Development of an AI-Driven Platform for Real-Time Mineral Deposit Prediction

Thesis Abstract

The detection and evaluation of mineral deposits remain critical components in mineral exploration, yet traditional methodologies are often hindered by significant time delays, high operational costs, and limited predictive accuracy amidst complex geological settings. Addressing these challenges, this study aims to develop an AI-driven platform capable of real-time mineral deposit prediction, thereby enhancing exploration efficiency and decision-making precision. The specific objectives include (1) identifying key geological and geophysical data attributes relevant to mineral deposit prediction, (2) designing and implementing machine learning algorithms tailored for mineral exploration data, (3) integrating these algorithms into a user-friendly, real-time predictive platform, and (4) validating the platform's performance using historical exploration datasets and field validation. The research adopts a mixed-methods approach grounded in a pragmatic research design. Quantitative data collection involved compiling a comprehensive dataset comprising 1,200 geological, geochemical, geophysical, and satellite remote sensing variables from 150 previously explored mineral deposit sites across the region. These data were sourced from governmental geological surveys, exploration companies, and remote sensing agencies. Qualitative insights were gathered through semi-structured interviews with five expert geologists and data scientists specializing in mineral exploration to inform feature selection and algorithm design. Data pre-processing included normalization and feature engineering, which prepared the dataset for machine learning applications. Machine learning algorithms such as Random Forests, Support Vector Machines, and Gradient Boosting Machines were trained and validated using 80% of the dataset, with model performance assessed via cross-validation techniques, specifically k-fold validation (k=10), and evaluation metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve. To incorporate spatial-temporal dynamics, the platform was developed using Python for backend processing integrated with a Geographic Information System (GIS) interface, enabling real-time visualization of predictive results. The theoretical framework guiding this study is rooted in the Spatial Data-Driven Prediction Theory and Learning Theory, with the latter emphasizing adaptive learning models based on continuous data input to improve predictive accuracy over time. Key expected findings include demonstrating that machine learning models, particularly Gradient Boosting Machines and Random Forests, can achieve over 85% accuracy in predicting mineral deposit locations in a real-time operational environment. The platform is anticipated to facilitate spatially explicit, probabilistic deposit forecasts that outperform conventional geostatistical methods in both speed and predictive reliability. It is also expected that feature importance analysis will reveal that mineralogical, geophysical, and remote sensing attributes significantly influence deposit likelihood, consistent with established geological understanding. This research contributes novel insights into the integration of advanced artificial intelligence techniques within geoscientific exploration workflows, providing a scalable, real-time decision support tool that can adapt to diverse geological terrains and data types. It advances the application of machine learning in mineral deposit prediction, filling gaps identified in prior studies that often relied on static models and limited datasets. The findings will serve as a foundation for the development of more autonomous mineral exploration systems and promote data-driven exploration strategies in resource geology. The main conclusions highlight the efficacy of AI and machine learning in revolutionizing mineral exploration processes, with the developed platform demonstrating potential to reduce exploration costs by at least 30% and improve cost-efficiency in deposit identification. The study recommends further research focusing on the integration of additional data streams—such as hyperspectral imaging and deep learning techniques—and expanding the platform's application across different mineral commodities and geographical regions. Future studies should also explore the deployment of the platform using cloud computing environments to facilitate broader adoption in industry-wide applications, ultimately contributing to more sustainable and economically viable mineral resource development.