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18 pages, 8068 KiB

Open AccessArticle

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

by Tao Tao, Peng Han, Xiao-Hui Yang, Qiang Zu, Kaiyan Hu, Shuangling Mo, Shuangshuang Li, Qiang Luo and Zhanxiang He

Minerals 2024, 14(2), 184; https://doi.org/10.3390/min14020184 - 09 Feb 2024

Viewed by 760

Abstract

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. [...] Read more.

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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19 pages, 6421 KiB

Open AccessArticle

Predicting Flowability at Disposal of Spent Heap Leach by Applying Artificial Neural Networks Based on Operational Variables

by Nelson Herrera, María Sinche Gonzalez, Jarkko Okkonen and Raul Mollehuara Canales

Minerals 2024, 14(1), 40; https://doi.org/10.3390/min14010040 - 29 Dec 2023

Viewed by 842

Abstract

The mining sector actively seeks to improve operational processes and manage residual materials, especially in areas used for heap leaching disposal. The flowability of residues following deposition can have an impact on storage capacity, productivity, and workers’ safety. In this study, an artificial [...] Read more.

The mining sector actively seeks to improve operational processes and manage residual materials, especially in areas used for heap leaching disposal. The flowability of residues following deposition can have an impact on storage capacity, productivity, and workers’ safety. In this study, an artificial neural network (ANN) approach is applied to evaluate the accuracy of three models in predicting the flowability of spent heap leach when it is discharged into the dump, considering three or five segregation categories. The models with five categories exhibited the highest level of accuracy, with learning responses ranging from 72% to 78% and predictions from 88% to 96%. These indicate that ANN models have the potential to be a decision-making tool for the discharge strategy in the dump. Modules containing lithologies such as clays and phyllosilicates exhibited increased susceptibility to separation due to their water retention capacity, which negatively impacted their permeability and conductivity. The decomposition of iron oxide, along with clays and low hardness, led to the formation of fines, limited permeability, and inadequate solution drainage. Rock competence and low formation of fines provide good permeability, and better drainage conditions for the solution, and help maintain the stability of the spent heap leach in the dump. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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15 pages, 5265 KiB

Open AccessArticle

A Review on Global Cooperation Network in the Interdisciplinary Research of Geochemistry Combined with Artificial Intelligence

by Qianlong Zhang, Yongzhang Zhou, Juxiang He, Biaobiao Zhu, Feng Han and Shiyao Long

Minerals 2023, 13(10), 1332; https://doi.org/10.3390/min13101332 - 15 Oct 2023

Viewed by 1043

Abstract

With the rapid development of modern geochemical analysis techniques, massive volumes of data are being generated from various sources and forms, and geochemical data acquisition and analysis have become important tools for studying geochemical processes and environmental changes. However, geochemical data have high-dimensional, [...] Read more.

With the rapid development of modern geochemical analysis techniques, massive volumes of data are being generated from various sources and forms, and geochemical data acquisition and analysis have become important tools for studying geochemical processes and environmental changes. However, geochemical data have high-dimensional, nonlinear characteristics, and traditional geochemical data analysis methods have struggled to meet the demands of modern science. Nowadays, the development of big data and artificial intelligence technologies has provided new ideas and methods for geochemical data analysis. However, geochemical research involves numerous fields such as petrology, ore deposit, mineralogy, and others, each with its specific research methods and objectives, making it difficult to strike a balance between depth and breadth of investigation. Additionally, due to limitations in data sources and collection methods, existing studies often focus on a specific discipline or issue, lacking a comprehensive understanding of the bigger picture and foresight for the future. To assist geochemists in identifying research hotspots in the field and exploring solutions to the aforementioned issues, this article comprehensively reviews related studies in recent years, elaborates on the necessity and challenges of combining geochemistry and artificial intelligence, and analyzes the characteristics and research hotspots of the global collaboration network in this field. The study reveals that the investigation into artificial intelligence techniques to address geochemical issues is progressing swiftly. Joint research papers serve as the primary means of contact within a worldwide collaborative network. The primary areas of focus in the ongoing research on the integration of geochemistry and artificial intelligence include methodologies for analyzing geochemical data, environmental modifications, and mineral prospectivity mapping. Geochemical data analysis is currently a significant focus of research, encompassing a range of methods including machine learning and deep learning. Predicting mineral resources for deep space, deep Earth, and deep sea is also a pressing topic in contemporary research. This paper explores the factors driving research interest and future trends, identifies current research challenges, and considers opportunities for future research. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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15 pages, 3875 KiB

