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How can artificial intelligence be integrated into mineral exploration workflows?

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Artificial intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence, such as learning, reasoning, and decision making. AI has the potential to transform mineral exploration workflows by enhancing data analysis, interpretation, and integration, as well as reducing costs, risks, and environmental impacts. In this article, we will explore some of the ways that AI can be integrated into mineral exploration workflows and the benefits and challenges of doing so.

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Michael McClintock, P.Eng.

Founder & Principal @ McClintock Group| Co-Founder EOC | Enhancing Mineral Projects, Advocating for Shareholders

1 Data acquisition and processing

One of the main applications of AI in mineral exploration is to improve the quality and efficiency of data acquisition and processing. AI can help to automate, optimize, and standardize the collection, storage, and transmission of various types of exploration data, such as geophysical, geochemical, geological, and remote sensing data. AI can also help to process and clean the data, removing noise, outliers, and errors, and enhancing the resolution, accuracy, and reliability of the data.

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Michael McClintock, P.Eng.

Founder & Principal @ McClintock Group| Co-Founder EOC | Enhancing Mineral Projects, Advocating for Shareholders
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One aspect often missed with the conversation of AI and exploration is the need for fit for purpose. Exploration stages and activities can range extensively from grass roots to advanced targeting. The amount of data that is ideal for various stages will matter.

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Kwaku Boakye, PhD, MBA

Regional Mining Manager @ Heidelberg Materials | PhD in Systems Engineering | Mining | Process Optimization | Sustainability | CSEP | SME-RM
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The time and cost of exploration can be reduced by using AI and data analytics to help discover areas with substantial mineralization potential in a safe and timely manner. In order to enhance voice and picture recognition, deep neural networks and other AI techniques can be used. This will raise the process's quantity and quality of ore extraction. By using historical data to develop and train the models, predictive models can be made to help guide the placement of exploratory holes in the target's accuracy position and decrease errors.

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2 Data analysis and interpretation

Another important application of AI in mineral exploration is to enhance the analysis and interpretation of the data. AI can help to extract meaningful patterns, trends, and insights from large and complex datasets, using techniques such as machine learning, deep learning, neural networks, and natural language processing. AI can also help to integrate and correlate data from different sources and scales, creating comprehensive and holistic models of the subsurface. AI can also help to generate and test hypotheses, evaluate uncertainties, and provide recommendations and predictions.

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3 Data visualization and communication

A third application of AI in mineral exploration is to improve the visualization and communication of the data. AI can help to create interactive and dynamic maps, graphs, charts, and images that illustrate the data in a clear and intuitive way. AI can also help to generate reports, summaries, and narratives that explain the data in a simple and understandable language. AI can also help to facilitate the collaboration and feedback among different stakeholders, such as explorers, managers, investors, regulators, and communities.

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4 Data management and security

A fourth application of AI in mineral exploration is to ensure the management and security of the data. AI can help to organize, archive, and access the data in a systematic and efficient way, using cloud computing, blockchain, and smart contracts. AI can also help to protect the data from unauthorized access, tampering, or loss, using encryption, authentication, and backup systems. AI can also help to comply with ethical, legal, and social standards and regulations regarding the data.

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5 Benefits and challenges of AI integration

Integrating AI into mineral exploration workflows can provide several advantages, such as increasing productivity, reducing costs, and optimizing decision making. It can also enhance the knowledge and skills of exploration personnel and create new opportunities for exploration companies. However, this integration also comes with some challenges. It requires high investments, infrastructures, and expertise to implement and maintain AI systems. Additionally, there may be technical, operational, and cultural barriers to adopt and adapt AI systems. Data availability, quality, compatibility, ownership, privacy, and security must also be managed. Last but not least, ethical, legal, and social implications must be addressed when using AI systems.

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Francisco Rubina Retamal

Strategic Planning and Analysis BA at Glencore
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Delve into AI's pivotal role in optimizing mineral exploration methodologies. It is demonstrated from the experience of mining success cases that by examine AI-powered techniques for data analysis, predictive modeling, and target delineation, it is possible to enhance precision and efficiency in the business. Evaluate practical applications, addressing technical nuances and regulatory considerations it is also a paramount of the industry. Finally, but not less important, propose a roadmap for AI adoption and future research directions, positioning this synergy between mining and AI as the most valuable potential hidden value to be discovered.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can artificial intelligence be integrated into mineral exploration workflows?

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1 Data acquisition and processing

2 Data analysis and interpretation

3 Data visualization and communication

4 Data management and security

5 Benefits and challenges of AI integration

6 Here’s what else to consider

Mining Engineering

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How can artificial intelligence be integrated into mineral exploration workflows?

1

2

3

4

5

6

1 Data acquisition and processing

2 Data analysis and interpretation

3 Data visualization and communication

4 Data management and security

5 Benefits and challenges of AI integration

6 Here’s what else to consider

Mining Engineering

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