The Role of AI in Mineral Exploration: Separating Hype from Reality

Introduction

Artificial intelligence (AI) has emerged as a transformative force in the mining sector, particularly in mineral exploration. By integrating technologies like subsurface modeling, core logging automation, and digital twins, AI is driving efficiency, sustainability, and innovation. Yet, the challenges of data quality, geological complexity, and workforce shortages persist. In this blog, we explore the real-world impact of AI on mineral exploration, its limitations, and how it is paving the way for a more sustainable and efficient future in mining.

Real Applications of AI in Mineral Exploration

AI is transforming the mining sector through its ability to process vast amounts of data and deliver actionable insights. Key applications include:

• Subsurface Modeling for Sustainability: Seequent’s tools, such as Leapfrog Geo and Oasis montaj, enable precise geological models that reduce unnecessary drilling and minimize environmental impact. At Cornish Lithium, AI-driven models were used to drill a single exploration borehole, significantly reducing waste and water usage (1).

• Tailings Management with Digital Twins: OceanaGold’s partnership with Seequent and Bentley Systems resulted in a digital twin for monitoring its Waihi Tailings Storage Facility in New Zealand. This allows real-time data tracking to manage environmental risks, such as changes in core pressure and groundwater levels during significant rainfall events (1).

• Core Logging Automation: Canadian company GeologicAI has pioneered AI-powered core logging and robotics, funded by Breakthrough Energy Ventures. Their technology streamlines the tedious process of core analysis, providing geologists with near-instantaneous precision data. This has proven effective in complex deposits like porphyry copper and gold, improving energy efficiency and reducing CO₂ emissions by 5.3% in pilot projects (1).

• Data-Driven Decisions: According to the Geoprofessionals Data Management Report 2023, over 70% of respondents in subsurface industries, including mining, view data management as critical to their operations. However, 57% cite unmanaged historical data as a challenge, and nearly a quarter report lacking sufficient information for data-driven decision-making. AI-powered solutions can bridge these gaps, enabling more effective exploration workflows (2).

Addressing Challenges: Separating Hype from Reality

While AI holds transformative potential, its implementation faces key challenges:

• Data Integration and Quality: AI relies on large volumes of accurate, high-quality data. Mining operations often face issues with fragmented datasets or outdated information.

• Complexity of Geological Variables: Mineral exploration involves numerous factors that require human expertise to interpret, particularly in complex deposits.

• Skills Shortage in Mining: With an impending shortfall in geoscientists, tools like Seequent’s Visible Geology aim to inspire interest in geosciences by providing engaging, interactive platforms for students (1).

Rob Ferguson, Director of Exploration and Resource Management at Seequent, highlights that AI complements human expertise, empowering geoscientists to focus on higher-value activities while reducing the environmental impact of operations (1).

Advancing Sustainability Through AI

AI is playing a pivotal role in making mining more sustainable:

• Precision in Exploration: By integrating subsurface data and predictive modeling, AI reduces waste streams and optimizes resource extraction, as demonstrated by Cornish Lithium’s innovative practices (1).

• Minimized Environmental Risks: Digital twins and AI-based monitoring tools enhance safety and environmental management, particularly in sensitive areas like tailings facilities (1).

• Adoption of Advanced Analytics: Nearly two-thirds of respondents to the Geoprofessionals Data Management Survey either use or are considering AI, machine learning, and advanced analytics to address critical challenges in exploration (2).

Conclusion

Artificial intelligence is proving to be a valuable ally in mineral exploration, enhancing data analysis, workflow efficiency, and sustainability. However, its success lies in complementing traditional methods and human expertise. By addressing challenges in data quality and integration, and embracing its potential for sustainable innovation, AI can drive meaningful advancements in mineral exploration.

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References:

1. AI Goes Underground, Institute of Materials, Minerals & Mining, May 13, 2024.

2. Geoprofessionals Data Management Report 2023, Seequent.

3. AI in Mining: Transforming Exploration Processes, Mining Journal.

4. Digital Twins in Mining, Global Mining Review.

5. Predictive Analytics in Mining Exploration, Mining Technology.

6. The Role of AI in Mining Sustainability, International Council on Mining and Metals.