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The demand for critical minerals is expected to quadruple by 2040

  • The informative dossier, developed by Xcalibur Smart Mapping based on official sources, shows how the application of artificial intelligence can manage natural resources, anticipate risks, minimize impacts, and build resilience in a transitioning energy system.
  • The application of AI in this field becomes a strategic approach to anticipating the future and better managing the present of the energy transition.
  • 800 billion dollars are needed in the critical minerals sector to reach the net-zero emissions target by 2040.

The demand for critical minerals will quadruple by 2040. This is one of the key findings of the First Xcalibur Smart Mapping Informative Dossier, titled “Artificial intelligence, critical minerals, and sustainable transformation”, developed by Xcalibur Smart Mapping, the global leader in natural capital mapping solutions. Drawing on its expertise and a range of official sources, the report highlights the geological, energy, and technological challenges facing the industry, as well as the transformative role that artificial intelligence (AI) plays in the current energy transition landscape.

The dossier notes that the growing demand for critical minerals is being driven by the development of key technologies such as batteries, electric transportation, and solar energy. To meet the net-zero emissions target by 2040, an investment of 800 billion dollars in the critical minerals sector will be required, according to the International Energy Agency.

The document presents artificial intelligence as a strategic tool for the energy transition. Its application is not only intended to optimize processes but also to transform the way we explore, plan, and manage the planet’s natural resources. AI enables us to anticipate future changes and manage present-day resources more efficiently. In fact, this key enabler extends beyond the energy sector: the dossier highlights that, according to EY’s AI Pulse Survey, 97% of companies that invested in AI in 2024 reported positive returns.

“It is not about technology replacing scientific knowledge, but rather enhancing and scaling it up. Technological integration does not replace experience, it multiplies it. It allows for the development of strategic solutions that improve exploration efficiency, reduce environmental impact, and reinforce traceability. It accelerates the identification and valuation of essential resources—key to European autonomy and sustainability—and transforms data into strategic decisions for a fairer and more resilient future,” highlights Jorge Urios, R&D Director of Xcalibur Smart Mapping.

Transforming geological exploration with AI

The application of artificial intelligence in geological exploration marks a turning point in the way we locate and assess subsurface resources. Unlike conventional methods, which rely on manual interpretation, long lead times, and high uncertainty, new approaches integrate advanced sensors, machine learning, and predictive models that transform millions of data points into actionable knowledge.
AI can identify patterns invisible to the human eye, anticipate risks, optimize resources, and accelerate real-time decision-making. Its implementation improves efficiency and reduces environmental impact by eliminating invasive drilling and prolonged campaigns.

With the need for critical minerals projected in the coming decades, the dossier prepared by the global leader in natural capital mapping solutions underscores the need to adopt technologies that turn data into strategic decisions.

As a result, both work times and financial costs are drastically reduced.
For example, the company’s AI-based tools for aeromagnetic leveling would reduce 150 workdays per year across 30 projects. Meanwhile, the algorithms for magnetic compensation would reduce 80 flights per year, equivalent to approximately 100 tons of CO₂ emissions and a saving of more than $500,000.

“Artificial intelligence and applied knowledge position Xcalibur Smart Mapping as a key player in ecosystem monitoring, territorial management, and environmental traceability. Our solutions go beyond mining: they directly contribute to sustainability and climate action policies,” says Nicolas Leiva, Head of AI at Xcalibur Smart Mapping.

The dossier prepared by Xcalibur Smart Mapping concludes that AI is not just a set of algorithms, but a strategic tool for guiding decisions with a real impact on the planet’s sustainability. Thanks to these algorithms, it is possible to better explore, prioritize areas with a smaller environmental footprint, reduce improvisation, and increase transparency for public and private entities, investors, and communities. Furthermore, the data obtained allows for better management of natural resources, the design of more effective public policies, and the guidance of responsible investments.

For this reason, Xcalibur Smart Mapping has taken on a relevant role in the energy transformation with an approach based on AI, advanced sensors and predictive models that not only respond to the demand for efficiency, but also incorporate a systemic vision: exploring better, with more information, less impact and greater purpose.

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Project Description

 

GEMA is an 18-month project (August 2023 – February 2025) that aims to develop innovative solutions by integrating artificial intelligence in the interpretation of geophysical data. Its main objective is the creation of models based on deep neural networks that combine multiple data sources, such as geophysics (magnetic, radiometric and hyperspectral), satellite imagery and digital terrain models. These models will allow the generation of ultra-high-resolution maps, improving the accuracy and speed of geological interpretation.

 

The project aims to reduce the time and costs associated with the manual interpretation of large volumes of data, improving the efficiency of exploration processes. GEMA is led by Xcalibur and is part of a wider initiative focused on the development of artificial intelligence applied to geophysics.

 

Proyecto GEMA

 

Project Results and Milestones Achieved

  1. Optimisation of Geophysical Data using Tie Level (Tabular Structure)

The Tie Level tool has optimised the correction of artefacts in geophysical data through an approach based on Bayesian models and spatial RBF (Radial Basis Function Interpolation). Its implementation has significantly reduced the need for manual adjustments, providing more homogeneous and accurate results in less time.

Milestones achieved:

 

More than 250,000 geophysical data points in the study regions have been corrected, reducing processing time by 40 % compared to traditional methods.

The accuracy of the interpolation models was improved by 15%, increasing the reliability of the data used in exploration.

Geographical scope of implementation:

  • Finland
  • Mongolia
  • France

 

  1. Detection of Anomalies in Magnetic Lineaments with Computer Vision (CNN Lineaments)

The CNN Lineaments tool has been developed for the automatic detection of geological structures using Computer Vision techniques and segmentation of geophysical images. Its implementation has optimised the interpretation of magnetic lineaments, facilitating the identification of structural anomalies.

Milestones achieved:

Identification of more than 1,200 magnetic lineaments in the study areas, with a 35 % reduction in the need for manual corrections.

Successful application of the model in Nigeria and La Guajira (Colombia), where its effectiveness has been validated with 90 % accuracy in anomaly detection.

Geographical scope of implementation:

  • Karamoja, Uganda
  • Katanga, Democratic Republic of Congo (DRC)
  • Nigeria (model validation)
  • La Guajira, Colombia (model validation).

 

  1. Magnetic Variation Correction in Directional Drilling

Magnetic variation correction tools have been developed to improve the accurate positioning of boreholes in exploration and resource extraction. This has optimised borehole orientation, reducing errors and increasing operational efficiency. In addition, work has been done on estimating electromagnetic fields in locations without measurement stations, facilitating data acquisition in remote areas.

Milestones achieved:

Improved borehole positioning accuracy by 25 %, reducing borehole trajectory errors.

Generation of an electromagnetic field estimation model that has enabled data collection in more than 20 locations without measurement stations in Canada.

 

Geographical scope of implementation:

The model has been trained and validated using data from the following regions in Canada, where Directional Drilling activity requires high accuracy:

  • Alberta (majority of validation stations).
  • British Columbia (Wells Gray)
  • Saskatchewan (Weyburn, Rabbit Lake)
  • Northwestern Territories (Fort Simpson, Fort Smith)

 

Funding

Total EU funding allocated: 1,206,071.80 €.

Total project budget: 1,418,908.00 €.

 

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