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Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 8983

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Special Issue Editors

Prof. Dr. Yongqing Chen

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Guest Editor

School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
Interests: quantitative exploration and assessment of mineral resources; ore information extraction and integration

Prof. Dr. Xianchuan Yu

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Guest Editor

School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Interests: machine learning and geoscience; 3D stochastic modeling; earth artificial intelligence; knowledge graph construction; spatio-temporal data mining; visualization in mineral Systems

Dr. Jiangnan Zhao

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Guest Editor

School of Earth Resources, China University of Geosciences, Wuhan 430074, China
Interests: mineral potential mapping; geochemical exploration; geochemical anomaly identification; comprehensive mineral exploration; fractals; machine learning

Special Issue Information

Dear Colleagues,

"Digital geology" can be called a combination of "mathematical geology" and "information technology", which is the data analysis component of geological science. Geological data science is a science that uses the general methodology of data to study geology based on the characteristics of geological data and the needs of geological works. Digital exploration and assessments of mineral resources manifest in the application of digital geology in mineral exploration to reduce ore-prospecting uncertainty and to improve ore-prospecting efficiency. The key digital geoscience knowledge lies in highly condensed information derived from raw geological, geochemistry, geophysics, and remote sensing survey data, which can be used to derive quantitatively exact expressions of the underlying ore-forming processes, phenomena, and regularities. These scientific expressions can be used to predict the occurrence, development, and results of ore-forming events in geological time. Digital mineral exploration is a successful application of data geosciences combined with information technology in geosciences. This Special Issue will showcase the theories, methods, and achievements in digital mineral exploration and assessments in recent years.

Prof. Dr. Yongqing Chen
Prof. Dr. Xianchuan Yu
Dr. Jiangnan Zhao
Guest Editors

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Keywords

ore-prospecting information from geology, geochemistry, geophysics and remote sensing survey
ore-forming geo-anomaly
ore-forming system
nonlinear geosciences
mineral potential mapping
machine learning and geoscience

Published Papers (6 papers)

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Research

21 pages, 12792 KiB

Open AccessArticle

Identifying Multi-Scale Gravity and Magnetic Anomalies Using Statistical Empirical Mode Decomposition: A Case Study from the Eastern Tianshan Orogenic Belt

by Fan Xiao, Weipeng Lin, Huaqing Yang and Cuicui Wang

Minerals 2023, 13(9), 1118; https://doi.org/10.3390/min13091118 - 24 Aug 2023

Viewed by 859

Abstract

Identifying multi-scale anomalies that have simple forms and geological significance is critical for enhancing the interpretability of gravity and magnetic survey data. In recent years, empirical mode decomposition (EMD), which was developed as a significant data-driven approach for analyzing complex signals, has been widely used in identifying gravity and magnetic anomalies due to its advantages of adaptability to nonlinear and nonstationary data. Nevertheless, the traditional EMD method is usually sensitive to outliers and irregularly spaced data because of the interpolation process in the construction of envelopes. In this regard, an extended algorithm called statistical EMD (SEMD) has been proposed based on the smoothing technique. In this study, for validation purposes, the novel SEMD method has been employed to identify multi-scale gravity and magnetic anomalies. The sensitivities of local polynomial and cubic spline smoothing methods in SEMD to combination and arrangement patterns of field sources including the size, depth, and distance in gravity and magnetic anomaly identification were investigated and compared by forward modeling under the same conditions. The results demonstrated that the local polynomial smoothing method performed better than the cubic spline smoothing method. Thus, in the case study, the SEMD method using the local polynomial smoothing technique was employed for identifying multi-scale gravity and magnetic anomalies in the eastern Tianshan orogenic belt, northwestern China. It has illustrated that the SEMD method provides a novel and useful data-driven method for extracting gravity and magnetic anomalies. Full article

(This article belongs to the Special Issue Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements)

