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3D/4D Geological Modeling for Mineral Exploration

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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 July 2022) | Viewed by 26694

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Special Issue Editors

Prof. Dr. Gongwen Wang

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School of Geosciences and Resources, China University of Geosciences, Beijing 100083, China
Interests: big data of geoscience; mineral exploration; remote sensening mapping; artificial intelligence; machine learning; deep learning; 3D/4D geological modeling; geostatistics; numerical Simulation; hyperspectrum
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lizhen Cheng

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Guest Editor

Research Institute in Mines and the Environment (IRME), University of Québec in Abitibi-Témiscamingue, Rouyn-Noranda, QC J9X 5E4, Canada
Interests: 3D geophysics modeling for geosciences
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Nan Li

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Guest Editor

Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Interests: 3D/4D modeling; prospectivity mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Weisheng Hou

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Guest Editor

School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou 510000, China
Interests: 3D geological modeling and its applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Three-dimensional and four-dimensional (3D/4D) geological modeling is a key technology and methodology for geologists to understand geological events and quantitatively analyze multiscale metallogenic models for mineral exploration. The geological concept model can be quantitatively analyzed and 3D/4D models can be built, simulated, and integrated via multisource geosciences datasets or big data from the field of geosciences. It is a challenge to construct 3D/4D certainty models for mineral exploration using multiscale and multisource datasets; mineral resource assessment and environment protection are associated with regional mining development and strategic planning. The Special Issue aims to improve decision-making processes using 3D/4D geological modeling for mineral exploration, and multiple innovative methodologies and technologies (e.g., conventional explicit and implicit modeling, real-time mining and 5G+ information technology, artificial intelligence decision making, 3D/4D simulation, and digital twin).

Prof. Dr. Gongwen Wang
Prof. Dr. Lizhen Cheng
Prof. Dr. Nan Li
Dr. Weisheng Hou
Guest Editors

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Keywords

3D/4D geological modeling
3D/4D simulation
data mining
knowledge discovery
artificial intelligence
uncertainty analysis
metallogenic systems and mineral systems
mineral resource assessment
deep 3D targeting
big data of geosciences

Published Papers (12 papers)

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Editorial

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3 pages, 186 KiB

Open AccessEditorial

Editorial for Special Issue “3D/4D Geological Modeling for Mineral Exploration”

by Gongwen Wang

Minerals 2023, 13(2), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/min13020198 - 30 Jan 2023

Viewed by 1106

Abstract

With the development of high-precision geological observation technology, in situ mineral microanalysis technology, isotope geochemical analysis technology, deep geophysical exploration technology, deep drilling, real-time mining, remote sensing high-resolution hyperspectral image technology, and supercomputer and industrial intelligence, geoscience has entered an era of big [...] Read more.

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

Research

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20 pages, 6228 KiB

Open AccessArticle

3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Deep Learning-Based Mineral Prediction

by Zhengbo Yu, Bingli Liu, Miao Xie, Yixiao Wu, Yunhui Kong, Cheng Li, Guodong Chen, Yaxin Gao, Shuai Zha, Hanyuan Zhang, Lu Wang and Rui Tang

Minerals 2022, 12(11), 1382; https://0-doi-org.brum.beds.ac.uk/10.3390/min12111382 - 30 Oct 2022

Cited by 8 | Viewed by 1852

Abstract

This paper focuses on the scientific problem of quantitative mineralization prediction at large depth in the Zaozigou gold deposit, west Qinling, China. Five geological and geochemical indicators are used to establish geological and geochemical quantitative prediction model. Machine learning and Deep learning algorithms are employed for 3D Mineral Prospectivity Mapping (MPM). Especially, the Student Teacher Ore-induced Anomaly Detection (STOAD) model is proposed based on the knowledge distillation (KD) idea combined with Deep Auto-encoder (DAE) network model. Compared to DAE, STOAD uses three outputs for anomaly detection and can make full use of information from multiple levels of data for greater overall robustness. The results show that the quantitative mineral resources prediction by applying the STOAD model has a good performance, where the value of Area Under Curve (AUC) is 0.97. Finally, three main mineral exploration targets are delineated for further investigation. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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31 pages, 9104 KiB

Open AccessArticle

3D Mineral Prospectivity Mapping of Zaozigou Gold Deposit, West Qinling, China: Machine Learning-Based Mineral Prediction

by Yunhui Kong, Guodong Chen, Bingli Liu, Miao Xie, Zhengbo Yu, Cheng Li, Yixiao Wu, Yaxin Gao, Shuai Zha, Hanyuan Zhang, Lu Wang and Rui Tang

