Renguang Zuo

Renguang Zuo
China University of Geosciences · State Key Laboratory of Geological Processes and Mineral Resources

Professor

About

193
Publications
37,386
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8,426
Citations
Introduction
Mineral exploration; Geochemical exploration; Big data analytics; Artificial intelligence; Mathematical Geosciences; Data science

Publications

Publications (193)
Article
Geochemical survey data are a type of spatial big data that play an increasingly significant role in mineral exploration. One challenge in the era of big data is how to mine geochemical data in support of mineral exploration. In this study, based on a generative adversarial network framework, we proposed an unsupervised spatial–spectrum dual‐branch...
Article
Mineral prospectivity mapping (MPM) is recognized as an essential tool for targeting new mineral deposits. MPM typically comprises two end-member approaches: knowledge-driven and data-driven. Knowledge-driven MPM relies on expert knowledge, which is based on causal relationships but is not readily adaptable to dynamic changes. Data-driven MPM is ca...
Article
Various deep learning algorithms have been employed for mineral prospectivity mapping (MPM) owing to their powerful capacity for automatic extraction of high-level representations from multisource data. However, extending the application of deep neural networks to areas with different ore-forming characteristics remains challenging because it requi...
Article
In recent years, numerous countries have initiated geochemical survey projects, highlighting the importance of identifying geochemical anomalies for the discovery of potential mineral deposits. In addition, anthropogenic activity, missing or inaccurate data, and overburden can lead to local enrichment or deficiency of elements, resulting in false o...
Article
Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as "industrial vitamins," making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with spec...
Article
Mineral prospectivity mapping (MPM) is designed to reduce the exploration search space by combining and analyzing geological prospecting big data. Such geological big data are too large and complex for humans to effectively handle and interpret. Artificial intelligence (AI) algorithms, which are powerful tools for mining nonlinear mineralization pa...
Article
The identification of mineral deposit footprints by processing geochemical survey data constitutes a crucial stage in mineral exploration because it provides valuable and substantial information for future prospecting endeavors. However, the selection of appropriate pathfinder elements and the recognition of their anomalous patterns for determining...
Article
Full-text available
Geochemical mapping is a fundamental tool for elucidating the distribution and behaviour of economically significant elements and providing valuable insights into geological processes. Nevertheless, the quantification of uncertainty associated with geochemical mapping has only recently become a subject of widespread concern. This study presents a p...
Article
Graph-based models have been utilized for mineral prospectivity mapping (MPM), and they have demonstrated excellent performance owing to their adaptable graph structure, which is conducive to comprehensively considering the spatial anisotropy of mineralization compared with pixel- or image-based models. However, widely used graph-based models canno...
Article
Various deep learning algorithms (DLAs) have been successfully employed for mineral prospectivity mapping (MPM) to support mineral exploration, due to their superior nonlinear extraction capabilities. DLAs algorithms are typically purely data-driven approaches that may ignore the geological domain knowledge. This renders the predictive results inco...
Article
The behavior and evolution trajectory of hydrofracture, which show a close relationship with the hydrothermal mineralization process, is greatly influenced by fluid flow and fluid pressure. However, further investigation is needed to achieve an in-depth understanding of the formation and evolution mechanisms behind the link between the rate of flui...
Article
The purpose of mineral prospectivity mapping (MPM) is to discover unknown mineral deposits by means of fusing multisource prospecting information. In recent years, with rapid advancements in artificial intelligence, deep learning algorithms (DLAs) as a groundbreaking technique have exhibited outstanding capabilities in geoscience. However, conventi...
Article
Full-text available
Geochemical mapping is a crucial tool that can provide valuable insights for a wide range of applications, including mineral resources prospecting, environmental impact assessment, geological process understanding, and climate change research. Despite its significance, geochemical mapping requires spatial modeling based on sparse, heterogeneous, an...
Article
Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical survey data. Yet purely data-driven DL algorithms often lack logical explanations and geological consistency, occasionally clashing with known geological insights and complicating model interpretation...
Preprint
Full-text available
Geochemical mapping is a fundamental tool for elucidating the distribution and behaviour of economically significant elements, and providing valuable insights for geological processes. Nevertheless, the quantification of uncertainty associated with geochemical mapping has recently become a subject of widespread concern. This study presents a proced...
Article
Full-text available
Hyperspectral remote sensing images are characterized by nanoscale spectral resolution and hundreds of continuous spectral bands, dominating significantly in geological applications ranging from lithological mapping to mineral exploration. A major challenge lies in how to incorporate spectral and spatial information, therefore promote classificatio...
