Minerals

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15 pages, 1710 KiB

Open AccessArticle

A Biological-Inspired Deep Learning Framework for Big Data Mining and Automatic Classification in Geosciences

by Paolo Dell’Aversana

Minerals 2025, 15(4), 356; https://doi.org/10.3390/min15040356 (registering DOI) - 28 Mar 2025

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MycelialNet is a novel deep neural network (DNN) architecture inspired by natural mycelial networks. Mycelia, the vegetative part of fungi, form extensive underground networks that, in a very efficient way, connect biological entities, transport nutrients and signals, and dynamically adapt to environmental conditions. [...] Read more.

MycelialNet is a novel deep neural network (DNN) architecture inspired by natural mycelial networks. Mycelia, the vegetative part of fungi, form extensive underground networks that, in a very efficient way, connect biological entities, transport nutrients and signals, and dynamically adapt to environmental conditions. Drawing inspiration from these properties, MycelialNet integrates dynamic connectivity, self-optimization, and resilience into its artificial structure. This paper explores how mycelial-inspired neural networks can enhance big data analysis, particularly in mineralogy, petrology, and other Earth disciplines, where exploration and exploitation must be efficiently balanced during the process of data mining. We validate our approach by applying MycelialNet to synthetic data first, and then to a large petrological database of volcanic rock samples, demonstrating its superior feature extraction, clustering, and classification capabilities with respect to other conventional machine learning methods. 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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21 pages, 13716 KiB

Open AccessArticle

A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data

by Zhenquan Hang, Tao Xue, Jianping Chen, Yujin Shi, Zehang Yin, Zijia Cui and Guanyun Zhou

Minerals 2025, 15(3), 301; https://doi.org/10.3390/min15030301 - 15 Mar 2025

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Abstract

Three-dimensional (3D) geological models are essential for geological analysis and mineral resource estimation. Although conventional on-site survey methods, such as boreholes, provide local engineering geological information for 3D geological modeling, accurately predicting strata in areas with sparse borehole data remains a challenge. This [...] Read more.

Three-dimensional (3D) geological models are essential for geological analysis and mineral resource estimation. Although conventional on-site survey methods, such as boreholes, provide local engineering geological information for 3D geological modeling, accurately predicting strata in areas with sparse borehole data remains a challenge. This study proposes a 3D geological modeling method using the Transformer model under the conditions of sparse borehole data. First, a K-dimensional tree was used to identify boreholes adjacent to the target point, and a borehole context sequence was constructed using stratigraphic information from neighboring boreholes. Subsequently, the relationship between the target point and its adjacent borehole sequence was calculated using the multi-head attention mechanism of the Transformer model. Finally, trained Transformer encoders were used to predict the stratigraphic category of the target point, and the normalized information entropy was used to quantify uncertainty during the modeling process. Experimental results showed that the accuracy of the method was 0.86, outperforming the accuracy and uncertainty of a recurrent neural network. The root mean square error is smaller than the inverse distance weight and Kriging. Compared to other methods, the proposed method can more accurately describe the geometric shape and distribution of geological bodies and reveal the sedimentary laws of the study area. 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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24 pages, 4725 KiB

Open AccessEditor’s ChoiceArticle

Unlocking Subsurface Geology: A Case Study with Measure-While-Drilling Data and Machine Learning

by Daniel Goldstein, Chris Aldrich, Quanxi Shao and Louisa O’Connor

Minerals 2025, 15(3), 241; https://doi.org/10.3390/min15030241 - 26 Feb 2025

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Abstract

Bench-scale geological modeling is often uncertain due to limited exploration drilling and geophysical wireline measurements, reducing production efficiency. Measure-While-Drilling (MWD) systems collect drilling data to analyze mining blast hole drill rig performance. Early MWD studies focused on penetration rates to identify rock types. [...] Read more.

Bench-scale geological modeling is often uncertain due to limited exploration drilling and geophysical wireline measurements, reducing production efficiency. Measure-While-Drilling (MWD) systems collect drilling data to analyze mining blast hole drill rig performance. Early MWD studies focused on penetration rates to identify rock types. This paper investigates Artificial Intelligence (AI)-based regression models to predict geophysical signatures like density, gamma, magnetic susceptibility, resistivity, and hole diameter using MWD data. The machine learning (ML) models evaluated include Linear Regression (LR), Decision Trees (DTs), Support Vector Machines (SVMs), Random Forests (RFs), Gaussian Processes (GP), and Neural Networks (NNs). An analytical method was validated for accuracy, and a three-tier experimental method assessed the importance of MWD features, revealing no performance loss when excluding features with less than 2% importance. RF, DTs, and GPs outperformed other models, achieving R² values up to 0.98 with a low RMSE, while LR and SVMs showed lower accuracy. The NN’s performance improved with larger datasets. This study concludes that the DT, RF, and GP models excel in predicting geophysical signatures. While ML-based methods effectively model relationships in the data, their predictive performance remains inherently constrained by the underlying geological and physical mechanisms. Model selection depends on computational resources and application needs, offering valuable insights for real-time orebody analysis using AI. These findings could be invaluable to geologists who wish to utilize AI techniques for real-time orebody analysis and 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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24 pages, 18601 KiB