Minerals 2022, 12(11), 1361; https://0-doi-org.brum.beds.ac.uk/10.3390/min12111361 - 26 Oct 2022

Cited by 6 | Viewed by 2578

Abstract

This paper focuses on researching the scientific problem of deep extraction and inference of favorable geological and geochemical information about mineralization at depth, based on which a deep mineral resources prediction model is established and machine learning approaches are used to carry out deep quantitative mineral resources prediction. The main contents include: (i) discussing the method of 3D geochemical anomaly extraction under the multi-fractal content-volume (C-V) models, extracting the 12 element anomalies and constructing a 3D geochemical anomaly data volume model for laying the data foundation for researching geochemical element distribution and association; (ii) extracting the element association characteristics of primary geochemical halos and inferring deep metallogenic factors based on compositional data analysis (CoDA), including quantitatively extracting the geochemical element associations corresponding to ore-bearing structures (Sb-Hg) based on a data-driven CoDA framework, quantitatively identifying the front halo element association (As-Sb-Hg), near-ore halo element association (Au-Ag-Cu-Pb-Zn) and tail halo element association (W-Mo-Co-Bi), which provide quantitative indicators for the primary haloes’ structural analysis at depth; (iii) establishing a deep geological and geochemical mineral resources prediction model, which is constructed by five quantitative mineralization indicators as input variables: fracture buffer zone, element association (Sb-Hg) of ore-bearing structures, metallogenic element Au anomaly, near-ore halo element association Au-Ag-Cu-Pb-Zn and the ratio of front halo to tail halo (As-Sb-Hg)/(W-Mo-Bi); and (iv) three-dimensional MPM based on the maximum entropy model (MaxEnt) and Gaussian mixture model (GMM), and delineating exploration targets at depth. The results show that the C-V model can identify the geological element distribution and the CoDA method can extract geochemical element associations in 3D space reliably, and the machine learning methods of MaxEnt and GMM have high performance in 3D MPM. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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19 pages, 8430 KiB

Open AccessArticle

3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration

by Bahman Abbassi, Li-Zhen Cheng, Michel Jébrak and Daniel Lemire

Minerals 2022, 12(10), 1296; https://0-doi-org.brum.beds.ac.uk/10.3390/min12101296 - 14 Oct 2022

Cited by 3 | Viewed by 1402

Abstract

Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. Compared to conventional 3D interpolation methods, the 3D lithology and Au grade models recovered with SFSS add predictive value to the geological understanding of the deposit in places without access to prior geological and borehole information. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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26 pages, 25732 KiB

Open AccessArticle

Combining 3D Geological Modeling and 3D Spectral Modeling for Deep Mineral Exploration in the Zhaoxian Gold Deposit, Shandong Province, China

by Bin Li, Yongming Peng, Xianyong Zhao, Xiaoning Liu, Gongwen Wang, Huiwei Jiang, Hao Wang and Zhenliang Yang

Minerals 2022, 12(10), 1272; https://0-doi-org.brum.beds.ac.uk/10.3390/min12101272 - 09 Oct 2022

Cited by 4 | Viewed by 2020

Abstract

The Jiaodong Peninsula hosts the main large gold deposits and was the first gold production area in China; multisource and multiscale geoscience datasets are available. The area is the biggest drilling mineral-exploration zone in China. This study used three-dimensional (3D) modeling, geology, and ore body and alteration datasets to extract and synthesize mineralization information and analyze the exploration targeting in the Zhaoxian gold deposit in the northwestern Jiaodong Peninsula. The methodology and results are summarized as follows: The regional Jiaojia fault is the key exploration criterion of the gold deposit. The compression torsion characteristics and concave–convex section zones in the 3D deep environment are the main indicators of mineral exploration using 3D geological and ore-body modeling in the Zhaoxian gold deposit. The hyperspectral detailed measurement, interpretation, and data mining used drill-hole data (>1000 m) to analyze the vectors and trends of the ore body and ore-forming fault and the alteration-zone rocks in the Zhaoxian gold deposit. The short-wave infrared Pos2200 values and illite crystallinity in the alteration zone can be used to identify 3D deep gold mineralization and potential targets for mineral exploration. This research methodology can be globally used for other deep mineral explorations. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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14 pages, 4302 KiB

Open AccessArticle

Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China

by Fandong Meng, Xiaohui Li, Yuheng Chen, Rui Ye and Feng Yuan

Minerals 2022, 12(9), 1174; https://0-doi-org.brum.beds.ac.uk/10.3390/min12091174 - 18 Sep 2022

