Dr. Mahdi Panahi, researcher in the fields of remote sensing, artificial intelligence, and hyperspectral image analysis—and the inventor of the "Intelligent Hyperspectral Drill Core Scanner"—has been consistently listed among the top 2% of scientists worldwide from 2022 to 2025, according to Stanford University's report. This technology is the outcome of his research collaboration with the University of Tehran within the framework of the Connect program, at the invitation of Dr. Mehdi Hasanlou, Dean of the Faculty of Surveying and Geospatial Engineering. The initial idea for this system took shape during this collaboration, and Dr. Hasanlou's guidance and support throughout all stages of its development played a decisive role in advancing the project. This collaboration facilitated the transfer of international experience to address the country's technological needs. In this context, the following interview was conducted with him.

Where did the idea for this technology originate?

The idea for this technology emerged from a real and long-standing need in the mining industry and geological studies. In many exploration projects, the analysis of drill cores is still based on visual observation, manual description, conventional photography, and the geologist's experience. While these methods are valuable, they are time-consuming, dependent on human interpretation, and limited in terms of reproducibility. On the other hand, critical exploration decisions—from determining alteration zones and selecting suitable samples for testing to deciding whether to continue or halt drilling and modeling the deposit—require data that is accurate, rapid, standardized, and archivable. It was precisely this gap between industry needs and available tools that drove us to develop a system capable of analyzing drill cores in a non-destructive, digital, automated, and data-driven manner.

How does this system work?

This system operates by integrating hyperspectral imaging (using the HYSPIM device from the knowledge-based company Partov Afzar Sanat), a precise motion mechanism, controlled lighting, and artificial intelligence algorithms. Drill cores are placed in standard trays, and the hyperspectral camera scans their surface with high precision using a line-scanning imaging method. The device's output is not an ordinary image but rather a hyperspectral data cube; this means that for every point on the core's surface, in addition to spatial position, a unique spectral signature is recorded. This information enables the identification of lithology, mineralogy, alteration, oxidation, silicification, and textural changes.

In conventional methods, the quality of results depends on the expert's experience and the conditions under which data is recorded, but in this system, data is collected, stored, and processed uniformly and is always subject to reanalysis. It also enables the creation of a digital archive of cores, comparison of samples across different time periods, and direct linkage of results to three-dimensional geological models.

What role does artificial intelligence play in this technology?

Artificial intelligence is the core of this system. After acquiring hyperspectral data, preprocessing steps including spectral correction, noise removal, normalization, and data preparation are performed. Then, machine learning and deep learning models identify spectral and textural patterns and automatically generate mineralogical and lithological maps.

In fact, artificial intelligence performs the same task as an experienced geologist, but at a much higher speed, without fatigue, and with the ability to process thousands of data points simultaneously.

In the laboratory prototype, mineral identification accuracy has exceeded 85%, and with the completion of a spectral library of Iran's native minerals and an increase in training data, further improvement of this accuracy in the industrial version is achievable.

What are the applications of this technology?

This technology has broad applications in mining, oil and gas, and geological studies. In the mining sector, it can be used to identify promising zones, optimize sampling, reduce laboratory costs, and increase the accuracy of deposit modeling. In the oil and gas industry, this system can also perform reservoir core analysis and facies differentiation with high accuracy and speed.

The most important impact of this technology is the reduction of uncertainty in exploration decision-making. When data is accurate, standardized, and comparable, decisions regarding whether to continue or halt drilling, selecting sampling locations, and prioritizing exploration areas will be made with greater confidence.

What are the future plans?

In the first step, we are pursuing the formal registration of the patent to protect the intellectual property of this achievement. At the same time, we have placed the completion of a spectral library of Iran's native minerals on the agenda so that artificial intelligence models can be trained with more realistic data and the system's accuracy can reach the desired industrial level.

Our main goal is to develop a semi-industrial and then industrial version of the device and to expand collaboration with mining companies and entities active in the exploration sector. Ultimately, we want this technology to transition from the laboratory stage to an intelligent decision-support tool in real mining exploration projects in the country—a tool that places Iran on the path of data-driven and modern exploration.

Closing Remarks

This technology was formed at the intersection of international experience and local needs, and the University of Tehran was the platform for this synergy. We believe that the future of the country's mining exploration will be based on data, artificial intelligence, and modern technologies.

"The earth beneath our feet holds countless secrets, and our duty is to develop tools that reveal these secrets in the language of science and data. We hope this step marks the beginning of developing indigenous technologies and strengthening the country's technological independence in the field of earth sciences."

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