Automatic description of rock thin sections: A web application

The identification and characterization of rock types is a core activity in geology and related fields, including mining, petroleum, environmental science, industry, and construction. Traditionally, this task is performed by human specialists who analyze and describe the type, composition, texture, shape, and other properties of rock samples, whether collected in-situ or prepared in a laboratory. However, the process is subjective, dependent on the specialist’s experience, and time-consuming. This study proposes an artificial intelligence-based approach that combines computer vision and natural language processing to generate both textual and verbal descriptions from images of rock thin sections. A dataset of images and corresponding textual descriptions is used to train a hybrid deep learning model. Features extracted from the images using EfficientNetB7 are processed by a Transformer network to generate textual descriptions, which are then converted into speech using a speech synthesis service. The experimental results show an accuracy of 0.892 and a BLEU score of 0.71. This model offers potential utility for research, professional, and academic applications and has been deployed as a web application for public use. • The distinctive aspects of AI can achieve an automatic description system from images of thin rock sections by reducing the heavy human subjective load and time-consuming work process. • The creation of a mixed dataset linking 5,600 images of rock thin sections organized into 14 categories according to rock type and their respective textual descriptions. • We developed an interactive online platform where users can upload their own images of rock thin sections and evaluate the textual and verbal responses provided by the model. • The ability to convert text to speech has been added, allowing not only textual, but also verbal results, which can aid people with visual limitations. • The implementation technologies for this web application include the GitHub, Streamlit and Wix platforms, all of which are freely available.