Researchers at the Paul Scherrer Institute PSI have shown that artificial neural networks have the potential to determine very precisely the characteristics of rock layers, like their mineralogical composition, solely on the basis of drill core images. This could speed up future geological investigation efforts while simultaneously optimising costs.
Underground investigations are often time-consuming and costly. Yet without knowledge of the properties and characteristics of the layers located deep below the surface, many important questions cannot be answered: Can data for future explorations around the deep geological repository be predicted quickly and reliably? Is a particular underground site suitable for obtaining deep geothermal heat and power, or for extracting natural gas? Are the geological conditions at a depth of 1,500 metres suitable for storing carbon dioxide? To make it easier to answer these and other questions, Romana Boiger, from the Laboratory for Waste Management in the PSI Center for Nuclear Engineering and Sciences, is working to establish new tools from the area of artificial intelligence for geological investigations.
Boiger’s attention is focused on so-called artificial neural networks in particular. These consist of several layers of interconnected artificial neurons. These are, in the final analysis, mathematical formulas that process input data and deliver a result. What makes this special is that artificial neural networks are capable of learning. For example, an artificial neural network that is supposed to distinguish between apples and pears can be trained by presenting it with images of apples and pears and simultaneously providing the correct interpretation. After a certain number of training runs, the artificial neural network is then prepared to correctly classify even unfamiliar pictures of apples and pears.
In her research, Boiger, a mathematician with a focus on data science and machine learning, uses a special type of artificial neural networks called convolutional neural networks (CNNs). These are especially well suited to the identification and analysis of patterns and simple features in images.
Scientifically uncharted territory
One novel application of CNNs is the subject of the study Boiger and colleagues published in May 2024 in the Swiss Journal of Geosciences. It is the result of an interdisciplinary collaboration between scientists from PSI and experts in geology and engineering at Nagra. In a first step, they used CNNs to analyse images of drill cores taken from the Trüllikon borehole in Northern Switzerland. This was part of Nagra’s site investigation programme to identify a suitable site for a deep geological repository. The test interval was selected from 55 metres of drill core from a depth of between 770 and 939 metres. «We wanted to find out if it’s possible to accurately determine the lithological formations and above all the mineralogical composition of the rock – such as the proportions of calcite, clay, and silicates – solely on the basis of drill core images. » Studies already exist to investigate the lithology, determining properties that can be observed with the naked eye, without the help of a microscope. On the other hand, determining mineralogy in this way is scientifically uncharted territory. «No one had ever done it this way before.»
For her research, Boiger used artificial neural networks that had already been trained. They had previously learned to distinguish between images of vehicles, animals, people, and fruit – as well as geological formations and rocks – using images from the ImageNet database, a collection of more than 14 million images.
The CNN models thus already had a certain knowledge base when they were presented with the Trüllikon drill cores. The 10 cm thick drill cores from various geological units, known as formations, were systematically photographed after washing. The photographs were then cut into slices. Boiger and colleagues proceeded step by step: They expanded the pre-trained CNN by a few layers, which they then specifically trained to distinguish between lithological formations on the basis of the images. This resulted in a new, larger CNN model. It was then expanded again by a few layers – and finally trained to recognise the mineralogical composition.
Read more on PSI website
Image: Romana Boiger wants to use artificial intelligence to improve the exploration of deep earth layers and the analysis of drill cores.
Credit: Paul Scherrer Institute PSI/Markus Fischer