Geoscientists, Except Hydrologists and Geographers

In geoscience work today, AI mostly helps with data-heavy tasks. For example, big mining companies use AI tools to scan drill‐hole images and build 3D models of underground rock and ore ^[1]. Smart software also analyzes large survey or seismic data sets faster than a person could, spotting patterns and even drawing maps or charts automatically. (For instance, Reuters reports that major mines use AI to optimize operations and process ore ^[2].) However, many parts of the job still need human skill.

Geoscientists go into the field with seismographs, magnetometers, and other equipment to measure Earth’s properties ^[3], and writing reports or teaching remain things only people do well right now. In short, today’s AI is more of a helper or “copilot” for geologists on complex calculations and big data ^[1], while humans focus on the tricky decisions and communication.

Whether geology teams adopt AI quickly depends on several factors. Mining and energy firms deal with huge data and often have big budgets, so they’re already moving faster on new tech. There’s also a looming shortage of geoscientists – one report projects about 27% will retire by 2029, leaving the U.S. short some 130,000 geologists ^[1].

This pension wave makes companies eager to try AI to boost productivity ^[1]. On the other hand, building good AI models can be expensive and requires cleaned-up data, so smaller projects may move more slowly. Geology also values safety and correctness, meaning people often double-check AI outputs.

Still, interest is growing: governments even fund AI for critical mineral surveys, and industry sees real gains. In the end, industry experts agree AI can save time and help with the heavy lifting, but human geologists are needed to interpret and verify the results ^{[1] [2]}.