Design and evaluate a GIS-based mineral deposit prediction model

Thesis Abstract

The increasing demand for mineral resources coupled with the rising costs and environmental concerns associated with traditional exploration methods necessitates the development of efficient and reliable predictive tools for mineral deposit exploration. This study aims to design, implement, and evaluate a Geographic Information System (GIS)-based mineral deposit prediction model to improve targeting accuracy and resource allocation. The specific objectives include identifying and integrating relevant geological, geochemical, geophysical, and remote sensing datasets; developing a spatial predictive model using machine learning algorithms within a GIS framework; and assessing the model’s predictive performance relative to existing geostatistical approaches. The research adopts a mixed-methods design, combining quantitative spatial analysis with qualitative validation processes. The population comprises geological and geophysical datasets from mineral-rich zones within the study area, a region covering approximately 10,000 square kilometers with diverse mineral occurrences. A stratified random sampling technique was employed to select 50 mineral deposit sites for validation, supplemented by secondary data sources obtained from national geological agencies and satellite imagery archives. Data collection instruments include high-resolution multispectral satellite images, airborne geophysical survey data, geochemical sampling reports, and geological maps. The core methodology involves constructing a comprehensive GIS database by georeferencing and integrating multi-source datasets. Advanced spatial analysis techniques, including principal component analysis (PCA) and weighted overlay, are employed to identify key mineralization indicators. Machine learning algorithms such as Random Forest and Support Vector Machine (SVM) classifiers are trained on a subset of confirmed mineral sites to generate predictive models. Model validation involves cross-validation and receiver operating characteristic (ROC) curve analysis, with performance metrics like area under the curve (AUC) and confusion matrices utilized to evaluate predictive accuracy. Expected findings suggest that the GIS-based model will outperform traditional geostatistical methods in identifying prospective mineral zones, with an anticipated AUC exceeding 0.85 and an overall accuracy above 80%. The model is projected to delineate high-potential mineralized zones with spatial precision, thereby reducing exploration costs and improving resource discovery efficacy. The study also aims to identify critical geospatial predictors, highlighting their relative importance in mineral deposit localization. This research makes a significant contribution to the body of knowledge by demonstrating the integration of machine learning techniques within a GIS framework for mineral exploration, advancing predictive modeling methodologies in geology. The findings will provide a replicable approach adaptable to various mineralization contexts globally, emphasizing the value of spatial analytics combined with computational intelligence. The study’s key theoretical underpinning includes the theory of spatial dependence and the predictive analytics paradigm, which together inform the model design and interpretation. The main conclusion asserts that GIS-based predictive models, when properly integrated with relevant datasets and validated rigorously, can substantially enhance mineral exploration strategies. Recommendations include adopting such models in routine exploration workflows, investing in high-quality geospatial data acquisition, and fostering interdisciplinary collaborations between geologists and data scientists to refine predictive algorithms further. Future research should explore the incorporation of new data sources such as drone-based remote sensing and advanced neural network models to enhance predictive capabilities and generalizability across different geological settings.