Imagine a world where the secrets hidden deep within the Earth’s crust are no longer shrouded in mystery. A world where technology can predict the location of valuable mineral deposits with unprecedented accuracy. This is not the realm of science fiction; it’s the reality of modern mineral exploration, thanks to the revolutionary advancements in Artificial Intelligence (AI).
Mineral exploration is essentially a step-by-step process in area reduction and increase in knowledge. Each step of the process is as much focused on where not to look as much as where to focus the exploration budget.
For centuries, mineral exploration has relied on a combination of intuition, experience, and traditional methods like geological mapping and drilling. While these techniques have served us well, they come with inherent limitations—time-consuming processes, high costs, and a significant degree of uncertainty. Enter AI, the game-changer that is transforming the landscape of mineral exploration.
Over the past 30 years, AI and automation has evolved from a nascent technology to a cornerstone of mineral exploration. Early applications were limited to basic data analysis, data display and pattern recognition. Today, AI encompasses a wide range of sophisticated algorithms and machine learning models that can process vast amounts of geological data, identify patterns, and predict mineral deposits with remarkable accuracy.
AI is now being used in various stages of mineral exploration, from initial prospecting to detailed resource modelling. Here are some of the cutting-edge applications:
- Prospecting and Targeting: AI algorithms analyse geological, geochemical, and geophysical data to identify potential mineral-rich areas. Companies like ALS Globals’ Geoanalytics unit successfully predicted 86% of the Abitibi Gold Belt’s gold resources using AI, based on data from just 4% of the region’s surface area.
- Geological Modelling: AI models create 3D representations of subsurface geology, helping geologists visualize mineral deposits and plan drilling programs more effectively. AI can be used to develop optimised drill plans with sufficient spacing patterns to achieve a desired level of mineral resource confidence. This reduces exploration costs and increases the chances of discovery.
- Drill Core Analysis: AI-powered image recognition systems analyse drill core samples, identifying mineralisation and structure patterns, providing real-time feedback to exploration teams. This accelerates the decision-making process and improves exploration efficiency.
- Remote Sensing: AI algorithms process satellite imagery and aerial surveys to detect mineral signatures that are invisible to the naked eye. When various datasets are layered together and analysed in combination, this technology allows exploration companies to cover vast areas quickly and cost-effectively.
- Predictive Analytics: AI models use historical exploration data to predict the likelihood of finding mineral deposits in unexplored areas. This helps companies prioritise their exploration efforts and allocate resources more effectively.
- Historical Data Capture and Cross Referencing: AI is being used to review hundreds of years of journals and scanned historic records and reports to identify potential targets that have been looked at by different groups over long periods of time.
Several companies have already embraced AI and achieved remarkable success in mineral exploration:
1. ALS Global – Geoanalytics: As mentioned earlier, ALS has developed technologies that include machine assisted mapping, automated logging and geophysical inversions to generate anomaly matching and pattern detection models for focussing on targets faster.
2. Fleet Space: Adelaide-based Fleet Space’s ExoSphere solution has been used to conduct hundreds of surveys across five continents for global mining leaders like Barrick Gold and Rio Tinto. They use space, geophysics and AI to map the subsurface down to 2.5km depth.
3. VerAI Discoveries: Boston-based VerAI is using AI and machine learning to accelerate the detection of economically significant critical mineral deposits. Their innovative approach focuses on uncovering hidden deposits in covered areas that traditional methods cannot see through. Their business model is then to partner with explorers to test the models in return for royalties or project sale revenue.
The future of AI in mineral exploration is bright and full of possibilities. As technology continues to advance, we can expect even more sophisticated AI models and applications. Here are some potential future trends:
1. Explainable AI: The development of explainable AI models will enhance transparency and trust in AI-driven exploration. These models will provide insights into how AI makes predictions, allowing geologists to better understand and validate the results.
2. Integration with Other Technologies: AI will be integrated with other emerging technologies like drones, autonomous vehicles, and advanced sensors to create a comprehensive exploration toolkit. This will further enhance exploration efficiency and accuracy.
3. Sustainable Exploration: AI can help reduce the environmental impact of mineral exploration by optimising exploration processes and minimising waste. This aligns with the growing emphasis on sustainable mining practices.
4. Global Collaboration: AI-driven exploration will facilitate global collaboration among mining companies, research institutions, and governments. Shared data and resources will accelerate the discovery of new mineral deposits and drive innovation in the industry.
The integration of AI into mineral exploration is not just a technological advancement; it’s a paradigm shift that is reshaping the industry. By harnessing the power of AI, exploration companies can uncover hidden treasures beneath the Earth’s surface with unprecedented precision and efficiency.
As we look to the future, the synergy between geology and artificial intelligence promises to unlock new frontiers in mineral exploration and secure the resources needed for a sustainable future.
Photo by vackground.com on Unsplash
