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OpenAlex Citation Counts

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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:

Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia
Stephen Kuhn, Matthew J. Cracknell, Anya M. Reading
Geophysics (2018) Vol. 83, Iss. 4, pp. B183-B193
Open Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

Machine learning for data-driven discovery in solid Earth geoscience
Karianne J. Bergen, Paul A. Johnson, Maarten V. de Hoop, et al.
Science (2019) Vol. 363, Iss. 6433
Closed Access | Times Cited: 936
A review of machine learning in processing remote sensing data for mineral exploration
Hojat Shirmard, Ehsan Farahbakhsh, R. Dietmar Müller, et al.
Remote Sensing of Environment (2021) Vol. 268, pp. 112750-112750
Open Access | Times Cited: 242
Machine learning for landslides prevention: a survey
Zhengjing Ma, Gang Mei, Francesco Piccialli
Neural Computing and Applications (2020) Vol. 33, Iss. 17, pp. 10881-10907
Open Access | Times Cited: 150
A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities
Wei Han, Xiaohan Zhang, Yi Wang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2023) Vol. 202, pp. 87-113
Closed Access | Times Cited: 122
Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco
Imane Bachri, Mustapha Hakdaoui, Mohammed Raji, et al.
ISPRS International Journal of Geo-Information (2019) Vol. 8, Iss. 6, pp. 248-248
Open Access | Times Cited: 107
A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data
Hojat Shirmard, Ehsan Farahbakhsh, Elnaz Heidari, et al.
Remote Sensing (2022) Vol. 14, Iss. 4, pp. 819-819
Open Access | Times Cited: 65
Petrophysical heterogeneity of the early Cretaceous Alamein dolomite reservoir from North Razzak oil field, Egypt integrating well logs, core measurements, and machine learning approach
Souvik Sen, Mohamed Abioui, Shib Sankar Ganguli, et al.
Fuel (2021) Vol. 306, pp. 121698-121698
Closed Access | Times Cited: 59
PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the support vector machine, random forest, and XG boost machine learning algorithms
Ali Shebl, Dávid Abriha, Amr S. Fahil, et al.
Ore Geology Reviews (2023) Vol. 161, pp. 105652-105652
Open Access | Times Cited: 29
Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
Liang Liu, Tianbin Li, Chunchi Ma
Applied Sciences (2024) Vol. 14, Iss. 1, pp. 423-423
Open Access | Times Cited: 9
A positive and unlabeled learning algorithm for mineral prospectivity mapping
Yihui Xiong, Renguang Zuo
Computers & Geosciences (2020) Vol. 147, pp. 104667-104667
Closed Access | Times Cited: 57
Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest
Caifeng Zou, Luanxiao Zhao, Minghui Xu, et al.
Journal of Geophysical Research Solid Earth (2021) Vol. 126, Iss. 7
Closed Access | Times Cited: 48
Generative adversarial network for geological prediction based on TBM operational data
Chao Zhang, Minming Liang, Xueguan Song, et al.
Mechanical Systems and Signal Processing (2021) Vol. 162, pp. 108035-108035
Closed Access | Times Cited: 46
Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data
Alix Lachaud, Marcus Adam, Ilija Mišković
Minerals (2023) Vol. 13, Iss. 8, pp. 1073-1073
Open Access | Times Cited: 21
Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm
Yile Ao, Hongqi Li, Liping Zhu, et al.
Journal of Petroleum Science and Engineering (2018) Vol. 173, pp. 781-792
Closed Access | Times Cited: 55
Deep carbonate reservoir characterisation using multi-seismic attributes via machine learning with physical constraints
Yuanyuan Chen, Luanxiao Zhao, Jianguo Pan, et al.
Journal of Geophysics and Engineering (2021) Vol. 18, Iss. 5, pp. 761-775
Open Access | Times Cited: 40
Advanced land imager superiority in lithological classification utilizing machine learning algorithms
Ali Shebl, Timothy Kusky, Árpád Csámer
Arabian Journal of Geosciences (2022) Vol. 15, Iss. 9
Open Access | Times Cited: 27
Applying machine learning methods to predict geology using soil sample geochemistry
Timothy C.C. Lui, Daniel D. Gregory, Marek Anderson, et al.
Applied Computing and Geosciences (2022) Vol. 16, pp. 100094-100094
Closed Access | Times Cited: 25
An improved method for lithology identification based on a hidden Markov model and random forests
Pu Wang, Xiaohong Chen, Benfeng Wang, et al.
Geophysics (2020) Vol. 85, Iss. 6, pp. IM27-IM36
Closed Access | Times Cited: 34
Fusion of Geochemical and Remote-Sensing Data for Lithological Mapping Using Random Forest Metric Learning
Ziye Wang, Renguang Zuo, Linhai Jing
Mathematical Geosciences (2020) Vol. 53, Iss. 6, pp. 1125-1145
Closed Access | Times Cited: 34
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
Geological Mapping via Convolutional Neural Network Based on Remote Sensing and Geochemical Survey Data in Vegetation Coverage Areas
Ting Pan, Renguang Zuo, Ziye Wang
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023) Vol. 16, pp. 3485-3494
Open Access | Times Cited: 12
Lithological mapping in the Central African Copper Belt using Random Forests and clustering: Strategies for optimised results
Stephen Kuhn, Matthew J. Cracknell, Anya M. Reading
Ore Geology Reviews (2019) Vol. 112, pp. 103015-103015
Open Access | Times Cited: 35
Unsupervised Machine Learning for Lithological Mapping Using Geochemical Data in Covered Areas of Jining, China
Guopeng Wu, Guoxiong Chen, Qiuming Cheng, et al.
Natural Resources Research (2021) Vol. 30, Iss. 2, pp. 1053-1068
Closed Access | Times Cited: 26
Accuracy comparison of various remote sensing data in lithological classification based on random forest algorithm
Yantao Xi, Abdallah M. Mohamed Taha, Anqi Hu, et al.
Geocarto International (2022) Vol. 37, Iss. 26, pp. 14451-14479
Closed Access | Times Cited: 19
Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China
Chuan Zhang, Min Yi, Fawang Ye, et al.
Remote Sensing (2022) Vol. 14, Iss. 20, pp. 5122-5122
Open Access | Times Cited: 19
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