TL;DR:
- Miners have been embracing new ideas and technology throughout history, and now they are leveraging AI and machine learning to enhance mineral exploration.
- New technologies help miners locate untapped mineral resources by exploring new areas and utilizing advanced analytical capabilities.
- The paper “Predicting new mineral occurrences and planetary analog environments via mineral association analysis” highlights the potential of AI and machine learning in uncovering critical mineral resources.
- The development of a machine learning model using a massive mineral evolution database has successfully predicted previously unknown mineral occurrences.
- Collaborations between technology startups, mining companies, and scientific organizations are actively utilizing AI and machine learning to locate valuable mineral deposits.
- AI and machine learning have the potential to transform not only mineral exploration but also the oil and gas industry, improving efficiency and reducing costs.
Main AI News:
The mining industry, often seen as a sluggish behemoth in the realm of modern industrialization, has emerged as a leader in embracing new ideas and cutting-edge technology. Throughout its extensive history spanning millennia and spanning the globe, miners have consistently leveraged innovative technologies to enhance their ability to locate and extract Earth’s valuable resources.
In the distant past, miners transitioned from rudimentary sticks to sophisticated metal-based tools in their tireless quest for minerals. Today, the mining industry seeks new frontiers and untapped territories, relying on groundbreaking technologies to make significant discoveries. Universities, governments, technology development companies, and mining giants relentlessly explore novel concepts that can aid explorers in pinpointing lucrative mineral deposits.
The fusion of artificial intelligence (AI) and machine learning, a subset of AI, has emerged as a fascinating prospect in the mineral exploration landscape. Recent research findings published in the paper titled “Predicting new mineral occurrences and planetary analog environments via mineral association analysis” underscore the tremendous potential of AI and machine learning in uncovering critical mineral resources. The paper highlights the invaluable role these technologies play in comprehending and interpreting the vast and ever-expanding mineral data resources accumulated over the years.
By harnessing the multidimensional analytical capabilities of machine learning, miners and their consultants can embark on a data-driven strategy for mineral discovery. This approach entails gaining a deeper understanding of the known associations between mineral species and distinct geological settings, enabling the identification of new mineral deposits. The authors of the study affirm that these predictive association methods hold the promise of shedding light on the origins of minerals within the context of their tectonic, environmental, and potentially microbiological settings, revealing the interdependence of the geosphere and biosphere.
The highlight of the study was the development of a machine learning model utilizing an extensive mineral evolution database comprising 295,583 mineral localities and 5,478 mineral species. Leveraging association rules, this model successfully predicted previously unknown mineral occurrences. Significantly, the model identified areas brimming with promising reserves of critical battery minerals and rare earth elements, offering a glimpse into the vast untapped potential lying beneath the Earth’s surface.
Various industry and scientific organizations are actively exploring the advantages AI and Machine Learning can provide in the mining sector. One notable collaboration emerged in 2021 when Bill Gates and Jeff Bezos, through their Silicon Valley-based startup KoBold Metals, partnered with BHP Group, the world’s largest mining company. This partnership aims to utilize advanced technology in locating battery metal deposits, focusing on a vast area in Western Australia. The search primarily targets copper and nickel deposits buried at depths ranging from 650 to 5,000 feet beneath the surface.
Another recent example involves Australian critical minerals specialist Tivan, which joined forces with EARTH AI to explore the Sandover lithium project in the Northern Territory. EARTH AI, equipped with a specialist geological team, proprietary cloud computing, and an integrated geological AI review and machine learning process, supports Tivan’s efforts in the Sandover project. The vast area covered by the project, approximately 8,000 square kilometers, shows high potential for hosting lithium-bearing pegmatites as well as sediment-hosted copper and iron oxide copper gold (IOCG) deposits.
The transformative power of AI and machine learning extends beyond mineral exploration. Global Artificial Intelligence specialist AI World School asserts that these technologies will undoubtedly revolutionize the mining sector. Already, companies are implementing AI and machine learning to enhance the efficiency and success rate of mineral prospecting. AI’s ability to map geographic locations from multiple data inputs assists in identifying mineral-rich areas, while geologists leverage machine learning algorithms to unearth valuable ores. Furthermore, AI’s processing prowess enables the analysis of vast amounts of historical data, empowering the identification of new targets and the prediction of potential mineral deposits within the Earth’s crust.
Drones, when coupled with AI technology, also play a pivotal role in gathering aerial data, processing it, and surveying potential exploration sites. The integration of AI and machine learning extends beyond mining; the oil and gas industry also benefits significantly. GlobalData, a leading international research firm, highlights the potential for machine learning to transform the oil and gas sector. These technologies can analyze seismic data, well logs, and other geological information to unveil hidden oil and gas reservoirs. Machine learning algorithms can also analyze production data to identify patterns that optimize well performance, leading to enhanced efficiency, increased production, and reduced costs within the industry.
The Organisation for Economic Co-operation and Development (OECD) predicts that AI could contribute up to $16 trillion to the world’s GDP by 2030, surpassing 10% of the global gross product. Resource companies worldwide are increasingly embracing AI technologies to improve their odds of success in exploration endeavors. As the mining industry continues to evolve, AI and machine learning will undoubtedly remain at the forefront, unlocking the vast potential of Earth’s resources and propelling the industry into a new era of discovery and efficiency.
Conclusion:
The integration of AI and machine learning technologies in the mining industry represents a significant leap forward in mineral exploration. The ability to analyze vast amounts of data, predict new mineral occurrences, and optimize exploration efforts holds immense value for resource companies. These advancements not only enhance the efficiency and success rate of exploration but also have the potential to drive economic growth. As AI and machine learning continue to evolve, the mining market can expect increased precision, reduced costs, and the unlocking of previously untapped mineral reserves, leading to a more sustainable and profitable future.
