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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:

70 years of machine learning in geoscience in review
Jesper Dramsch
Advances in geophysics (2020), pp. 1-55
Open Access | Times Cited: 222

Showing 1-25 of 222 citing articles:

Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
Sibo Cheng, César Quilodrán-Casas, Said Ouala, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 6, pp. 1361-1387
Open Access | Times Cited: 125
Editorial: Broadening the Use of Machine Learning in Hydrology
Chaopeng Shen, Xingyuan Chen, Eric Laloy
Frontiers in Water (2021) Vol. 3
Open Access | Times Cited: 103
Machine learning in microseismic monitoring
Denis Anikiev, Claire Birnie, Umair bin Waheed, et al.
Earth-Science Reviews (2023) Vol. 239, pp. 104371-104371
Open Access | Times Cited: 60
Artificial intelligence for geoscience: Progress, challenges and perspectives
Tianjie Zhao, Sheng Wang, Chaojun Ouyang, et al.
The Innovation (2024) Vol. 5, Iss. 5, pp. 100691-100691
Open Access | Times Cited: 51
Subsurface sedimentary structure identification using deep learning: A review
Chuanjun Zhan, Zhenxue Dai, Zhijie Yang, et al.
Earth-Science Reviews (2023) Vol. 239, pp. 104370-104370
Closed Access | Times Cited: 44
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, et al.
Earth s Future (2024) Vol. 12, Iss. 7
Open Access | Times Cited: 26
Twenty Years of Progress, Challenges, and Opportunities in Measuring and Understanding Soil Respiration
Ben Bond‐Lamberty, Ashley P. Ballantyne, E. Berryman, et al.
Journal of Geophysical Research Biogeosciences (2024) Vol. 129, Iss. 2
Open Access | Times Cited: 24
Trustworthy remote sensing interpretation: Concepts, technologies, and applications
Sheng Wang, Wei Han, Xiaohui Huang, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 209, pp. 150-172
Closed Access | Times Cited: 21
Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms
Mahsa Hajihosseinlou, Abbas Maghsoudi, Reza Ghezelbash
Earth Science Informatics (2024) Vol. 17, Iss. 5, pp. 4859-4873
Closed Access | Times Cited: 16
The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset
Georgy Ayzel, Maik Heistermann
Computers & Geosciences (2021) Vol. 149, pp. 104708-104708
Closed Access | Times Cited: 87
INSTANCE – the Italian seismic dataset for machine learning
Alberto Michelini, S. Cianetti, Sonja Gaviano, et al.
Earth system science data (2021) Vol. 13, Iss. 12, pp. 5509-5544
Open Access | Times Cited: 74
Lithology prediction from well log data using machine learning techniques: A case study from Talcher coalfield, Eastern India
Thinesh Kumar, Naresh Kumar Seelam, G. Srinivasa Rao
Journal of Applied Geophysics (2022) Vol. 199, pp. 104605-104605
Closed Access | Times Cited: 68
Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts
Haobo Li, Yan Jiao, Kenneth Davey, et al.
Angewandte Chemie International Edition (2022) Vol. 62, Iss. 9
Open Access | Times Cited: 66
3D geological structure inversion from Noddy-generated magnetic data using deep learning methods
Jiateng Guo, Yunqiang Li, Mark Jessell, et al.
Computers & Geosciences (2021) Vol. 149, pp. 104701-104701
Closed Access | Times Cited: 65
Fairness and accountability of AI in disaster risk management: Opportunities and challenges
Caroline Gevaert, Mary Carman, Benjamin Rosman, et al.
Patterns (2021) Vol. 2, Iss. 11, pp. 100363-100363
Open Access | Times Cited: 56
Big Data Seismology
Stephen Arrowsmith, Daniel T. Trugman, J. MacCarthy, et al.
Reviews of Geophysics (2022) Vol. 60, Iss. 2
Open Access | Times Cited: 47
Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan
Nafees Ali, Jian Chen, Xiaodong Fu, et al.
Geosystems and Geoenvironment (2022) Vol. 2, Iss. 1, pp. 100123-100123
Open Access | Times Cited: 43
Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California
Vibhor Agarwal, Orhan Akyılmaz, C. K. Shum, et al.
The Science of The Total Environment (2022) Vol. 865, pp. 161138-161138
Open Access | Times Cited: 43
Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods
F. Sabzehee, Alireza Amiri-Simkooei, Siavash Iran Pour, et al.
Journal of Environmental Management (2023) Vol. 330, pp. 117180-117180
Open Access | Times Cited: 24
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: 24
Toward the reliable prediction of reservoir landslide displacement using earthworm optimization algorithm-optimized support vector regression (EOA-SVR)
Zhiyang Liu, Junwei Ma, Ding Xia, et al.
Natural Hazards (2023) Vol. 120, Iss. 4, pp. 3165-3188
Closed Access | Times Cited: 24
Machine Learning in Petrology: State-of-the-Art and Future Perspectives
Maurizio Petrelli
Journal of Petrology (2024) Vol. 65, Iss. 5
Open Access | Times Cited: 11
Explainability can foster trust in artificial intelligence in geoscience
Jesper Dramsch, Monique M. Kuglitsch, Miguel‐Ángel Fernández‐Torres, et al.
Nature Geoscience (2025)
Closed Access | Times Cited: 1
Recovering 3D Basement Relief Using Gravity Data Through Convolutional Neural Networks
Siyuan He, Hongzhu Cai, Shuang Liu, et al.
Journal of Geophysical Research Solid Earth (2021) Vol. 126, Iss. 10
Closed Access | Times Cited: 43
Automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial networks
Zixiao Yang, Qiyu Chen, Zhesi Cui, et al.
Computational Geosciences (2022) Vol. 26, Iss. 5, pp. 1135-1150
Closed Access | Times Cited: 36
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