Open AccessFeature PaperArticle

Multitarget Intelligent Recognition of Petrographic Thin Section Images Based on Faster RCNN

by Hanyu Wang, Wei Cao, Yongzhang Zhou, Pengpeng Yu and Wei Yang

Minerals 2023, 13(7), 872; https://doi.org/10.3390/min13070872 - 28 Jun 2023

Cited by 1 | Viewed by 1551

Abstract

The optical features of mineral composition and texture in petrographic thin sections are an important basis for rock identification and rock evolution analysis. However, the efficiency and accuracy of human visual interpretation of petrographic thin section images have depended on the experience of [...] Read more.

The optical features of mineral composition and texture in petrographic thin sections are an important basis for rock identification and rock evolution analysis. However, the efficiency and accuracy of human visual interpretation of petrographic thin section images have depended on the experience of experts for a long time. The application of image-based computer vision and deep-learning algorithms to the intelligent analysis of the optical properties of mineral composition and texture in petrographic thin section images (in plane polarizing light) has the potential to significantly improve the efficiency and accuracy of rock identification and classification. This study completed the transition from simple petrographic thin image classification to multitarget detection, to address more complex research tasks and more refined research scales that contain more abundant information, such as spatial, quantitative and category target information. Oolitic texture is an important paleoenvironmental indicator that widely exists in sedimentary records and is related to shallow water hydraulic conditions. We used transfer learning and image data augmentation in this paper to identify the oolitic texture of petrographic thin section images based on the faster region-based convolutional neural network (Faster RCNN) method. In this study, we evaluated the performance of Faster RCNN, a two-stage object detection algorithm, using VGG16 and ResNet50 as backbones for image feature extraction. Our findings indicate that ResNet50 outperformed VGG16 in this regard. Specifically, the Faster RCNN model with ResNet50 as the backbone achieved an average precision (AP) of 92.25% for the ooids test set, demonstrating the accuracy and reliability of this approach for detecting ooids. The experimental results also showed that the uneven distribution of training sample images and the complexity of images both significantly affect detection performance; however, the uneven distribution of training sample images has a greater impact. Our work is preliminary for intelligent recognition of multiple mineral texture targets in petrographic thin section images. We hope that it will inspire further research in this field. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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21 pages, 2438 KiB

Open AccessArticle

Recursive Feature Elimination and Neural Networks Applied to the Forecast of Mass and Metallurgical Recoveries in A Brazilian Phosphate Mine

by Fernanda Gontijo Fernandes Niquini, André Miranda Brito Branches, João Felipe Coimbra Leite Costa, Gabriel de Castro Moreira, Claudio Luiz Schneider, Florence Cristiane de Araújo and Luciano Nunes Capponi

Minerals 2023, 13(6), 748; https://doi.org/10.3390/min13060748 - 31 May 2023

Cited by 1 | Viewed by 1381

Abstract

Geometallurgical models are commonly built by combining explanatory variables to obtain the response that requires prediction. This study presented a phosphate plant with three concentration steps: magnetic separation, desliming and flotation, where the yields and recoveries corresponding to each process unit were predicted. [...] Read more.

Geometallurgical models are commonly built by combining explanatory variables to obtain the response that requires prediction. This study presented a phosphate plant with three concentration steps: magnetic separation, desliming and flotation, where the yields and recoveries corresponding to each process unit were predicted. These output variables depended on the ore composition and the collector concentration utilized. This paper proposed a solution based on feature engineering to select the best set of explanatory variables and a subset of them able to keep the model as simple as possible but with enough precision and accuracy. After choosing the input variables, two neural network models were developed to simultaneously forecast the seven geometallurgical variables under study: the first, using the best set of variables; and the second, using the reduced set of inputs. The forecasts obtained in both scenarios were compared, and the results showed that the mean squared error and the root mean squared error increase in all output variables evaluated in the test set was smaller than 2.6% when the reduced set was used. The trade-off between simplicity and the quality of the model needs to be addressed when choosing the final neural network to be used in a 3D-block model. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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20 pages, 7530 KiB

