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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:

Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network
Dario Jozinović, Anthony Lomax, Ivan Štajduhar, et al.
Geophysical Journal International (2020) Vol. 222, Iss. 2, pp. 1379-1389
Open Access | Times Cited: 97

Showing 26-50 of 97 citing articles:

Onsite intensity prediction for earthquake early warning with multimodal deep learning
Jingbao Zhu, Shanyou Li, Qiang Ma, et al.
Soil Dynamics and Earthquake Engineering (2025) Vol. 195, pp. 109430-109430
Closed Access
Onsite Early Prediction of PGA Using CNN With Multi-Scale and Multi-Domain P-Waves as Input
Ting‐Yu Hsu, Chao‐Wen Huang
Frontiers in Earth Science (2021) Vol. 9
Open Access | Times Cited: 30
Graph Convolution Networks for Seismic Events Classification Using Raw Waveform Data From Multiple Stations
Gwantae Kim, Bonhwa Ku, Jae-Kwang Ahn, et al.
IEEE Geoscience and Remote Sensing Letters (2021) Vol. 19, pp. 1-5
Closed Access | Times Cited: 29
A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction
Zhenyu Bao, Jingyu Zhao, Pu Huang, et al.
Sensors (2021) Vol. 21, Iss. 13, pp. 4434-4434
Open Access | Times Cited: 27
CREIME—A C onvolutional R ecurrent Model for E arthquake I dentification and M agnitude E stimation
Megha Chakraborty, Darius Fenner, Wei Li, et al.
Journal of Geophysical Research Solid Earth (2022) Vol. 127, Iss. 7
Open Access | Times Cited: 21
Early Peak Ground Acceleration Prediction for On-Site Earthquake Early Warning Using LSTM Neural Network
Ting‐Yu Hsu, Afaf Nadiyah Rifa Pratomo
Frontiers in Earth Science (2022) Vol. 10
Open Access | Times Cited: 20
Neural Network-Based Strong Motion Prediction for On-Site Earthquake Early Warning
You-Jing Chiang, Tai-Lin Chin, Da-Yi Chen
Sensors (2022) Vol. 22, Iss. 3, pp. 704-704
Open Access | Times Cited: 19
A deep learning approach for the development of an Early Earthquake Warning system
Marco Carratù, Vincenzo Gallo, Vincenzo Paciello, et al.
2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (2022), pp. 1-6
Closed Access | Times Cited: 19
Real-Time Seismic Intensity Measurements Prediction for Earthquake Early Warning: A Systematic Literature Review
Zhenpeng Cheng, Chaoyong Peng, Meirong Chen
Sensors (2023) Vol. 23, Iss. 11, pp. 5052-5052
Open Access | Times Cited: 12
Cumulative absolute velocity prediction for earthquake early warning with deep learning
Yanwei Wang, Qingxu Zhao, Kai Qian, et al.
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 11, pp. 1724-1740
Closed Access | Times Cited: 11
Ground motion prediction maps using seismic-microzonation data and machine learning
Federico Mori, Amerigo Mendicelli, Gaetano Falcone, et al.
Natural hazards and earth system sciences (2022) Vol. 22, Iss. 3, pp. 947-966
Open Access | Times Cited: 18
An Approach for Clustering of Seismic Events using Unsupervised Machine Learning
Мархаба Карменова, Aizhan Tlebaldinova, Юрий Крак, et al.
Acta Polytechnica Hungarica (2022) Vol. 19, Iss. 5, pp. 7-22
Open Access | Times Cited: 18
Machine intelligence in dynamical systems: \A state‐of‐art review
Arup Kumar Sahoo, Snehashish Chakraverty
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2022) Vol. 12, Iss. 4
Closed Access | Times Cited: 18
Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning
Hongcai Zhang, Diego Melgar, Valerie J. Sahakian, et al.
Geophysical Journal International (2022) Vol. 231, Iss. 3, pp. 2186-2204
Closed Access | Times Cited: 16
Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests
Jawad Fayaz, Carmine Galasso
Geoscience Frontiers (2024) Vol. 15, Iss. 5, pp. 101839-101839
Open Access | Times Cited: 3
Data‐Knowledge Driven Hybrid Deep Learning for Earthquake Early Warning
Jingwei Zhu, Shanyou Li, Jindong Song
Earth and Space Science (2024) Vol. 11, Iss. 9
Open Access | Times Cited: 3
Earthquake Early Warning System Using Ncheck and Hard-Shared Orthogonal Multitarget Regression on Deep Learning
Adi Wibowo, Cecep Pratama, David P. Sahara, et al.
IEEE Geoscience and Remote Sensing Letters (2021) Vol. 19, pp. 1-5
Closed Access | Times Cited: 20
Localizing microseismic events on field data using a U-Net-based convolutional neural network trained on synthetic data
Nicolas Vinard, Guy Drijkoningen, D. J. Verschuur
Geophysics (2021) Vol. 87, Iss. 2, pp. KS33-KS43
Open Access | Times Cited: 18
Efficient regional seismic risk assessment via deep generative learning of surrogate models
Shanwu Li, Charles R. Farrar, Yongchao Yang
Earthquake Engineering & Structural Dynamics (2023) Vol. 52, Iss. 11, pp. 3435-3454
Open Access | Times Cited: 7
Seismic severity estimation using convolutional neural network for earthquake early warning
Tao Ren, Xinliang Liu, Hongfeng Chen, et al.
Geophysical Journal International (2023) Vol. 234, Iss. 2, pp. 1355-1362
Closed Access | Times Cited: 7
PCA-LSTM: An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning
Yizhao Wang
Computer Modeling in Engineering & Sciences (2024) Vol. 139, Iss. 3, pp. 3029-3045
Open Access | Times Cited: 2
On-site instrumental seismic intensity prediction for China via recurrent neural network and transfer learning
Jingbao Zhu, Shanyou Li, Yongxiang Wei, et al.
Journal of Asian Earth Sciences (2023) Vol. 248, pp. 105610-105610
Closed Access | Times Cited: 6
Identification of Low‐Frequency Earthquakes on the San Andreas Fault With Deep Learning
Amanda M. Thomas, Asaf Inbal, J. Searcy, et al.
Geophysical Research Letters (2021) Vol. 48, Iss. 13
Open Access | Times Cited: 15
A Novel Approach for Earthquake Prediction Using Random Forest and Neural Networks
Nidhi Agarwal, Ishika Arora, Harsh Saini, et al.
EAI Endorsed Transactions on Energy Web (2023) Vol. 10
Open Access | Times Cited: 5
A multibranch, multitarget neural network for rapid point-source inversion in a microseismic environment: examples from the Hengill Geothermal Field, Iceland
Nima Nooshiri, Christopher J. Bean, Torsten Dahm, et al.
Geophysical Journal International (2021) Vol. 229, Iss. 2, pp. 999-1016
Open Access | Times Cited: 12

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