Article
Various artificial intelligence (AI) algorithms have been employed successfully to map mineral prospectivity for a specific mineral deposit type to assist mineral exploration. Numerous tools have been developed to incorporate AI algorithms, such as ArcSDM and ArcGIS. However, existing tools remain inadequate for geologist-friendly functions, and th...
Article
Ecological restoration of rare earth element (REE) mining areas has become a key component in the sustainable development of the ion-adsorption-type REE industry. Current studies on ecological restoration assessments are mainly based on a comparison of REE mining ranges conducted over two or more periods, therefore, the dynamic evolution of spatial...
Article
We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been pro...
Article
Here, we propose a new concept, ‘new generation artificial intelligence (AI) algorithms for mineral prospectivity mapping (MPM)’, which places greater emphasis on interpretability and domain cognitive consistency than the established machine learning (ML) algorithms pertaining to MPM. More specifically, the newly proposed algorithms are designed to...
Article
Various data-driven mineral prospectivity mapping (MPM) methods have been successfully adopted to delineate prospecting regions for a specific type of mineral deposit. These methods are mainly developed based on pixel-wise or image (pixel-patch) data, which do not adequately consider the spatial patterns linked to mineralization or the spatial char...
Article
Uncertainty associated with the identification of geochemical anomalies linked to mineralization has been a major concern in processing geochemical survey data. In this study, a Monte Carlo-based workflow, consisting of simulation-based local singularity analysis (LSA) and distance-based generalized sensitivity analysis (DGSA), was presented to ide...
Article
Machine learning algorithms, including supervised and unsupervised learning ones, have been widely used in mineral prospectivity mapping. Supervised learning algorithms require the use of numerous known mineral deposits to ensure the reliability of the training results. Unsupervised learning algorithms can be applied to areas with rare or no known...
Article
Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey data for mineral exploration. However, it is difficult to understand their working mechanisms and decision-making behaviors, which may lead to unreliable results. The construction of a reliable and interpretable DLA has become a focus in data-driven geoscience d...
Article
Full-text available
It is assumed that variable fluid pressure states in space control the spatiotemporal evolution of hydrofracture, and their responses on mineralization. In this study, mineral precipitation caused by rapid fluid pressure reduction is incorporated into a cellular automaton model to explore the effects of various spatially structured fluid pressure r...
Article
Geochemical survey data play a critical role in geological studies, mineral exploration, and environmental applications by providing information on geological events and processes such as mineralization and pollution. A typical geochemical survey dataset contains the analysis of multiple elements. For example, the National Geochemical Mapping Proje...
Article
Full-text available
Geological mapping in vegetation coverage areas is a challenging task. In this study, convolutional neural networks (CNNs) were employed for geological mapping in a vegetation coverage area based on remote sensing images and geochemical survey data. The Gram-Schmidt fusion technology was first applied to fuse Sentinel-2A and ASTER remote sensing im...
Article
Mineral prospectivity mapping (MPM) mainly focuses on searching prospective areas for a particular type of mineral deposits. However, MPM is typically subject to uncertainties originated from conceptual mineral deposit models, geoscience data, and prediction models. This study utilizes a hybrid model combining a direct sampling algorithm and a conv...
Article
Mapping of geochemical anomalies is crucial to exploration and environmental geochemistry. The complex geochemical landscape, multiple geological sources and various secondary surficial processes impose a certain degree of spatial uncertainty in mapping of geochemical anomalies. Quantifying such uncertainty is significant for improving the efficien...
Article
Multi-source data integration for mineral prospectivity mapping (MPM) is an effective approach for reducing uncertainty and improving MPM accuracy. Multi-source data (e.g., geological, geophysical, geochemical, remote sensing, and drilling) should first be identified as evidence layers that represent ore-prospecting-related features. Traditional me...
Article
The identification of multivariate geochemical anomalies is critical in mineral exploration. Machine learning algorithms have been successfully employed in the recognition of multivariate geochemical anomalies in support of mineral exploration, owing to their strong ability to learn the complex relationship between geochemical characteristics and m...
Article
Geochemical prospecting plays an important role in mineral exploration. In recent years, deep learning algorithms (DLAs) have been applied in mapping geochemical anomalies associated with mineralization. However, few of them evaluated the effects of data and model uncertainty on geochemical anomaly identification, which can introduce bias and risk...
