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Earth Science

D-Index
60
Citations
10390
World Ranking
1951
National Ranking
124

Overview

Renguang Zuo is affiliated with the China University of Geosciences in China. Their primary research focuses on geochemistry, geologic mapping, and related engineering and computer science disciplines, particularly the application of artificial intelligence in earth sciences.

Their recent scholarly work includes several articles published in well-regarded scientific journals. Notable papers include:

  • Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping, 2020, published in Natural Resources Research
  • Geodata Science-Based Mineral Prospectivity Mapping: A Review, 2020, published in Natural Resources Research
  • The processing methods of geochemical exploration data: past, present, and future, 2021, published in Applied Geochemistry
  • Uncertainties in GIS-Based Mineral Prospectivity Mapping: Key Types, Potential Impacts and Possible Solutions, 2021, published in Natural Resources Research
  • Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine, 2020, published in Computers & Geosciences

Zuo frequently collaborates with several researchers, including Yihui Xiong, Ziye Wang, Oliver P. Kreuzer, Fanfan Yang, and Jian Wang. Such collaborations have been reflected in multiple joint publications.

The venues for Zuo's publications are often those specializing in geosciences, computer science applications, and remote sensing. Frequent publication venues include:

  • Mathematical Geosciences (24 publications)
  • Natural Resources Research (15 publications)
  • Applied Geochemistry (12 publications)
  • Computers & Geosciences (8 publications)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (4 publications)

Renguang Zuo's research spans a number of main fields, prominently Engineering and Computer Science. Their work extensively covers subfields such as Artificial Intelligence, Media Technology, Mechanical Engineering, Environmental Engineering, and Mechanics of Materials.

The core topics of their investigations encompass a range of subjects related to earth and environmental sciences coupled with computational methods. These main topics include:

  • Geochemistry and Geologic Mapping
  • Remote-Sensing Image Classification
  • Mineral Processing and Grinding
  • Soil Geostatistics and Mapping
  • Hydrocarbon Exploration and Reservoir Analysis
  • Geological and Geochemical Analysis
  • Geological Modeling and Analysis

Best Publications

  • Support vector machine: A tool for mapping mineral prospectivity

    Renguang Zuo;Emmanuel John M. Carranza

  • Deep learning and its application in geochemical mapping

    Renguang Zuo;Yihui Xiong;Jian Wang;Emmanuel John M. Carranza

  • Fractal/multifractal modeling of geochemical data: A review

    Renguang Zuo;Jian Wang

  • Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China)

    Renguang Zuo

  • Recognition of geochemical anomalies using a deep autoencoder network

    Yihui Xiong;Renguang Zuo

  • Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China

    Renguang Zuo;Renguang Zuo;Qiuming Cheng;Qiuming Cheng;F.P. Agterberg;Qinglin Xia

  • Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods

    Renguang Zuo

  • Mapping mineral prospectivity through big data analytics and a deep learning algorithm

    Yihui Xiong;Renguang Zuo;Emmanuel John M. Carranza

  • Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization

    Renguang Zuo;Qinglin Xia;Haicheng Wang

  • A comparison study of the C–A and S–A models with singularity analysis to identify geochemical anomalies in covered areas

    Renguang Zuo;Qinglin Xia;Daojun Zhang

  • Application of fractal models to characterization of vertical distribution of geochemical element concentration

    Renguang Zuo;Qiuming Cheng;Qiuming Cheng;Qinglin Xia

  • Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods

    Renguang Zuo;Yihui Xiong

  • Spatial analysis and visualization of exploration geochemical data

    Renguang Zuo;Emmanuel John M. Carranza;Emmanuel John M. Carranza;Jian Wang

  • Geodata Science-Based Mineral Prospectivity Mapping: A Review

    Renguang Zuo

  • Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping

    Tong Li;Renguang Zuo;Yihui Xiong;Yong Peng

  • Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China

    Renguang Zuo

  • Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China

    Renguang Zuo;Zhenjie Zhang;Daojun Zhang;Emmanuel John M. Carranza

  • Fractal/multifractal modelling of geochemical exploration data

    Renguang Zuo;Emmanuel John M. Carranza;Qiuming Cheng

  • Identification of weak anomalies: A multifractal perspective

    Renguang Zuo;Jian Wang;Guoxiong Chen;Mingguo Yang

  • The processing methods of geochemical exploration data: past, present, and future

    Renguang Zuo;Jian Wang;Yihui Xiong;Ziye Wang

  • A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China

    ZhenJie Zhang;RenGuang Zuo;YiHui Xiong

Frequent Co-Authors

Qiuming Cheng
Qiuming Cheng China University of Geosciences
Emmanuel John M. Carranza
Emmanuel John M. Carranza University of the Free State
Jef Caers
Jef Caers Stanford University
Keith C. Clarke
Keith C. Clarke University of California, Santa Barbara
Stefano Albanese
Stefano Albanese University of Naples Federico II
Domenico Cicchella
Domenico Cicchella University of Sannio
Orlando Vaselli
Orlando Vaselli University of Florence
Annamaria Lima
Annamaria Lima University of Naples Federico II
Wei Li
Wei Li Tsinghua University
Chaosheng Zhang
Chaosheng Zhang University of Galway

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