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Minerals
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Application of Big Data Mining, Machine Learning and Artificial Intelligence...
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Application of Big Data Mining, Machine Learning and Artificial Intelligence in Geoscience, 2nd Edition

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2195

Special Issue Editors

Prof. Dr. Yongzhang Zhou

E-Mail Website
Guest Editor
School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
Interests: big data mining, machine learning and mathematical geoscience; ore deposit-related geochemistry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hui Yang

E-Mail Website
Guest Editor
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
Interests: GIS applications; remote sensing; digital earth; environmental monitoring; disaster management and response
Dr. Xiaohui Ji

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Interests: artificial intelligence; mineral identification; high performance computing

Special Issue Information

Dear Colleagues,

Big data and machine learning have brought the study of geoscience onto the artificial intelligence research stage. Big data mining, machine learning and artificial intelligence algorithms and models have been applied to study multi-scale and multi-type geoscientific observation and exploration. The goal of this Special Issue is to highlight recent progress in the research and applications of big data and machine learning in the field of geoscience.

Prof. Dr. Yongzhang Zhou
Prof. Dr. Hui Yang
Dr. Xiaohui Ji
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial intelligence
  • big data mining
  • mineral
  • petrologic geology
  • ore deposit
  • mineral resources prediction
  • remote sensing
  • geochemical exploration

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Related Special Issue

Published Papers (3 papers)

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Research

25 pages, 4754 KiB  
Article
A “Pipeline”-Based Approach for Automated Construction of Geoscience Knowledge Graphs
by Qiurui Feng, Ting Zhao and Chao Liu
Minerals 2024, 14(12), 1296; https://doi.org/10.3390/min14121296 (registering DOI) - 21 Dec 2024
Abstract
With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about locations, geographical concepts, geological characteristics of mineral [...] Read more.
With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about locations, geographical concepts, geological characteristics of mineral deposits, and relationships. Efficient management of these Earth Science data is crucial for the development of digital earth systems, rational planning of resource industries, and resource security. By representing entities, relationships, and attributes through graph structures, knowledge graphs capture and present concepts and facts about the real world, facilitating efficient data management. However, due to the highly specialized and complex nature of Earth Science data and disciplinary differences, the methods used to construct general-purpose knowledge graphs cannot be directly applied to building knowledge graphs in the field of geological science. Therefore, this paper summarizes a “pipeline” approach to constructing an Earth Science knowledge graph in order to clarify the complete construction process and reduce barriers between data and technology. This approach divides the construction of the Earth Science knowledge graph into two parts and designs functional modules under each part to specify the construction process of the knowledge graph. In addition to proposing this approach, a knowledge graph of iron ore deposits is automatically constructed by integrating geographic and geological data related to iron ore deposits using deep learning techniques. The systematic approach presented in this paper reduces the threshold for constructing geological science knowledge graphs, provides methodological support for specific disciplines or research objects in Earth Science, and also lays the foundation for the construction of large-scale Earth Science knowledge graphs that combine crowdsourcing and expert decision-making, as well as the development of intelligent question-answering systems and intelligent decision-making systems covering the entire field of Earth Science. Full article
(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Geoscience, 2nd Edition)
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19 pages, 11777 KiB  
Article
Optimization of Feature Selection in Mineral Prospectivity Using Ensemble Learning
by Hong Zhang, Miao Xie, Shiyao Dan, Meilin Li, Yunhe Li, Die Yang and Yuanxi Wang
Minerals 2024, 14(10), 970; https://doi.org/10.3390/min14100970 - 26 Sep 2024
Viewed by 633
Abstract
In recent years, machine learning (ML) has been extensively used for the quantitative prediction of mineral resources. However, the accuracy of prediction models is often influenced by data quality, feature selection, and algorithm limitations. This research investigates the benefits of data-driven feature optimization [...] Read more.
In recent years, machine learning (ML) has been extensively used for the quantitative prediction of mineral resources. However, the accuracy of prediction models is often influenced by data quality, feature selection, and algorithm limitations. This research investigates the benefits of data-driven feature optimization techniques in enhancing model accuracy. Using the Lhasa region in Tibet as the study area, this research applies ensemble learning methods, such as random forest and gradient boosting tree techniques, to optimize 43 feature variables encompassing geology, geochemistry, and geophysics. The optimized feature variables are then input into a support vector machine (SVM) model to generate a prospectivity map. The performance characteristics of the SVM, RF_SVM, and GBDT_SVM models are evaluated using ROC curves. The results indicate that the feature-optimized GBDT_SVM model achieves superior classification accuracy and prediction effectiveness, demonstrating that feature optimization is a necessary step for mineral prospectivity mapping, as it can significantly improve the performance of mineral prospectivity prediction. Full article
(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Geoscience, 2nd Edition)
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20 pages, 57677 KiB  
Article
Application of Target Detection Based on Deep Learning in Intelligent Mineral Identification
by Luhao He, Yongzhang Zhou and Can Zhang
Minerals 2024, 14(9), 873; https://doi.org/10.3390/min14090873 - 27 Aug 2024
Viewed by 1053
Abstract
In contemporary society, rich in mineral resources, efficiently and accurately identifying and classifying minerals has become a prominent issue. Recent advancements in artificial intelligence, particularly breakthroughs in deep learning, have offered new solutions for intelligent mineral recognition. This paper introduces a deep-learning-based object [...] Read more.
In contemporary society, rich in mineral resources, efficiently and accurately identifying and classifying minerals has become a prominent issue. Recent advancements in artificial intelligence, particularly breakthroughs in deep learning, have offered new solutions for intelligent mineral recognition. This paper introduces a deep-learning-based object detection model for intelligent mineral identification, specifically employing the YOLOv8 algorithm. The model was developed with a focus on seven common minerals: biotite, quartz, chalcocite, silicon malachite, malachite, white mica, and pyrite. During the training phase, the model learned to accurately recognize and classify these minerals by analyzing and annotating a large dataset of mineral images. After 258 rounds of training, a stable model was obtained with high performance on key indicators such as Precision, Recall, mAP50, and mAP50–95, with values stable at 0.91766, 0.89827, 0.94300, and 0.91696, respectively. In the testing phase, using samples provided by the Geological and Mineral Museum at the School of Earth Sciences and Engineering, Sun Yat-sen University, the model successfully identified all test samples, with 83% of them having a confidence level exceeding 87%. Despite some potential misclassifications, the results of this study contribute valuable insights and practical experience to the development of intelligent mineral recognition technologies. Full article
(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Geoscience, 2nd Edition)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: Natural Language Processing and Machine Learning Based Approach for Clustering Analysis of Mining Accident Narratives

Authors: Abid Ali Khan Danish, Snehamoy Chatterjee, and Kumar Vaibhav Raj

2. Title: Enhanced Classification of Gold Ore: A Comparative Study of Machine Learning Techniques Using Texture and Chemical Data

Authors: Fabrizzio Rodrigues Costa*, Cleyton de Carvalho Carneiro, Carina Ulsen

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