Article
Geochemical mapping based on machine learning algorithms has been proven to significantly improve the efficiency of geological mapping related to mineral exploration. This process is generally implemented by interpolating discrete geochemical data into spatially continuous fields and comparing chemical composition and spatial distribution to a refe...
Article
Mineral prospectivity mapping (MPM) aims to reduce the areas for searching of mineral deposits. Various statistical models that have been successfully adopted to delineate prospecting regions for a specific type of mineral deposit can be divided into pixel-wise, image- (or pixel-patch), and graph-based approaches. The pixel-wise models, which frequ...
Article
The late Mesozoic to Cenozoic tectonothermal history of the basin-and-range system within the Cathaysia Block in the southeastern South China Block remains poorly constrained, despite its significance in the tectonic and topographic evolution of eastern China. Zircon and apatite fission-track thermochronometers were applied to reconstruct the cooli...
Article
Mapping of lithological units is a significant challenge for geological tasks. Stream sediment geochemical survey data contain abundant geological information that can help delineate lithological units. In this study, a convolutional neural network (CNN) was applied to map the lithological units in the Daqiao gold District, West Qinling Orogen, Chi...
Article
Deep learning algorithms (DLAs) have achieved better results than traditional methods in the field of multivariate geochemical anomaly recognition because of their strong ability to extract feature from nonlinear data. However, most of DLAs are black-box approaches because of the high nonlinearity characteristics of the hidden layer. In addition, t...
Article
The complexity of geochemical patterns in the surficial media makes it necessary to consider the uncertainty in the process of identifying geochemical anomalies. The existence of various anomaly detectors that define geochemical anomaly under different assumptions, constitutes an important source of such uncertainty. In this study, the model averag...
Article
Deep learning algorithms (DLAs) are becoming popular tools for mineral prospectivity mapping. However, purely data-driven DLAs frequently ignore expert and domain knowledge, imposing difficulty in interpretability from a geological perspective. The efficient integration of geological knowledge into DLAs remains challenging in geosciences. In this s...
Article
Geochemical prospecting is an important and effective approach for discovering mineral deposits. Collection, management, visualization, interpretation, modeling, and publishing of geochemical survey data remain challenging. The main aim of this study is to illustrate the application of Google Earth in the visualization and interpretation of geochem...
Article
The effective identification of geochemical anomalies is essential in mineral exploration. Recently, data-driven deep learning algorithms have gained popularity for recognizing the geochemical patterns linked to mineralization. While purely data-driven deep learning algorithms can exploit geochemical patterns well, but the predicted and extracted r...
Article
The regolith-hosted rare earth elements (REE) deposits are the dominant source of the global heavy REE resources. This study proposed a convolutional neural network (CNN) architecture to integrate the multi-source data (e.g., geological, geochemical and geomorphological data) to map mineral prospectivity of regolith-hosted REE deposits. To solve th...
Article
Full-text available
Urban expansion is generally accompanied by a series of ecological problems; therefore, it is of great significance to strengthen the research on urban expansion to effectively guide and control urban expansion. In this study, we used a one-class support vector machine (OCSVM) based on Landsat image data to extract the construction land area in Xia...
Article
Full-text available
Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning metho...
Article
The application of deep learning algorithms in mineral prospectivity mapping (MPM) is a hot topic in mineral exploration. However, few studies have focused on recurrent neural networks (RNNs) in terms of integrating different evidential layers to map mineral potential. In this study, a gated recurrent unit (GRU) model was employed for MPM using a c...
Article
Iron oxide-copper-gold (IOCG) and iron oxide-apatite (IOA) are two significant mineral deposit types with similar tectonic settings and hydrothermal alteration characteristics. There are huge differences in the geological setting, alteration system, and ore-forming fluid composition among IOCG and IOA deposits, leading to controversial genesis. Dis...
Article
Quantification and recognition of geochemical patterns are extremely important for geochemical prospecting and can facilitate a better understanding of regional metallogenesis. Recognition of such patterns with deep learning (DL) algorithms has attracted considerable attention, as these algorithms can generally extract high-level geochemical featur...
Article
Full-text available
With the rapid development in the global economy and technology, urbanization has accelerated. It is important to characterize the urban expansion and determine its driving force. In this study, we used the Xiaonan District in Hubei Province, China, as an example to map and quantify the spatiotemporal dynamics of urban expansion from the two perspe...
