Geoscientists, Except Hydrologists and Geographers

Today, AI is helping geoscientists with data-heavy tasks, but humans still lead the work. For example, researchers recently used an AI model to spot a hidden “ring fault” under Italy’s Campi Flegrei volcano by analyzing seismic data ^[1]. In Britain, geologists combined AI with satellite images to identify 3,000 moving landslides nationwide, a task that was too slow to do by hand ^[2].

AI is also used to make maps: the British Geological Survey reports using machine learning to turn complex survey data into detailed soil and mineral maps with accuracy as good as or better than traditional methods ^[2]. These cases show AI taking on pattern-spotting and number-crunching (e.g. analyzing geophysical data or satellite surveys) to find features of interest. However, many tasks still need human creativity and judgment.

Geoscientists interpret the AI results, design field studies, and write reports or teach. In short, AI tools augment geoscientists by handling big data, but they don’t replace the scientists’ expertise ^{[1] [2]}.

Big companies and early adopters see promise in AI, but rollout is gradual. Mining and energy firms have lots of data and budgets for new tech. For instance, a leader at mining giant BHP noted that AI can digest “astonishing amounts of data” to help find mineral resources ^[3].

Startups also report success: one AI-driven exploration tool helped find a large metal deposit in Australia, spending only about \$300,000 to make predictions and drill targets ^[3]. These examples show AI can save time and money in exploration. Still, experts stress it’s early days.

BHP’s president admits “we’re still learning how to take full advantage” of AI ^[3]. Developing effective AI models requires high-quality data and skilled staff, which can be costly. Because geology data is technical rather than personal, there are few legal or privacy concerns, but companies must trust AI’s accuracy.

Overall, as tools improve and prove their value (for example by cutting expensive surveys or finding new resources), we expect more geoscience teams to gradually adopt AI support ^{[3] [3]}.