Open AccessArticle

Utilizing Multifractal and Compositional Data Analysis Combined with Random Forest for Mineral Prediction in Goulmima, Morocco

by Yanbin Wu, Li Sun, Zhiguang Qu, Wenming Yu, Peng Zhang, Guoqing Jing, Pengliang Shen, Shujuan Tian, Qicai Wang, Hua Liu, Fafu Wu, Jiangtao Liu, Keyan Xiao and Rui Tang

Minerals 2025, 15(3), 222; https://doi.org/10.3390/min15030222 - 25 Feb 2025

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Abstract

Morocco is rich in Mississippi Valley Type (MVT) copper deposits. Currently, geochemical surveying is being conducted in the Goulmima region in pursuit of breakthroughs in mineral exploration. This paper focuses on the delineation of prospecting targets in the Goulmima area based on the [...] Read more.

Morocco is rich in Mississippi Valley Type (MVT) copper deposits. Currently, geochemical surveying is being conducted in the Goulmima region in pursuit of breakthroughs in mineral exploration. This paper focuses on the delineation of prospecting targets in the Goulmima area based on the ongoing 1:100,000 geochemical survey work in Morocco. The study employs compositional data transformation to perform isometric log-ratio (ilr) transformations on raw data, followed by the Spectrum-Area (S-A) fractal processing, and then uses the Random Forest (RF) algorithm for mineral prediction. Finally, the prediction results are further delineated using the Concentration-Area (C-A) fractal model to identify high-probability areas, marking two prospecting targets. The results show: (1) the ilr transformation reduces the closure problem of the original data and improves their symmetry, thereby more effectively revealing the spatial structural features of the elements; (2) the principal component analysis (PCA) performed on the ilr-transformed data successfully identifies two main element combinations, representing high-temperature hydrothermal environments (Mo-Sn-Ti-W-U) and low-temperature mineralization environments (CaO-Pb-Zn), consistent with the regional mining history; (3) the application of the S-A multifractal model effectively distinguishes between anomalies and background distributions in the geochemical data of the study area, and combines fault buffer zones as the basis for mineral prediction; (4) the C-A fractal model further subdivides the prediction results, dividing potential mining areas into high, medium, and low probability zones, and ultimately identifies two prospecting targets. 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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17 pages, 10844 KiB

Open AccessArticle

Mineral Prospectivity Mapping in Xiahe-Hezuo Area Based on Wasserstein Generative Adversarial Network with Gradient Penalty

by Jiansheng Gong, Yunhe Li, Miao Xie, Yunhui Kong, Rui Tang, Cheng Li, Yixiao Wu and Zehua Wu

Minerals 2025, 15(2), 184; https://doi.org/10.3390/min15020184 - 16 Feb 2025

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Abstract

The Xiahe-Hezuo area in Gansu Province, China, located in the West Qinling Metallogenic Belt, is characterized by complex regional geological structures and abundant mineral resources. A number of gold-polymetallic deposits have been identified in this region, demonstrating significant potential for gold-polymetallic mineral prospecting [...] Read more.

The Xiahe-Hezuo area in Gansu Province, China, located in the West Qinling Metallogenic Belt, is characterized by complex regional geological structures and abundant mineral resources. A number of gold-polymetallic deposits have been identified in this region, demonstrating significant potential for gold-polymetallic mineral prospecting within the metallogenic belt. This study focuses on regional Mineral Prospectivity Mapping (MPM) in the Xiahe-Hezuo area. To address the common challenge of small-sample data limitations in geological prediction, we introduce a Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to generate high-fidelity geological feature samples, effectively expanding the training dataset. A Convolutional Neural Network (CNN) was used to train and predict on both pre- and post-augmentation data. The experimental results show that, before augmentation, the CNN model’s Receiver Operating Characteristic (ROC) value was 0.9648. After data augmentation with the WGAN-GP, the CNN model’s ROC value improved to 0.9792. Additionally, the CNN model’s classification performance was significantly enhanced, with the training set accuracy increasing by 5% and the test set accuracy improving by 2%, successfully overcoming the issue of insufficient model generalization caused by small sample sizes. The mineralization prediction results based on data augmentation delineate five prospective mineralization targets, whose spatial distribution exhibits strong correlations with known deposits and fault structural belts, confirming the reliability of the predictions. This study validates the effectiveness of data augmentation techniques in MPM and provides a transferable technical framework for MPM in data-scarce regions. 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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25 pages, 4754 KiB

Open AccessArticle

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 - 21 Dec 2024

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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

Open AccessArticle

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

Cited by 2 | Viewed by 893

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

Open AccessArticle

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

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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 mAP_50–95, 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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