Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Mineral Prospectivity Mapping via Gated Recurrent Unit Model
Bojun Yin, Renguang Zuo, Yihui Xiong
Natural Resources Research (2021) Vol. 31, Iss. 4, pp. 2065-2079
Closed Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

Graph Deep Learning Model for Mapping Mineral Prospectivity
Renguang Zuo, Ying Xu
Mathematical Geosciences (2022) Vol. 55, Iss. 1, pp. 1-21
Closed Access | Times Cited: 78
A New Generation of Artificial Intelligence Algorithms for Mineral Prospectivity Mapping
Renguang Zuo, Yihui Xiong, Ziye Wang, et al.
Natural Resources Research (2023) Vol. 32, Iss. 5, pp. 1859-1869
Closed Access | Times Cited: 45
Assimilation of the chronology of mineral system components in prospectivity analysis procedure for mineral exploration targeting: Adaptation of recurrent neural networks
Soran Qaderi, Abbas Maghsoudi, Mahyar Yousefi, et al.
Journal of Geochemical Exploration (2025) Vol. 272, pp. 107706-107706
Closed Access | Times Cited: 3
Geological Knowledge‐Guided Dual‐Branch Deep Learning Model for Identification of Geochemical Anomalies Related to Mineralization
Ying Xu, Renguang Zuo, Yang Bai
Journal of Geophysical Research Machine Learning and Computation (2025) Vol. 2, Iss. 1
Open Access | Times Cited: 2
Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping
Jiangning Yin, Nan Li
Ore Geology Reviews (2022) Vol. 145, pp. 104916-104916
Open Access | Times Cited: 66
A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping
Renguang Zuo, Zijing Luo, Yihui Xiong, et al.
Natural Resources Research (2022) Vol. 31, Iss. 3, pp. 1121-1133
Closed Access | Times Cited: 62
Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data
Tong Li, Renguang Zuo, Xin-Fu Zhao, et al.
Ore Geology Reviews (2022) Vol. 142, pp. 104693-104693
Open Access | Times Cited: 59
Learning 3D mineral prospectivity from 3D geological models using convolutional neural networks: Application to a structure-controlled hydrothermal gold deposit
Hao Deng, Yang Zheng, Jin Chen, et al.
Computers & Geosciences (2022) Vol. 161, pp. 105074-105074
Open Access | Times Cited: 50
An Interpretable Graph Attention Network for Mineral Prospectivity Mapping
Ying Xu, Renguang Zuo
Mathematical Geosciences (2023) Vol. 56, Iss. 2, pp. 169-190
Closed Access | Times Cited: 34
A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration
Renguang Zuo, Ying Xu
Computers & Geosciences (2023) Vol. 182, pp. 105490-105490
Closed Access | Times Cited: 26
Geologically Constrained Convolutional Neural Network for Mineral Prospectivity Mapping
Fanfan Yang, Renguang Zuo
Mathematical Geosciences (2024) Vol. 56, Iss. 8, pp. 1605-1628
Closed Access | Times Cited: 13
Mineral Prospectivity Mapping Based on a Novel Self-Ensembling Graph Convolutional Network
Yonghang Lou, Yue Liu
Mathematical Geosciences (2025)
Closed Access | Times Cited: 1
Quantification of Uncertainty Associated with Evidence Layers in Mineral Prospectivity Mapping Using Direct Sampling and Convolutional Neural Network
Fanfan Yang, Ziye Wang, Renguang Zuo, et al.
Natural Resources Research (2022) Vol. 32, Iss. 1, pp. 79-98
Closed Access | Times Cited: 35
Mineral Prospectivity Mapping Using Deep Self-Attention Model
Bojun Yin, Renguang Zuo, Siquan Sun
Natural Resources Research (2022) Vol. 32, Iss. 1, pp. 37-56
Closed Access | Times Cited: 34
Developments in Quantitative Assessment and Modeling of Mineral Resource Potential: An Overview
Yue Liu, Emmanuel John M. Carranza, Qinglin Xia
Natural Resources Research (2022) Vol. 31, Iss. 4, pp. 1825-1840
Open Access | Times Cited: 30
Deep Reinforcement Learning for Mineral Prospectivity Mapping
Zixian Shi, Renguang Zuo, Bao Zhou
Mathematical Geosciences (2023) Vol. 55, Iss. 6, pp. 773-797
Closed Access | Times Cited: 19
Mineral prospectivity mapping using knowledge embedding and explainable ensemble learning: A case study of the Keeryin ore concentration in Sichuan, China
Shitao Yin, Nan Li, Keyan Xiao, et al.
Ore Geology Reviews (2024) Vol. 167, pp. 106017-106017
Open Access | Times Cited: 5
Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data
Ziye Wang, Renguang Zuo, Fanfan Yang
Mathematical Geosciences (2022) Vol. 55, Iss. 7, pp. 1035-1058
Closed Access | Times Cited: 20
Supervised Mineral Prospectivity Mapping via Class-Balanced Focal Loss Function on Imbalanced Geoscience Datasets
Zhiqiang Zhang, Yingjie Li, Gongwen Wang, et al.
Mathematical Geosciences (2023) Vol. 55, Iss. 7, pp. 989-1010
Closed Access | Times Cited: 13
A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping
Luyi Shi, Ying Xu, Renguang Zuo
Natural Resources Research (2024) Vol. 33, Iss. 4, pp. 1365-1376
Closed Access | Times Cited: 5
Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction
Weilin Chen, Xiaogang Ma, Zhe Wang, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 3, pp. 1819-1835
Open Access | Times Cited: 4
Predictive Modeling of Prospectivity for VHMS Mineral Deposits, Northeastern Bathurst Mining Camp, NB, Canada, Using an Ensemble Regularization Technique
Mohammad Parsa, David R. Lentz, James A. Walker
Natural Resources Research (2022) Vol. 32, Iss. 1, pp. 19-36
Closed Access | Times Cited: 17
Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification
Ying Xu, Renguang Zuo
Mathematical Geosciences (2024) Vol. 57, Iss. 2, pp. 307-331
Closed Access | Times Cited: 4
Tungsten prospectivity mapping using multi-source geo-information and deep forest algorithm
Yue Liu, Tao Sun, Kaixing Wu, et al.
Ore Geology Reviews (2025), pp. 106452-106452
Open Access
Anomaly Detection-Oriented Positive-Unlabeled Metric Learning for Extracting High-Dimensional Geochemical Anomalies Linked to Mineralization
Zhoujun Yang, Yongliang Chen
Natural Resources Research (2025)
Closed Access
Page 1 - Next Page

Scroll to top