Identification and measurement of minerals on thin sections images of intrusive igneous rocks using Deep Learning *S.D. Paucar1 1Department of Geology, Central University of Ecuador, Ecuador, (*Presenting author: stalynpaucar271828@gmail.com) ABSTRACT Artificial Intelligence (AI) has moved beyond theory and is now applied in everyday contexts such as politics, economics, medicine, social networks, web browsers, and academic research. Since the early 21st century, Deep Learning has also been increasingly applied within the Earth Sciences. In this research, the Detectron2-v0.6 model was used to identify eight minerals on intrusive igneous rock thin sections, while the scikit-image library was employed to obtain quantitative measurements of the identified minerals. The methodology follows a standard AI model workflow. A total of 400 images were collected from the British Geological Survey and The Open University databases, and 62178 minerals were labeled in the Roboflow platform. These data were used to create four datasets for training and validation, along with an external evaluation dataset of 20 images. All experiments were conducted using Google Colab. The best performance was achieved using the XPL-10X dataset, with a total loss of 1.15 and an average precision (AP, IoU = 0.5-0.95) of 15.7%. Scikit-image showed relative errors between 0-3%, suitable for quantitative analysis. An interactive application was developed in Google Colab and Hugging Face-Streamlit, allowing users to upload images and generate automatic PDF reports with mineral segmentation, identification, mineralogical analysis, and crystal size distribution histograms. KEYWORDS Minerals, Intrusive igneous rocks, Deep Learning, Convolutional Neural Network, Detectron2 1. CONTEXT AND PROBLEM STATEMENT In the last decade, the AI model with the greatest impact has been the Convolutional Neural Network (CNN), whose rise is mainly due to the use of Graphics Processing Units (GPUs) (Bhatnagar, 2023). Koeshidayatullah et al. (2020), using the VGG16 and InceptionResNetV2 models, automated the petrography of carbonate rocks. In sandstones, Liu et al. (2022) employed the ResNet, DenseNet, Mask-RCNN, Generative Adversarial Network (GAN), and Path Aggregation Network (PANet) models to identify minerals, analyze porosity, weathering, sorting, roundness, and grain contacts. Currently, three open-source models represent the state of the art in object segmentation and/or identification: Detectron2, SAM, and YOLO. Sitar & Leary (2023) use Detectron2 to segment zircons, while Zaki et al. (2023) applied it to the segmentation of alite and belite in clinker. Azzam et al. (2024) developed GrainSight, based on SAM, for grain segmentation in sandstones, and the company DiUS, together with Solve Geosolutions, implemented Datarock to analyze drill
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