AI Boosts Geology: How Russian Scientists Cut Mineral Analysis Time by Thousands of Times

A Scientific Breakthrough in Analytical Geology

Russian researchers have introduced an innovative web service capable of automatically identifying microminerals based on Raman scattering spectra, also known as Raman spectroscopy. This non-destructive method allows scientists to analyze mineral composition in samples ranging from terrestrial rocks to extraterrestrial materials. The platform’s key achievement is reducing analysis time from several months to just a few minutes. The result is driven by a combination of artificial intelligence, machine-learning algorithms, and high-speed indexed data search using the Faiss library.

The service relies on an original phase analysis algorithm (RPA) that decomposes complex spectra into components corresponding to individual minerals. The integration of neural network models delivers identification accuracy of about 96%. This approach not only radically accelerates geological analysis but also makes it far more objective by minimizing human bias.

Why It Matters for Russia

The development carries strategic importance. First, it strengthens Russia’s scientific and technological autonomy in analytical geology. Until now, domestic researchers and companies have depended on foreign software solutions such as Crystal Sleuth or the RRUFF database. The new service is a fully Russian alternative capable of replacing imported tools.

Second, the technology directly improves the efficiency of geological exploration. Rapid analysis enables faster evaluation of deposits containing copper, gold, lithium, and other strategically important resources. Over time, it could accelerate the discovery of new reserves that are critical to the country’s technological sovereignty.

When a mineral is large and tangible, it can be identified simply by learning to distinguish it from others. Many macroscopic features, such as streak color and luster, allow most minerals to be identified by eye. But we work at much smaller scales, where macroscopic diagnostic methods no longer apply

Sergey Smirnov

Doctor of Geological and Mineralogical Sciences and Deputy Director for Research at the Institute of Geology and Mineralogy of the Siberian Branch of the Russian Academy of Sciences

Finally, the tool has potential applications in space research. Raman spectroscopy is already used aboard planetary missions, including NASA’s Perseverance rover on Mars. A Russian service adapted for streaming data from orbital and surface spectrometers could become part of future planetary science missions.

Global Context and Export Potential

Globally, interest in AI-driven mineral identification is growing. Since 2022, a rising number of studies have focused on automated mineral analysis using deep learning. In 2025, comparable tools such as ArDI, designed for pegmatite analysis, appeared. However, the Russian service stands out due to its integrated approach, combining neural networks, fast data search, and an open architecture.

This creates clear export potential. As a software-as-a-service platform, it could be adopted by research centers, mining companies, and universities worldwide, especially in countries pursuing digital transformation of their geological services.

Challenges and the Road Ahead

Moving from a laboratory prototype to industrial deployment will not be straightforward. Key challenges include validating AI models across a broad range of minerals, including rare and complex phases, and building open, representative spectral databases. Many existing spectral collections remain fragmented or proprietary, limiting algorithm generalization.

Another critical factor is compatibility with international standards. To become part of the global scientific infrastructure, the Russian service must support widely accepted data formats and APIs.

The Future Is Already Taking Shape

Over the next three to five years, developers plan to expand the database, integrate the service with laboratory equipment and geographic information systems, and adapt it for field use. In the medium term, the technology could be deployed on portable Raman spectrometers already used by geologists at exploration sites. In the longer term, it may evolve into multimodal AI systems combining Raman data with infrared spectroscopy and X-ray diffraction.

The new web service is more than a utility. It signals a shift in Russian science toward a new paradigm, one in which artificial intelligence becomes an integral part of fundamental research. It is a step toward the future of digital geology that is faster, more precise, and more independent.