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15 pages, 7730 KiB

Open AccessArticle

Partial Decision Tree Forest: A Machine Learning Model for the Geosciences

by Elife Ozturk Kiyak, Goksu Tuysuzoglu and Derya Birant

Minerals 2023, 13(6), 800; https://doi.org/10.3390/min13060800 - 12 Jun 2023

Cited by 3 | Viewed by 1100

Abstract

As a result of the continuous growth in the amount of geological data, machine learning (ML) offers an opportunity to contribute to solving problems in geosciences. However, digital geology applications introduce new challenges for machine learning due to the unique geoscience properties encountered in each problem, requiring novel research in ML. This paper proposes a novel machine learning method, entitled “Partial Decision Tree Forest (PART Forest)”, to overcome these challenges introduced by geoscience problems and offers potential advancements in both machine learning and geoscience disciplines. The effectiveness of the proposed PART Forest method was illustrated in mineral classification. This study aims to build an intelligent ML model that automatically classifies the minerals in terms of their crystal structures (triclinic, monoclinic, orthorhombic, tetragonal, hexagonal, and trigonal) by taking into account their chemical compositions and their physical and optical properties. In the experiments, the proposed PART Forest method demonstrated its superiority over one of the well-known ensemble learning methods, random forest, in terms of accuracy, precision, recall, f-score, and AUC (area under the curve) metrics. Full article

(This article belongs to the Special Issue Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements)

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13 pages, 4063 KiB

Open AccessArticle

Application of Logistic Regression and Weights of Evidence Methods for Mapping Volcanic-Type Uranium Prospectivity

by Jiangnan Zhao, Yu Sui, Zongyao Zhang and Mi Zhou

Minerals 2023, 13(5), 608; https://doi.org/10.3390/min13050608 - 27 Apr 2023

Cited by 2 | Viewed by 1019

Abstract

Pucheng district is a part of the Wuyi Mountain polymetallic metallogenic belt, which is constituted by Archean-Proterozoic metamorphic basements and Mesozoic volcanic-sedimentary covers. Uranium deposits are formed as volcanic-hosted and structural controls. In this study, the hybrid data-driven methods of logistic regression (LR) and weights of evidence (WofE) were applied for the mineral potential mapping of uranium in the Pucheng district. Evidential layers such as volcanic stratum, structure, igneous rock, alteration and radioactive anomaly were used in the mineral prospectivity analyses. The results show that the data-driven methods can not only measure the relative importance of each type of geological feature in uranium controls but also delineate prospective grounds for uranium exploration. The receiver operating characteristics (ROC) curve and under the ROC curve (AUC) were applied to measure the performance of the prospectivity models. The data-driven models are highly capable of mapping uranium prospectivity because AUC is close to 1. The results show that more than 90% of the known uranium deposits occur in regions with high probability. LR performs a little better than WofE in this area. The prospectivity mapping confirmed that there is significant potential for uranium mineralization for further exploration. Full article

(This article belongs to the Special Issue Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements)

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32 pages, 13460 KiB

Open AccessArticle

Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China

by Shi Li, Chang Liu and Jianping Chen

Minerals 2023, 13(4), 504; https://doi.org/10.3390/min13040504 - 31 Mar 2023

Cited by 4 | Viewed by 1573

Abstract

In the big data era, mineral explorations need to accommodate for the growth in spatial dimensions and data dimensions, as well as the data volume and the correlation between data. Aiming to overcome the problems of limited and scattered data sources, chaotic data types, questionable data quality, asymmetric data information, and small sample sizes in current mineral prospecting data, this paper improved traditional 3D prediction methods based on the characteristics and actual needs of relevant mineral prospecting data. First, for the regions with incomplete data, a new 3D prediction method based on transfer learning was proposed. Meanwhile, random noise was adopted to compensate for the limited sample size in mineral prediction. By taking the Huayuan Mn deposit in Hunan Province as the study area, 22 proposed ore-controlling variables were divided into six groups for comparative tests under different combinations, and each group was further divided into the 3D CNN prediction method and the transfer learning prediction method. After the similarities between the regional metallogenic backgrounds were proven, the convolution kernel of the Minle area was transferred to that of the Huayuan area with poor data. Then, both were used to train a 3D prediction model to realize the training and transfer of the spatial correlation between the spatial distribution of ore-controlling factors and the manganese ore. The results indicated that the accuracy of the transfer learning model in test 6 could reach 100%, with good stability of the transfer learning prediction model and a high convergence speed. By comparing the 3D-predicted targets before and after the transfer learning of tests 5 and 6, it was found that the 3D CNN model of test 5 still performed well, but the transfer learning model of test 6 was superior. In verifications based on superposition with the basin model and the growth fault model, the prediction results were consistent with the geological characteristics of the research area. To sum up, the 3D CNN prediction method has advantages in mineral prediction when big data are available, and transfer learning based on the 3D CNN algorithm helps to realize 3D deep mineral prospect prediction in the case of incomplete data. Full article