Article
Geochemical exploration has provided significant clues for mineral exploration and has helped discover many mineral deposits. Although various methods, including classic statistics, multivariate statistics, geostatistics, fractal/multifractal models, and machine learning algorithms, have been successfully employed to process geochemical exploration...
Article
The successful application of geographic information system (GIS)-based mineral prospectivity mapping (MPM) essentially relies on two factors: one is reasonable evidential layers that conform to geological cognition, and the other is excellent models that can extract critical prospecting information from evidential layers. Geological features in MP...
Chapter
Geographical information system (GIS) is gaining its popularity beyond geography and information technology (IT) with its strong power in managing and analysing spatial data. In medical geology, GIS provides two main useful functions: (a) mapping and (b) spatial analysis. It contains specialised computer software and hardware designed to process da...
Article
We have entered the fourth research paradigm with the overwhelming availability of vast amounts of data. The process and mining these data for a better understanding of earth systems and predicting mineral resources is challenging. This study discusses a data-driven knowledge discovery of geochemical patterns and presents a case study of geochemica...
Article
Geochemical exploration data is popular in mineral exploration in that it plays a notable role in discovering unknown mineral deposits. In this study, we review the state-of-the-art popular methods for processing geochemical exploration data and for identifying geochemical anomalies associated with mineralization. The distribution laws of geochemic...
Article
Weathering would produce damage to objects. This paper proposed a method to monitor geochemical variations on outcrop surfaces, through in situ measurement using portable X-ray fluorescence (pXRF) analyser at high spatial resolution, and qualitative and quantitative determination of elemental gains and losses based on spatial patterns of elemental...
Article
In this study, a GANomaly network was used to detect geochemical anomalies related to mineralization in the southern part of Jiangxi Province and its adjacent areas in China. The training data used in this study belong to a typical rare-sample category of imbalanced data samples; thus, during the training phase, only non-mineralized dataset randoml...
Article
Machine learning (ML) algorithms are widely applied in various fields owing to their strong ability to abstract high-level features from a large number of training samples. However, few supervised ML algorithms have been applied in geochemical prospecting and mineral exploration because mineralization is a rare geological event that leads to an ins...
Article
The complexity of geochemical patterns in surficial media makes it necessary to consider the uncertainty when identifying geochemical anomalies in geochemical prospecting. In this contribution, the type of uncertainty related to spatial modelling of geochemical element distribution based on a limited number of observations is considered. A hybrid m...
Article
Deep learning (DL) algorithms have a strong ability to recognize high-level features in geochemical exploration data and have been widely employed for the recognition of multivariate geochemical anomalies linked to mineralization. In this study, the adversarially learned anomaly detection (ALAD) algorithm, an improved generative adversarial network...
Article
Quantitative prediction of mineral resources needs the support of data science urgently as the field has now changed from qualitative to quantitative, from data sparse to data intensive. On the basis of previous studies, this paper discusses data science-based theory and method of quantitative prediction of mineral resources. The theoretical basis...
Article
GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for delineating and better constraining target areas deemed prospective for mineral deposits of a particular type. The underlying algorithms are well-established and well-understood, but on the whole, MPM that is a multi-faceted and multi-criteria approach, is faced with...
Article
Full-text available
p class="15" align="justify">Geological big data is growing exponentially. Only by developing intelligent data processing methods can we catch up with the extraordinary growth of big data. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Machine learning has become the frontier hotspot o...
Article
Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical exploration data typically contain a proportion of large variations and missing values, which motivated us to construct a network architecture optimized to deal with these data. Our approach adopted a...
Article
Application of supervised machine learning algorithms for mineral prospectivity mapping (MPM) requires positive and negative training samples. Typically, known mineral deposits are considered as positive training samples. However, the selection of negative training samples in the process of MPM is challenging. The one-class classification methods r...
Article
Mineral resources prediction and assessment is one of the most important tasks in geosciences. Geochemical anomalies, as direct indicators of the presence of mineralization, have played a significant role in the search of mineral deposits in the past several decades. In the near future, it may be possible to recognize subtle geochemical anomalies t...
Article
Multisource geoscience data can provide significant information for mineral exploration in a variety of ways. For example, remote-sensing images record the spectral characteristics of objects, and geochemical data represent the enrichment or depletion of geochemical elements, which reflect the physical and chemical attributes of geological features...
Article
Convolutional neural network (CNN) has demonstrated promising performance in classification and prediction in various fields. In this study, a CNN is used for mineral prospectivity mapping (MPM) in the southwestern Fujian Province, China. Two limitations of applying CNNs in MPM are addressed: insufficient labeled samples and difficulty of applying...
