Maximizing Discoveries: Deploying AI and Decision-Making in Mineral Exploration Strategies abstract
This article examines and contrasts two fundamental paradigms in artificial intelligence (AI) and decision-making: data-driven and
Knowledge-based approaches. It aims to explore their applications, effectiveness, and significance across various problem-solving contexts, providing insight into their unique attributes and performance capacities. The research seeks to assist researchers and decision-makers in selecting or integrating the most suitable methods tailored to specific application needs. in mineral exploration, the integration of contemporary concepts like AI alongside conventional decision-making approaches is crucial. The exploration landscape often confronts two predominant paradigms: knowledgebased and data-driven methodologies, shaping research endeavors and technological advancements.
Knowledge-based methodologies involve leveraging existing expertise, domain-specific knowledge, and established principles to inform decision-making processes within mineral exploration. These approaches prioritize qualitative insights, geological theories, and empirical observations to facilitate informed decisionmaking.On the other hand, data-driven methodologies emphasize empirical data analysis, statistical modeling, and computational techniques to extract actionable insights from vast datasets. These approaches prioritize quantitative analysis of geological data using machine learning algorithms, data mining techniques, and spatial analytics to identify patterns indicative of mineralization. Hybrid methodologies, integrating both paradigms, aim to enhance exploration efficacy and decision-making capabilities. By combining domain expertise with advanced computational tools, these methodologies mitigate uncertainties and expedite the discovery process. Overall, the juxtaposition of knowledge-based and data-driven methodologies underscores the multifaceted nature of mineral exploration, offering diverse avenues for technological innovation. As the field continues to evolve, the adoption of hybrid methodologies is poised to catalyze transformative advancements, facilitating the discovery of new mineral deposits and sustainable resource utilization