Cited by 4 | Viewed by 2080

Abstract

The Middle–Lower Yangtze River Metallogenic Belt is an important copper and iron polymetallic metallogenic belt in China. Today’s economic development is inseparable from the support of metal mineral resources. With the continuous exploitation of shallow and easily identifiable mines in China, the prospecting work of deep and hidden mines is very important. Mineral prospectivity modeling (MPM) is an important means to improve the efficiency of mineral exploration. With the increase in resource demands and exploration difficulty, the traditional 2DMPM is often difficult to use to reflect the information of deep mineral deposits. More large-scale deposits are needed to carry out 3DMPM research. With the rise of artificial intelligence, the combination of machine learning and geological big data has become a hot issue in the field of 3DMPM. In this paper, a case study of 3DMPM is carried out based on the Xuancheng–Magushan area’s actual data. Two machine learning methods, the random forest and the logistic regression, are selected for comparison. The results show that the 3DMPM based on random forest method performs better than the logistic regression method. It can better characterize the corresponding relationship between the geological structure combination and the metallogenic distribution, and the accuracy in the test set reaches 96.63%. This means that the random forest model could provide more effective and accurate support for integrating predictive data during 3DMPM. Finally, five prospecting targets with good metallogenic potential are delineated in the deep area of the Xuancheng–Magushan area for future exploration. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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19 pages, 8392 KiB

Open AccessArticle

Orebody Modeling Method Based on the Coons Surface Interpolation

by Zhaohao Wu, Lin Bi, Deyun Zhong, Ju Zhang, Qiwang Tang and Mingtao Jia

Minerals 2022, 12(8), 997; https://0-doi-org.brum.beds.ac.uk/10.3390/min12080997 - 06 Aug 2022

Cited by 2 | Viewed by 1640

Abstract

This paper presents a surface modeling method for interpolating orebody models based on a set of cross-contour polylines (geological polylines interpreted from the raw geological sampling data) using the bi-Coons surface interpolation method. The method is particularly applicable to geological data with cross-contour polylines acquired during the geological and exploration processes. The innovation of this paper is that the proposed method can automatically divide the closed loops and automatically combine the sub-meshes. The method solves the problem that it is difficult to divide closed loops from the cross-contour polylines with complex shapes, and it greatly improves the efficiency of modeling based on complex cross-contour polylines. It consists of three stages: (1) Divide closed loops using approximate planes of contour polylines; each loop is viewed as a polygon combined with several polylines, that is the n-sided region. (2) After processing the formed n-sided regions, Coons surface interpolation is improved to complete the modeling of every single loop (3) Combine all sub-meshes to form a complete orebody model. The corresponding algorithm was implemented using the C++ programing language on 3D modeling software. Experimental results show that the proposed orebody modeling method is useful for efficiently recovering complex orebody models from a set of cross-contour polylines. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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22 pages, 5543 KiB

Open AccessArticle

A 3D Predictive Method for Deep-Seated Gold Deposits in the Northwest Jiaodong Peninsula and Predicted Results of Main Metallogenic Belts

by Mingchun Song, Shiyong Li, Jifei Zheng, Bin Wang, Jiameng Fan, Zhenliang Yang, Guijun Wen, Hongbo Liu, Chunyan He, Liangliang Zhang and Xiangdong Liu

Minerals 2022, 12(8), 935; https://0-doi-org.brum.beds.ac.uk/10.3390/min12080935 - 25 Jul 2022

Cited by 4 | Viewed by 1618

Abstract

With the rapid depletion of mineral resources, deep prospecting is becoming a frontier field in international geological exploration. The prediction of deep mineral resources is the premise and foundation of deep prospecting. However, conventional metallogenic predictive methods, which are mainly based on surface geophysical, geochemical, and remote sensing data and geological information, are no longer suitable for deep metallogenic prediction due to the large burial depth of deep-seated deposits. Consequently, 3D metallogenic prediction becomes a critical method for delineating deep prospecting target areas. As a world-class giant gold metallogenic province, the Jiaodong Peninsula is at the forefront in China in terms of deep prospecting achievements and exploration depth. Therefore, it has unique conditions for 3D metallogenic prediction and plays an important exemplary role in promoting the development of global deep prospecting. This study briefly introduced the method, bases, and results of the 3D metallogenic prediction in the northwest Jiaodong Peninsula and then established 3D geological models of gold concentration areas in the northwest Jiaodong Peninsula using drilling combined with geophysics. Since gold deposits in the northwest Jiaodong Peninsula are often controlled by faulting in the 3D space, this study proposed a method for predicting deep prospecting target areas based on a stepped metallogenic model and a method for predicting the deep resource potential of gold deposits based on the shallow resources of ore-controlling faults. Multiple characteristic variables were extracted from the 3D geological models of the gold concentration areas, including the buffer zone and dip angle of faults, the changing rate of fault dip angle, and the equidistant distribution of orebodies. Using these characteristic variables, five deep prospecting target areas in the Jiaojia and Sanshandao faults were predicted. Moreover, based on the proven gold resources at an elevation of −2000 m and above, the total gold resources of the Sanshandao, Jiaojia, and Zhaoping ore-controlling faults at an elevation of −5000–−2000 m were predicted to be approximately 3377–6490 t of Au. Therefore, it is believed that the total gold resources in the Jiaodong Peninsula are expected to exceed 10,000 t. These new predicted results suggest that the northwest Jiaodong Peninsula has huge potential for the resources of deep gold deposits, laying the foundation for further deep prospecting. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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20 pages, 16140 KiB