Article
Recently, deep learning (DL) algorithms have received increased attention in various fields. In the field of geoscience, DL has been shown to be a powerful tool for mining complex, high-level, and non-linear geospatial data and for extracting previously unknown patterns related to geological processes. In this study, a deep variational autoencoder...
Article
Geochemical patterns in the surficial media are complex because of the various processes involved in their formation. These processes may or may not relate to mineralization. Therefore, geochemical anomalies identified are usually subject to uncertainty. Geographically weighted lasso (GWL) was adopted in this study to model the non-stationary relat...
Article
This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between geological prospecting big data (GPBD) and locations of known mineralization. Geodata science reveals the inter-correlations between GPBD and mineralization, converts GPBD into mappable crite...
Article
Full-text available
The widely distributed leucogranite belt in the Himalayan orogeny is expected to have excellent potential for developing of rare metal mineralization. Finding a way to effectively map the spatial distribution of leucogranites would be a significant contribution to rare metal exploration. Research shows remote sensing technology has long been recogn...
Article
Supervised data-driven mineral prospectivity mapping (MPM) usually employs both positive and negative training datasets. Positive training datasets are typically created using the locations of known mineral deposits, whereas negative training datasets can be generated using the locations of random points. However, not all the negative points can be...
Article
The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links...
Article
Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in r...
Article
Full-text available
Quantification of a mineral prospectivity mapping (MPM) heavily relies on geological, geophysical and geochemical analysis, which combines various evidence layers into a single map. However, MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples. In this paper, we provide a fra...
Article
Full-text available
The distribution of geochemical elements in the surficial media is the end product of geochemical dispersion under complex geological conditions. This study explored the frequency and spatial distribution characteristics of geochemical elements and their associations. It quantifies the frequency distribution via mean, variance, skewness and kurtosi...
Article
Rare metals play a considerable role in the development of new materials and energy, making them key mineral resources for global competition. Widely distributed along the Himalayan orogen, the Himalayan leucogranite belt is expected to be an important rare metal metallogenic belt in China. Thus, mapping the spatial distribution of Himalayan leucog...
Article
Full-text available
The Greater Bay Area (GBA) of China is experiencing a high level of exposure to outdoor PM2.5 pollution. The variations in the exposure level are determined by spatiotemporal variations in the PM2.5 concentration and population. To better guide public policies that aim to reduce the population exposure level, it is essential to explicitly decompose...
Article
Geodata science (GDS) is an interdisciplinary field in which geoscience data are mined for us to well understand the origin, evolution and future of our Earth and planet with prediction and assessment of its resources, environments, and natural hazards. The data chain of GDS involves collecting geosciences data, mining geoinformation, discovering g...
Article
A number of volcanic-type uranium deposits occur in Pucheng district, Fujian province, China. Structural controls and volcanic rocks clearly explain the uranium mineralization in this area. To explore the subtle spatial associations of uranium occurrences with certain geological features, these uranium deposits were subjected to L–function, Fry, an...
Article
Fractal and multifractal models, including the concentration-area (C–A) fractal model, spectrum-area (S–A) multifractal model, and local singularity analysis (LSA) method, are widely applied when processing various geoscience datasets. However, there is lack of ArcGIS-based software that contains these popular fractal and multifractal models. Such...
Preprint
In this paper, multifractal analysis based on moment method was used to explore the distribution characteristics of geochemical patterns and their relationships. Criterion considering the convergence behavior of singularity index function was also presented here to help determine an appropriate moment range for performing multifractal analysis. A c...
Article
The majority of machine learning algorithms that have been applied in data-driven predictive mapping of mineral prospectivity require a sufficient number of training samples (known mineral deposits) to obtain results with high performance and reliability. Semi-supervised learning can take advantage of the huge amount of unlabeled data to benefit th...
Article
Among the various mechanisms for magma ascent and emplacement, rock fracturing is a highly significant factor. In this study, a cellular automaton based on the Olami-Feder-Christensen model was used to generate a self-organized network in which magma can ascend and be arrested to form granitic intrusions under the influence of their buoyancy. The m...
Article
Accessing geochemical compositions on the hand-specimen scale provides important clues towards understanding fluid-rock interaction in mineralization. In this study, we used the ITRAX core scanner to detect the geochemical composition of a hand specimen obtained from Yangshan skarn-type Iron deposits in China. In order to investigate fine-scale nat...

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