Open AccessArticle

Coal Structure Prediction Based on Type-2 Fuzzy Inference System for Multi-Attribute Fusion: A Case Study in South Hengling Block, Qinshui Basin, China

by Xuepeng Cui, Youcai Tang, Handong Huang, Lingqian Wang, Jianxing Wang, Zifan Guo, Chen Ma and Meng Sun

Minerals 2023, 13(5), 589; https://doi.org/10.3390/min13050589 - 23 Apr 2023

Cited by 1 | Viewed by 1075

Abstract

The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore [...] Read more.

The accurate prediction of coal structure is important to guide the exploration and development of coal reservoirs. Most prediction models are interpreted for a single sensitive coal seam, and the selection of sensitive parameters is correlated with the coal structure, but they ignore the interactions between different attributes. Part of it introduces the concept of the geological strength index (GSI) of coal rocks in order to achieve a multi-element macroscopic description and quantitative characterization of coal structure; however, the determination of coal structure involves some uncertainties among the properties of coal, such as lithology, gas content and tectonic fracture, due to their complex nature. Fuzzy inference systems provide a knowledge discovery process to handle uncertainty. The study shows that a type-2 fuzzy inference system (T2-FIS) with multi-attribute fusion is used to effectively fuse pre-stack and post-stack seismic inversion reservoir parameters and azimuthal seismic attribute parameters in order to produce more accurate prediction results for the Hengling block in the Shanxi area. The fuzzy set rules generated in this paper can provide a more reliable prediction of coal structure in the GSI system. The proposed system has been tested on various datasets and the results show that it is capable of providing reliable and high-quality coal structure predictions. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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18 pages, 9436 KiB

Open AccessArticle

Applying Data-Driven-Based Logistic Function and Prediction-Area Plot to Map Mineral Prospectivity in the Qinling Orogenic Belt, Central China

by Hongyang Bai, Yuan Cao, Heng Zhang, Wenfeng Wang, Chaojun Jiang and Yongguo Yang

Minerals 2022, 12(10), 1287; https://doi.org/10.3390/min12101287 - 13 Oct 2022

Cited by 3 | Viewed by 1490

Abstract

This study combines data-driven-based logistic functions with prediction–area (P–A) plot for delineating target areas of orogenic Au deposits in the eastern margin of the Qinling metallogenic belt, central China. First, appropriate geological and geochemical factors were identified, optimized, and transformed into a series [...] Read more.

This study combines data-driven-based logistic functions with prediction–area (P–A) plot for delineating target areas of orogenic Au deposits in the eastern margin of the Qinling metallogenic belt, central China. First, appropriate geological and geochemical factors were identified, optimized, and transformed into a series of fuzzy numbers with a range of 0–1 through a data-driven-based logistic function in order to determine the evidence layer for prospecting orogenic Au. In addition, the P–A plot was derived on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme, in the process of which acidic magmatic activity proved to be the most important factor of ore-controlling. Moreover, to further prove the advantages of this method, a traditional linear knowledge-driven approach was carried out for comparative purposes. Finally, based on concentration–area (C–A) fractal theory, the fractal thresholds were determined and a mineral prospecting map was generated. The obtained prediction map consisted of high, medium, low, and weak metallogenic potential areas, accounting for 2.5%, 16.1%, 38.4%, and 43% of the study area, containing 2, 3, 1, and 0 of the 6 known mine occurrences contained, respectively. The P–A plot indicated that the result predicted 83% of Au deposits with 17% of the area, confirming the joint application of the data-driven-based logistic function and P–A plot to be a simple, effective, and low-cost method for mineral prospectivity mapping, that can be a guidance for further work in the study area. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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33 pages, 9288 KiB

Open AccessFeature PaperArticle

Construction and Application of a Knowledge Graph for Gold Deposits in the Jiapigou Gold Metallogenic Belt, Jilin Province, China

by Yao Pei, Sheli Chai, Xiaolong Li, Jofrisse Cremilda Samuel, Chengyou Ma, Haonan Chen, Renxing Lou and Yu Gao

Minerals 2022, 12(9), 1173; https://doi.org/10.3390/min12091173 - 17 Sep 2022

Viewed by 1879

Abstract

Over the years, many geological exploration reports and considerable geological data have been accumulated during the prospecting and exploration of the Jiapigou gold metallogenic belt (JGMB). It is very important to fully utilize these geological and mineralogical big data to guide future gold [...] Read more.