Open AccessArticle

3D Multi-Parameter Geological Modeling and Knowledge Findings for Mo Oxide Orebodies in the Shangfanggou Porphyry–Skarn Mo (–Fe) Deposit, Henan Province, China

by Zhifei Liu, Ling Zuo, Senmin Xu, Yaqing He, Chunyi Wang, Luofeng Wang, Tao Yang, Gongwen Wang, Linggao Zeng, Nini Mou and Wangdong Yang

Minerals 2022, 12(6), 769; https://0-doi-org.brum.beds.ac.uk/10.3390/min12060769 - 17 Jun 2022

Cited by 3 | Viewed by 2299

Abstract

The Shangfanggou Mo–Fe deposit is a typical and giant porphyry–skarn deposit located in the East Qinling–Dabie molybdenum (Mo) polymetallic metallogenic belt in the southern margin of the North China Block. In this paper, three-dimensional (3D) multi-parameter geological modeling and microanalysis are used to discuss the mineralization and oxidation transformation process of molybdenite during the supergene stage. Meanwhile, from macro to micro, the temporal–spatial–genetic correlation and exploration constraints are also established by 3D geological modeling of industrial Mo orebodies and Mo oxide orebodies. SEM-EDS and EPMA-aided analyses indicate the oxidation products of molybdenite are dominated by tungsten–powellite at the supergene stage. Thus, a series of oxidation processes from molybdenite to tungsten–powellite are obtained after the precipitation of molybdenite; eventually, a special genetic model of the Shangfanggou high oxidation rate Mo deposit is formed. Oxygen fugacity reduction and an acid environment play an important part in the precipitation of molybdenite: (1) During the oxidation process, molybdenite is first oxidized to a MoO₂·SO₄ complex ion and then reacts with a carbonate solution to precipitate powethite, in which W and Mo elements can be substituted by complete isomorphism, forming a unique secondary oxide orebody dominated by tungsten–powellite. (2) Under hydrothermal action, Mo⁴⁺ can be oxidized to jordisite in the strong acid reduction environment at low temperature and room temperature during the hydrothermal mineralization stage. Ilsemannite is the oxidation product, which can be further oxidized to molybdite. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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26 pages, 41297 KiB

Open AccessArticle

Three-Dimensional Structural Modeling (3D SM) and Joint Geophysical Characterization (JGC) of Hydrocarbon Reservoir

by Baoyi Zhang, Yongqiang Tong, Jiangfeng Du, Shafqat Hussain, Zhengwen Jiang, Shahzad Ali, Ikram Ali, Majid Khan and Umair Khan

Minerals 2022, 12(3), 363; https://0-doi-org.brum.beds.ac.uk/10.3390/min12030363 - 16 Mar 2022

