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AI in mineral exploration

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The recent copper discovery by KoBold Metals in Zambia illustrates the important of AI and reassessing historic data. Image credit: Leon Louw for WhyAfrica

AI in mineral exploration

The recent copper discovery by KoBold Metals illustrates the important of AI and reassessing historic data.      

By Dr. Nicolaas Steenkamp

KoBold Metals, backed by tech giants like Bill Gates and Jeff Bezos, recently announced a potentially game-changing copper discovery in Zambia.

This monumental find, estimated to be worth billions, highlights the increasingly important role of Artificial Intelligence (AI) in mineral exploration.

This potential discovery shows the critical importance of reassessing historic data and combining it with AI methodologies when exploring for significant deposits in highly prospective regions.

Historic data as the foundation

KoBold’s success hinges on its unique approach to data utilisation. Unlike traditional exploration methods relying solely on physical surveys, KoBold leverages a vast repository of historic geological data.

This includes geophysical surveys, drilling logs, geochemical data, and satellite imagery, spanning decades and covering large geographical areas.

By amassing and integrating this data, KoBold creates a rich tapestry of the subsurface, revealing potential mineral deposits invisible to the naked eye.

AI in mineral exploration

Once acquired, the true power lies in extracting meaningful insights from this massive dataset. This is where AI was extensively utilised. KoBold employs complex machine learning algorithms trained on specific geological signatures associated with mineral deposits.

These algorithms sift through the data, identifying subtle patterns and relationships that might escape human analysis. This allows KoBold to predict the presence and location of ore bodies with greater accuracy and efficiency.

Utilising specific AI methodologies   

Specifically, KoBold utilises several AI methodologies. The first is deep learning where convolutional neural networks analyse geophysical and satellite imagery, identifying anomalies indicative of mineralised zones.

This is followed by Bayesian optimisation, as this method iteratively refines exploration targets based on drilling results, continuously improving prediction accuracy.

Finally generative models are created, that simulated geological scenarios, allowing KoBold to assess the potential of unexplored areas even before drilling commences.

Exploration in “Elephant Country”

Sceptics have however referred to the discovery as “hunting for elephants in elephant country”, where abundant resources make discovery almost inevitable.

While Zambia is known for its copper wealth associated with the Central African Copperbelt, pinpointing specific, high-grade deposits within a vast geological landscape remains a complex challenge. AI serves as a sophisticated tool, not a magic wand.

The key for the discovery is the data quality and quantity. Historical data can be incomplete, inaccurate, or inconsistent. AI algorithms require high-quality, standardised data for optimal performance.

In addition, the deposit comes with a high degree of geological complexity.

Mineral deposits form under diverse and intricate geological processes. Identifying these intricacies from data demands powerful AI models and expert geological interpretation.

It also needs to consider the economic viability, as even a large deposit might not be economically viable due to factors like depth, accessibility, and environmental considerations. AI can’t determine these aspects on its own.

Transformational potential

Overall, KoBold’s discovery underscores the transformative potential of AI in mineral exploration by harnessing historic data and employing sophisticated AI methods.

However, the method still requires detailed scrutiny of the outputs by skilled geologists. KoBold has potentially unearthed a significant copper resource.

It is important to remember though that AI is a tool, not a guaranteed path to success. Geological expertise, data quality, and economic considerations remain essential for translating potential into reality.

AI in mineral exploration


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AI in mineral exploration

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