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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:

Instantaneous tracking of earthquake growth with elastogravity signals
Andrea Licciardi, Quentin Blétery, Bertrand Rouet‐Leduc, et al.
Nature (2022) Vol. 606, Iss. 7913, pp. 319-324
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Machine Learning in Earthquake Seismology
S. Mostafa Mousavi, Gregory C. Beroza
Annual Review of Earth and Planetary Sciences (2022) Vol. 51, Iss. 1, pp. 105-129
Open Access | Times Cited: 111
Magnitude estimation and ground motion prediction to harness fiber optic distributed acoustic sensing for earthquake early warning
Itzhak Lior, Diane Rivet, Jean‐Paul Ampuero, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 45
Recent advances in earthquake seismology using machine learning
Hisahiko Kubo, Makoto Naoi, Masayuki Kano
Earth Planets and Space (2024) Vol. 76, Iss. 1
Open Access | Times Cited: 21
Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer
Bertrand Rouet‐Leduc, Claudia Hulbert
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 14
A neural encoder for earthquake rate forecasting
Oleg Zlydenko, Gal Elidan, Avinatan Hassidim, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 19
Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning
Pablo Lara, Quentin Blétery, Jean‐Paul Ampuero, et al.
Journal of Geophysical Research Solid Earth (2023) Vol. 128, Iss. 11
Open Access | Times Cited: 16
Earthquake Forecasting Using Big Data and Artificial Intelligence: A 30-Week Real-Time Case Study in China
Omar M. Saad, Yunfeng Chen, Alexandros Savvaidis, et al.
Bulletin of the Seismological Society of America (2023) Vol. 113, Iss. 6, pp. 2461-2478
Closed Access | Times Cited: 13
Testing Driving Mechanisms of Megathrust Seismicity With Explainable Artificial Intelligence
Juan Carlos Graciosa, Fabio A. Capitanio, Adam Beall, et al.
Journal of Geophysical Research Solid Earth (2025) Vol. 130, Iss. 1
Open Access
Identifying ultramafic rocks using artificial neural network method based on aeromagnetic data
Fuxiang Liu, Shengqing Xiong, Hai Yang, et al.
Journal of Applied Geophysics (2025), pp. 105688-105688
Closed Access
PEGSGraph: A Graph Neural Network for Fast Earthquake Characterization Based on Prompt ElastoGravity Signals
Céline Hourcade, Kévin Juhel, Quentin Blétery
Journal of Geophysical Research Machine Learning and Computation (2025) Vol. 2, Iss. 1
Open Access
Rapid Estimation of Single-Station Earthquake Magnitudes with Machine Learning on a Global Scale
Sydney N. Dybing, William L. Yeck, Hank M. Cole, et al.
Bulletin of the Seismological Society of America (2024) Vol. 114, Iss. 3, pp. 1523-1538
Closed Access | Times Cited: 2
The Role of Machine Learning in Earthquake Seismology: A Review
Anup Chitkeshwar
Archives of Computational Methods in Engineering (2024)
Closed Access | Times Cited: 2
Multi-station deep learning on geodetic time series detects slow slip events in Cascadia
Giuseppe Costantino, Sophie Giffard‐Roisin, Mathilde Radiguet, et al.
Communications Earth & Environment (2023) Vol. 4, Iss. 1
Open Access | Times Cited: 6
Rapid Source Characterization of the Maule Earthquake Using Prompt Elasto‐Gravity Signals
Gabriela Arias, Quentin Blétery, Andrea Licciardi, et al.
Journal of Geophysical Research Solid Earth (2023) Vol. 128, Iss. 9
Open Access | Times Cited: 4
Early Source Characterization of Large Earthquakes Using W Phase and Prompt Elastogravity Signals
Kévin Juhel, Zacharie Duputel, Luis Rivera, et al.
Seismological Research Letters (2023) Vol. 95, Iss. 3, pp. 1558-1568
Open Access | Times Cited: 4
Early-Warning Inverse Source Problem for the Elasto-Gravitational Equations
Lorenzo Baldassari, Maarten V. de Hoop, Elisa Francini, et al.
SIAM Journal on Applied Mathematics (2024) Vol. 84, Iss. 3, pp. 831-855
Open Access | Times Cited: 1
Fast and full characterization of large earthquakes from prompt elastogravity signals
Kévin Juhel, Quentin Blétery, Andrea Licciardi, et al.
Communications Earth & Environment (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 1
Spectral-infinite-element simulations of seismic wave propagation in self-gravitating, rotating 3-D Earth models
Hom Nath Gharti, Will Eaton, Jeroen Tromp
Geophysical Journal International (2023) Vol. 235, Iss. 3, pp. 2671-2693
Open Access | Times Cited: 3
Theories and applications of earthquake-induced gravity variation: Advances and perspectives
He Tang, Wenke Sun
Earthquake Science (2023) Vol. 36, Iss. 5, pp. 376-415
Open Access | Times Cited: 3
Seismic prompt gravity strain signals in a layered spherical Earth
Shenjian Zhang, Rongjiang Wang, Xiaofei Chen
Earthquake Science (2023) Vol. 36, Iss. 5, pp. 341-355
Open Access | Times Cited: 3
Harnessing Distributed Acoustic Sensing for Earthquake Early Warning: Magnitude Estimation and Ground Motion Prediction
Itzhak Lior, Diane Rivet, Jean‐Paul Ampuero, et al.
EarthArXiv (California Digital Library) (2022)
Open Access | Times Cited: 3
GRAPES: Earthquake Early Warning by Passing Seismic Vectors Through the Grapevine
Timothy Clements, E. S. Cochran, A. Baltay, et al.
Geophysical Research Letters (2024) Vol. 51, Iss. 9
Open Access
Empowering Machine Learning Forecasting of Labquake Using Event‐Based Features and Clustering Characteristics
Sadegh Karimpouli, Grzegorz Kwiatek, Yehuda Ben‐Zion, et al.
Journal of Geophysical Research Machine Learning and Computation (2024) Vol. 1, Iss. 2
Open Access
Novel application of unsupervised machine learning for characterization of subsurface seismicity, tectonic dynamics and stress distribution
Mohammad Salam, M. Iqbal, Raihan Habib, et al.
Applied Computing and Geosciences (2024), pp. 100200-100200
Open Access
Classification of pre-seismic gravity perturbation and background noises based on scattering network
Jianing Gou, Zhenghua Liu, Ji Li, et al.
Research Square (Research Square) (2024)
Open Access
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