World's Best Scientists 2026 revealed!

D-Index & Metrics

Earth Science

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

Renguang Zuo publication distribution in Earth Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Earth Science in 2026. The highlighted bar marks where Renguang Zuo sits on this spectrum.

38–47 publications: 5 scientists 48–57 publications: 33 scientists 58–67 publications: 94 scientists 68–77 publications: 161 scientists 78–87 publications: 278 scientists 88–97 publications: 376 scientists 98–107 publications: 404 scientists 108–117 publications: 484 scientists 118–127 publications: 540 scientists 128–137 publications: 557 scientists 138–147 publications: 491 scientists 148–157 publications: 495 scientists 158–167 publications: 458 scientists 168–177 publications: 490 scientists 178–187 publications: 414 scientists 188–197 publications: 401 scientists 198–207 publications: 333 scientists 208–217 publications: 292 scientists 218–227 publications: 290 scientists 228–237 publications: 262 scientists 238–247 publications: 243 scientists 248–257 publications: 208 scientists 258–267 publications: 185 scientists 268–277 publications: 147 scientists 278–287 publications: 128 scientists 288–297 publications: 119 scientists 298–307 publications: 120 scientists 308–317 publications: 107 scientists 318–327 publications: 93 scientists 328–337 publications: 87 scientists 338–347 publications: 64 scientists 348–357 publications: 87 scientists 358–367 publications: 60 scientists 368–377 publications: 60 scientists 378–387 publications: 34 scientists 388–397 publications: 50 scientists 398–407 publications: 44 scientists 408–417 publications: 31 scientists 418–427 publications: 39 scientists 428–437 publications: 27 scientists 438–447 publications: 36 scientists 448–457 publications: 27 scientists 458–467 publications: 29 scientists 468–477 publications: 30 scientists 478–487 publications: 19 scientists 488–497 publications: 15 scientists 498–507 publications: 18 scientists 508–517 publications: 19 scientists 518–527 publications: 16 scientists 528–537 publications: 10 scientists 538–547 publications: 11 scientists 548–557 publications: 14 scientists 558–567 publications: 11 scientists 568–577 publications: 7 scientists 578–587 publications: 14 scientists 588–597 publications: 5 scientists 598–602 publications: 4 scientists 603+ publications: 100 scientists
38 publications 603+

This scientist: 179 publications — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 603 publications or more.

Renguang Zuo D-index placement in Earth Science in 2026

The chart shows the D-index (discipline H-index) distribution of Earth Science scientists ranked by Research.com in 2026. The highlighted bar marks where Renguang Zuo sits on this spectrum.

30 D-Index: 144 scientists 31 D-Index: 215 scientists 32 D-Index: 278 scientists 33 D-Index: 379 scientists 34 D-Index: 366 scientists 35 D-Index: 372 scientists 36 D-Index: 389 scientists 37 D-Index: 366 scientists 38 D-Index: 344 scientists 39 D-Index: 349 scientists 40 D-Index: 296 scientists 41 D-Index: 289 scientists 42 D-Index: 277 scientists 43 D-Index: 279 scientists 44 D-Index: 248 scientists 45 D-Index: 257 scientists 46 D-Index: 212 scientists 47 D-Index: 202 scientists 48 D-Index: 207 scientists 49 D-Index: 204 scientists 50 D-Index: 183 scientists 51 D-Index: 178 scientists 52 D-Index: 182 scientists 53 D-Index: 178 scientists 54 D-Index: 163 scientists 55 D-Index: 117 scientists 56 D-Index: 163 scientists 57 D-Index: 120 scientists 58 D-Index: 113 scientists 59 D-Index: 136 scientists 60 D-Index: 135 scientists 61 D-Index: 96 scientists 62 D-Index: 103 scientists 63 D-Index: 83 scientists 64 D-Index: 103 scientists 65 D-Index: 83 scientists 66 D-Index: 89 scientists 67 D-Index: 92 scientists 68 D-Index: 89 scientists 69 D-Index: 78 scientists 70 D-Index: 75 scientists 71 D-Index: 53 scientists 72 D-Index: 62 scientists 73 D-Index: 54 scientists 74 D-Index: 35 scientists 75 D-Index: 52 scientists 76 D-Index: 50 scientists 77 D-Index: 32 scientists 78 D-Index: 37 scientists 79 D-Index: 20 scientists 80 D-Index: 33 scientists 81 D-Index: 36 scientists 82 D-Index: 34 scientists 83 D-Index: 34 scientists 84 D-Index: 24 scientists 85 D-Index: 19 scientists 86 D-Index: 18 scientists 87 D-Index: 26 scientists 88 D-Index: 30 scientists 89 D-Index: 17 scientists 90 D-Index: 21 scientists 91 D-Index: 19 scientists 92 D-Index: 15 scientists 93 D-Index: 14 scientists 94 D-Index: 20 scientists 95 D-Index: 9 scientists 96 D-Index: 10 scientists 97 D-Index: 15 scientists 98 D-Index: 13 scientists 99 D-Index: 3 scientists 100 D-Index: 13 scientists 101 D-Index: 3 scientists 102 D-Index: 9 scientists 103 D-Index: 4 scientists 104 D-Index: 6 scientists 105 D-Index: 6 scientists 106+ D-Index: 98 scientists
30 D-Index 106+

This scientist: 60 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 106 D-Index or more.

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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