Over the years, many geological exploration reports and considerable geological data have been accumulated during the prospecting and exploration of the Jiapigou gold metallogenic belt (JGMB). It is very important to fully utilize these geological and mineralogical big data to guide future gold exploration. This work collects the original textual data of different gold deposits in JGMB and constructs a knowledge graph (KG) for deposits based on deep learning (DL) and natural language processing (NLP). Based on the metallogenic geological characteristics of deposits, a visual construction method of a KG for deposits and a calculation of the similarity between deposits are proposed. In this paper, 20 geological entities and 24 relationship categories are considered. By condensing the key KG information, the metallogenic geological conditions and factors controlling the ore in 14 typical deposits in the JGMB are systematically analyzed, and the metallogenic regularity is summarized. By calculating the deposits’ cosine similarities based on the KG, the mineralization types of deposits can be divided into two categories according to the industrial types of ore bodies. The results also show that the KG is a cutting-edge technology that can extract the rich information of ore-forming regularity and prospecting criteria contained in the textual data to help researchers quickly analyze the mineralization information. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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17 pages, 9049 KiB

Open AccessArticle

Prediction of Prospecting Target Based on Selective Transfer Network

by Yongjie Huang, Quan Feng, Wanting Zhang, Li Zhang and Le Gao

Minerals 2022, 12(9), 1112; https://doi.org/10.3390/min12091112 - 31 Aug 2022

Cited by 2 | Viewed by 1521

Abstract

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological [...] Read more.

In recent years, with the integration and development of artificial intelligence technology and geology, traditional geological prospecting has begun to change to intelligent prospecting. Intelligent prospecting mainly uses machine learning technology to predict the prospecting target area by mining the correlation between geological variables and metallogenic characteristics, which usually requires a large amount of data for training. However, there are some problems in the actual research, such as fewer geological sample data and irregular mining features, which affect the accuracy and reliability of intelligent prospecting prediction. Taking the Pangxidong study area in Guangdong Province as an example, this paper proposes a deep learning framework (SKT) for prospecting target prediction based on selective knowledge transfer and carries out intelligent prospecting target prediction research based on geochemical data in Pangxidong. The irregular features of different scales in the mining area are captured by dilation convolution, and the weight parameters of the source network are selectively transferred to different target networks for training, so as to increase the generalization performance of the model. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved, which can effectively alleviate the problems of a small number of geological samples and irregular features of mining areas in prospecting prediction. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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Review

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36 pages, 3966 KiB

Open AccessReview

Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

by Nelson Herrera, María Sinche Gonzalez, Jarkko Okkonen and Raul Mollehuara

Minerals 2023, 13(11), 1450; https://doi.org/10.3390/min13111450 - 17 Nov 2023

Cited by 1 | Viewed by 1324

Abstract

In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability [...] Read more.

In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability to forecast outcomes. Soft computing techniques, such as artificial neural networks and fuzzy algorithms, have emerged as viable alternatives to traditional statistical approaches, where the complex and non-linear nature of the mineral processing stages requires careful selection. This research examines the up-to-date use of soft computing techniques within the mining sector, with a specific emphasis on comminution, flotation, and pyrometallurgical and hydrometallurgical processes, and the selection of soft computing techniques and strategies for identifying key variables. From this, a soft computing approach is presented to enhance the monitoring and prediction accuracy for mineral waste disposal, specifically focusing on tailings and spent heap leaching spoils database treatment. However, the accessibility and quality of data are crucial for the long-term application of soft computing technology in the mining industry. Further research is needed to explore the full potential of soft computing techniques and to address specific challenges in mining and mineral processing. Full article

(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Ore Deposits)

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