environment. An overlay analysis and Venn diagram analysis are performed using anomalous geospatial data. This results in a prospective area map based on GC highlighting targeted zones for porphyry Cu deposits in Kazakhstan. The study presents a framework for mineral prospectivity mapping that is scalable and can be applied to similar geological settings worldwide. It enhances exploration targeting, reduces costs, and supports sustainable resource discovery.

Authors

Institutions

Publication Details

Journal
Frontiers in Earth Science
Published
2025-03-05
DOI
https://doi.org/10.3389/feart.2025.1489969
Citations
2
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
4.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Determination of geospatial criteria and prediction of potential areas for porphyry copper deposits in Kazakhstan

Zohreh Rahnama, Jessica Neafie, Emil Bayramov, Reimar Seltmann et al.
2 citations
Frontiers in Earth Science
Geochemistry and Geologic Mapping
4.96
article

Determination of geospatial criteria and prediction of potential areas for porphyry copper deposits in Kazakhstan

Zohreh Rahnama, Jessica Neafie, Emil Bayramov, Reimar Seltmann, Zahoor Ahmad Shah
article en
2 citations

Abstract

Determining spatial parameters related to mineral deposits is crucial for analysing mineral prospectivity. These spatial parameters, called Geospatial Criteria (GC), can serve as input data for modelling mineral prospectivity. Porphyry Copper (Cu) deposits are the major source of the world’s Cu supply, and reliance on these deposits has significantly increased over the past decade. Kazakhstan produces 2%–4% of global Cu production, with most sources are porphyry Cu deposits. This study applies data-driven and knowledge-based overlay techniques to define region-scale GC for porphyry Cu deposits in Kazakhstan to be used for mineral prospectivity modelling. The spatial attributes of highly prospective porphyry Cu deposits in Kazakhstan are systematically integrated into a Geographic Information System (GIS) to identify and evaluate the GC. Regional spatial datasets, including tectonics, lithological, geochronological, gravity, and magnetic data related to porphyry Cu deposits in Kazakhstan are comprehensively compiled and integrated within the GIS environment. An overlay analysis and Venn diagram analysis are performed using anomalous geospatial data. This results in a prospective area map based on GC highlighting targeted zones for porphyry Cu deposits in Kazakhstan. The study presents a framework for mineral prospectivity mapping that is scalable and can be applied to similar geological settings worldwide. It enhances exploration targeting, reduces costs, and supports sustainable resource discovery.

Frontiers in Earth ScienceVol. 13
Natural History Museum (GB), Anglo American (United Kingdom) (GB), Technische Universität Berlin (DE), Nazarbayev University (KZ)
Openalex Percentile: Top 6%
Geochemistry and Geologic Mapping
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.