Cited by 13 | Viewed by 3167

Abstract

A complex structural geology generally leads to significant consequences for hydrocarbon reservoir exploration. Despite many existing wells in the Kadanwari field, Middle Indus Basin (MIB), Pakistan, the depositional environment of the early Cretaceous stratigraphic sequence is still poorly understood, and this has implications for regional geology as well as economic significance. To improve our understanding of the depositional environment of complex heterogeneous reservoirs and their associated 3D stratigraphic architecture, the spatial distribution of facies and properties, and the hydrocarbon prospects, a new methodology of three-dimensional structural modeling (3D SM) and joint geophysical characterization (JGC) is introduced in this research using 3D seismic and well logs data. 3D SM reveals that the field in question experienced multiple stages of complex deformation dominated by an NW to SW normal fault system, high relief horsts, and half-graben and graben structures. Moreover, 3D SM and fault system models (FSMs) show that the middle part of the sequence underwent greater deformation compared to the areas surrounding the major faults, with predominant one oriented S30°–45° E and N25°–35° W; with the azimuth at 148°–170° and 318°–345°; and with the minimum (28°), mean (62°), and maximum (90°) dip angles. The applied variance edge attribute better portrays the inconsistencies in the seismic data associated with faulting, validating seismic interpretation. The high amplitude and loss of frequency anomalies of the sweetness and root mean square (RMS) attributes indicate gas-saturated sand. In contrast, the relatively low-amplitude and high-frequency anomalies indicate sandy shale, shale, and pro-delta facies. The petrophysical modeling results show that the E sand interval exhibits high effective porosity (∅_eff) and hydrocarbon saturation (S_hc) compared to the G sand interval. The average petrophysical properties we identified, such as volume of shale (V_shale), average porosity (∅_avg), ∅_eff, water saturation (S_W), and the S_hc of the E sand interval, were 30.5%, 17.4%, 12.2%, 33.2% and, 70.01%, respectively. The findings of this study can help better understand the reservoir’s structural and stratigraphic characteristics, the spatial distribution of associated facies, and petrophysical properties for reliable reservoir characterization. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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15 pages, 12361 KiB

Open AccessArticle

Applications of Radial Basis Functional Link Networks in the Exploration for Lala Copper Deposits in Sichuan Province, China

by Xiumei Lv, Wangdong Yang, Xiaoning Liu and Gongwen Wang

Minerals 2022, 12(3), 352; https://0-doi-org.brum.beds.ac.uk/10.3390/min12030352 - 15 Mar 2022

Cited by 2 | Viewed by 1684

Abstract

The Lala copper area in Huili County, Sichuan Province, China, is favored by superior regional metallogenic geological conditions due to its location in an extremely important copper–iron metallogenic belt in southwest China, and it has witnessed the formation of a series of unique iron–copper deposits following the superposition of multiple tectonic events. In recent years, major mineral exploration breakthroughs have been achieved in the deep and peripheral zones of this area. Using the Lala copper mining area in Sichuan as an example, this paper describes metallogenic prediction research carried out based on multivariate geoscience information (geological information, geophysics, geochemistry, and remote sensing data) and the application of geographic information system (GIS) technology and the radial basis function neural network (RBFLN) model. The five specific aspects covered in this paper are as follows: (1) we collected geology–geophysics–geochemistry remote sensing data and other information, adopted GIS technology to extract multivariate geoscience ore-forming anomaly information, and established a geoscience prospecting information database; (2) we applied the RBFLN algorithm for information on integrated analysis of ore-forming anomalies in the study area; (3) we applied a statistical method to divide the threshold value to delineate favorable ore-prospecting target areas; (4) we applied three-dimensional (3D) visualization technology, through which sample assistance was verified, to evaluate the performance of the RBFLN model; and (5) the results revealed that the RBFLN model can integrate multivariate and multi-type geoscience information and effectively predict metallogenic prospective areas and delineate favorable target areas. The metallogenic prediction method based on RBFLN technology provides a scientific basis for the exploration and deployment of minerals in the study area. It is obvious that the methods to predict and evaluate mineral resources are developing towards model integration and information intelligent analysis. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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Review

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Open AccessReview

Micro-Mechanisms and Implications of Continental Red Beds

by Wang He, Zhijun Yang, Hengheng Du, Jintao Hu, Ke Zhang, Weisheng Hou and Hongwei Li

Minerals 2022, 12(8), 934; https://0-doi-org.brum.beds.ac.uk/10.3390/min12080934 - 25 Jul 2022

Cited by 5 | Viewed by 2414

Abstract

Continental red beds, widely formed at various geologic timescales, are sedimentary rocks and sediments with red as the main color. Geoscientists have analyzed the geomorphology, paleomagnetism, paleoenvironments, paleontology, energy, and minerals in continental red beds. Despite the agreement that fine-grained hematite is closely related to the color of continental red beds, controversies and problems still exist regarding the micro-mechanism of their formation. As a review, this paper details the composition and color properties of pigmentation in red beds, analyzes the existence and distribution of authigenic hematite, and summarizes the iron sources and the formation of hematite. In addition, we introduce the fading phenomenon observed in continental red beds, including three types of secondary reduction zones: reduction spots, reduction strips, and reduction areas. Lastly, this paper summarizes the evolution of color in continental red beds, emphasizes the relationship between authigenic hematite and the diagenetic environment, and proposes possible research directions for future red bed-related issues. Full article

(This article belongs to the Special Issue 3D/4D Geological Modeling for Mineral Exploration)

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