(This article belongs to the Special Issue Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements)

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17 pages, 6286 KiB

Open AccessArticle

Mineralization Regularities of the Bainiuchang Ag Polymetallic Deposit in Yunnan Province, China

by Fuju Jia, Ceting Yang, Guolong Zheng, Mingrong Xiang, Xuelong Liu, Wei Duan, Junshan Dao and Zhihong Su

Minerals 2023, 13(3), 418; https://doi.org/10.3390/min13030418 - 16 Mar 2023

Viewed by 1096

Abstract

The Bainiuchang Ag polymetallic deposit is located at the junction between the Cathaysia, Yangtze, China and Indosinian blocks. It has experienced many geological events, and records excellent conditions for multiple mineralization. In this paper, elemental correlation analysis, cluster analysis, factor analysis, a semivariogram of Zn/Pb values, mineralization distribution and trend surface analysis have been carried out based on the prospecting database and ore body model. Our results show that Ag–Pb–Zn were mineralized at moderate temperatures. Tin was mineralized at high temperatures, and Sn and Zn/Pb values are well correlated. The Zn/Pb values can be used for tracing the ore-forming fluid. The semivariogram revealed that the Zn/Pb values are moderately spatially dependent, with good mineralization continuity in the 100° and 10° directions. The spatial pattern of the elemental grade correlates with mineralization enrichment. The trend surface analysis shows that the Ag, Pb, Zn, and Cu mineralization is weak in the south and strong in the north of the deposit, and the Sn grades and Zn/Pb values are high in the south and low in the north. High-temperature Sn, medium-temperature Cu, and medium-temperature Ag–Pb–Zn mineralization have occurred in a south-to-north trend. Therefore, the source of the ore-forming fluid was in the southern part of the mining area. During the migration of the ore-forming fluid from south to north, different minerals were precipitated due to changes in the physicochemical environment. The spatial patterns of mineralization may provide a basis for studying the formation of the ore deposit, and can guide ore exploration and mining in the mine area and similar ore deposits elsewhere. Full article

(This article belongs to the Special Issue Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements)

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12 pages, 2975 KiB

Open AccessArticle

Evaluation of Portable X-ray Fluorescence Analysis and Its Applicability As a Tool in Geochemical Exploration

by Shuguang Zhou, Jinlin Wang, Wei Wang and Shibin Liao

Minerals 2023, 13(2), 166; https://doi.org/10.3390/min13020166 - 24 Jan 2023

Cited by 6 | Viewed by 2303

Abstract

Large-scale, high-density geochemical explorations entail enormous workloads and high costs for sample analysis, but, for early mineral exploration, absolute concentrations are not essential. Geochemists require ranges, dynamics of variation, and correlations for early explorations rather than absolute accuracy. Thus, higher work efficiency and lower costs for sample analysis are desirable for geochemical exploration. This study comprehensively analyzed the reliability and applicability of portable X-ray fluorescence (pXRF) spectrometry in geochemical exploration. The results show that pXRF can be applied effectively to rock and rock powder samples, and sample preparation and a longer detection time have been shown to increase the precision of the pXRF results. When pXRF is used on rock samples, if less than 30% of the samples are assessed as containing an element, the element is usually undetectable using pXRF when these rock samples are prepared as rock powders, indicating that the data about the detected element are unreliable; thus, it is suggested that some representative samples should be selected for testing before starting to use a pXRF in a geochemical exploration project. In addition, although the extended detection time increased the reliability of the analysis results, an increase in detection time of more than 80 s did not significantly affect the accuracy of the results. For this reason, the recommended detection time for the pXRF analysis of rock powder samples is 80 s for this study. pXRF has the advantages of being low-cost, highly efficient, and stable, and its results are reliable enough to exhibit the spatial distribution of indicator elements (arsenic, nickel, lead, sulfur, titanium, and zinc) in polymetallic mineralization exploration. Therefore, pXRF is recommendable for practical use in geochemical exploration. Full article

(This article belongs to the Special Issue Digital Exploration and Assessment of Mineral Resources: Theories, Methods and Achievements)

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