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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:

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: 51

Showing 1-25 of 51 citing articles:

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm
Tie Yan, Rui Xu, Shihui Sun, et al.
Petroleum Science (2023) Vol. 21, Iss. 2, pp. 1135-1148
Open Access | Times Cited: 57
Porosity prediction using semi-supervised learning with biased well log data for improving estimation accuracy and reducing prediction uncertainty
Wenjing Sang, Sanyi Yuan, Hongwei Han, et al.
Geophysical Journal International (2022) Vol. 232, Iss. 2, pp. 940-957
Closed Access | Times Cited: 41
Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?
Yunyue Elita Li, Daniel O’Malley, Greg Beroza, et al.
Journal of Geophysical Research Solid Earth (2023) Vol. 128, Iss. 1
Closed Access | Times Cited: 26
Estimating shear wave velocity in carbonate reservoirs from petrophysical logs using intelligent algorithms
Mohammad Mehrad, Ahmad Ramezanzadeh, Mahdi Bajolvand, et al.
Journal of Petroleum Science and Engineering (2022) Vol. 212, pp. 110254-110254
Closed Access | Times Cited: 36
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
Review of machine learning methods applied to enhanced geothermal systems
Long Wang, Ziwang Yu, Yanjun Zhang, et al.
Environmental Earth Sciences (2023) Vol. 82, Iss. 3
Closed Access | Times Cited: 15
Unsupervised contrastive learning: Shale porosity prediction based on conventional well logging
Lu Qiao, Shengyu Yang, Qinhong Hu, et al.
Physics of Fluids (2024) Vol. 36, Iss. 5
Open Access | Times Cited: 6
Reservoir porosity assessment and anomaly identification from seismic attributes using Gaussian process machine learning
Maulana Putra, Maman Hermana, Ida Bagus Suananda Yogi, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1315-1327
Open Access | Times Cited: 5
Development of machine learning-based burst capacity models for pipelines containing dent-gouges with synthetic full-scale burst test data generated using tabular generative adversarial network
Z. He, Wenxing Zhou
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108090-108090
Open Access | Times Cited: 5
Submarine landslide susceptibility assessment integrating frequency ratio with supervised machine learning approach
Xiangshuai Meng, Xiaolei Liu, Yueying Wang, et al.
Applied Ocean Research (2024) Vol. 153, pp. 104237-104237
Closed Access | Times Cited: 5
A comparison of machine learning methods to predict porosity in carbonate reservoirs from seismic-derived elastic properties
Caifeng Zou, Luanxiao Zhao, Fei Hong, et al.
Geophysics (2022) Vol. 88, Iss. 2, pp. B101-B120
Closed Access | Times Cited: 21
Joint use of multiseismic information for lithofacies prediction via supervised convolutional neural networks
Minghui Xu, Luanxiao Zhao, Shunli Gao, et al.
Geophysics (2022) Vol. 87, Iss. 5, pp. M151-M162
Closed Access | Times Cited: 20
Porosity prediction from prestack seismic data via deep learning: incorporating a low-frequency porosity model
Jingyu Liu, Luanxiao Zhao, Minghui Xu, et al.
Journal of Geophysics and Engineering (2023) Vol. 20, Iss. 5, pp. 1016-1029
Open Access | Times Cited: 12
Machine Learning Prediction of Permeability Distribution in the X Field Malay Basin Using Elastic Properties
Zaky Ahmad Riyadi, John Oluwadamilola Olutoki, Maman Hermana, et al.
Results in Engineering (2024), pp. 103421-103421
Open Access | Times Cited: 4
Seismic Porosity Prediction in Tight Carbonate Reservoirs Based on a Spatiotemporal Neural Network
Fei Li, Zhiyi Yu, Wang Yong-gang, et al.
Processes (2025) Vol. 13, Iss. 3, pp. 788-788
Open Access
Porosity prediction based on improved structural modeling deep learning method guided by petrophysical information
Bocheng Tao, Huailai Zhou, Wenyue Wu, et al.
Petroleum Science (2025)
Open Access
Uncertainty-Driven Modeling of Microporosity and Permeability in Clastic Reservoirs Using Random Forest
Muhammad Risha, Mohamed Elsaadany, Paul Liu
(2025)
Closed Access
A scoping review of numerical modelling studies of geothermal reservoirs: Trends and opportunities post-COP25
Ella María Llanos, Daniela Blessent
International Journal of Renewable Energy Development (2025) Vol. 14, Iss. 4, pp. 668-693
Open Access
Spatial bagging for predictive machine learning uncertainty quantification
Fehmi Özbayrak, John T. Foster, Michael J. Pyrcz
Computers & Geosciences (2025) Vol. 203, pp. 105947-105947
Closed Access
Machine learning-based estimation of blue water footprints for transboundary water management in the Rio Grande River watershed
Santosh S. Palmate
Elsevier eBooks (2025), pp. 19-31
Closed Access
Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes
Jianguo Song, Munezero Ntibahanana, Moïse Luemba, et al.
IEEE Access (2023) Vol. 11, pp. 8761-8772
Open Access | Times Cited: 8
Seismic predictions of fluids via supervised deep learning: Incorporating various class-rebalance strategies
Shunli Gao, Minghui Xu, Luanxiao Zhao, et al.
Geophysics (2023) Vol. 88, Iss. 4, pp. M185-M200
Closed Access | Times Cited: 8
Porosity prediction based on a structural modeling deep-learning method
Bocheng Tao, Xingye Liu, Huailai Zhou, et al.
Geophysics (2024) Vol. 89, Iss. 6, pp. M197-M210
Closed Access | Times Cited: 2
Modelling the effective thermal conductivity and porosity of an open-cell material using an image-based technique coupled with machine learning
Marco Caniato
International Communications in Heat and Mass Transfer (2024) Vol. 159, pp. 107975-107975
Open Access | Times Cited: 2
Estimating Petrophysical Properties using Geostatistical Inversion and Data-Driven Extreme Gradient Boosting: A Case Study of Late Eocene McKee Formation, Taranaki Basin, New Zealand
John Oluwadamilola Olutoki, Mohamed Elsaadany, Numair Ahmed Siddiqui, et al.
Results in Engineering (2024), pp. 103494-103494
Open Access | Times Cited: 2
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