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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:

Automatic microseismic event picking via unsupervised machine learning
Yangkang Chen
Geophysical Journal International (2017) Vol. 212, Iss. 1, pp. 88-102
Closed Access | Times Cited: 114

Showing 1-25 of 114 citing articles:

Machine Learning in Seismology: Turning Data into Insights
Qingkai Kong, Daniel T. Trugman, Zachary E. Ross, et al.
Seismological Research Letters (2018) Vol. 90, Iss. 1, pp. 3-14
Open Access | Times Cited: 413
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI
S. Mostafa Mousavi, Yixiao Sheng, Weiqiang Zhu, et al.
IEEE Access (2019) Vol. 7, pp. 179464-179476
Open Access | Times Cited: 307
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
S. Mostafa Mousavi, Weiqiang Zhu, Yixiao Sheng, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 301
Deep learning electromagnetic inversion with convolutional neural networks
Vladimir Puzyrev
Geophysical Journal International (2019) Vol. 218, Iss. 2, pp. 817-832
Open Access | Times Cited: 217
A review of the current status of induced seismicity monitoring for hydraulic fracturing in unconventional tight oil and gas reservoirs
Lei Li, Jingqiang Tan, David A. Wood, et al.
Fuel (2019) Vol. 242, pp. 195-210
Closed Access | Times Cited: 205
A theory-guided deep-learning formulation and optimization of seismic waveform inversion
Jian Sun, Zhan Niu, K. A. Innanen, et al.
Geophysics (2019) Vol. 85, Iss. 2, pp. R87-R99
Closed Access | Times Cited: 178
Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders
S. Mostafa Mousavi, Weiqiang Zhu, William L. Ellsworth, et al.
IEEE Geoscience and Remote Sensing Letters (2019) Vol. 16, Iss. 11, pp. 1693-1697
Closed Access | Times Cited: 160
P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning
Zachary E. Ross, Men‐Andrin Meier, Egill Hauksson
Journal of Geophysical Research Solid Earth (2018) Vol. 123, Iss. 6, pp. 5120-5129
Open Access | Times Cited: 156
Reliable Real‐Time Seismic Signal/Noise Discrimination With Machine Learning
Men‐Andrin Meier, Zachary E. Ross, Anshul Ramachandran, et al.
Journal of Geophysical Research Solid Earth (2018) Vol. 124, Iss. 1, pp. 788-800
Open Access | Times Cited: 130
Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking
Chengping Chai, Mónica Maceira, Hector Santos-Villalobos, et al.
Geophysical Research Letters (2020) Vol. 47, Iss. 16
Open Access | Times Cited: 113
Deep learning reservoir porosity prediction based on multilayer long short-term memory network
Wei Chen, Liuqing Yang, Bei Zha, et al.
Geophysics (2020) Vol. 85, Iss. 4, pp. WA213-WA225
Closed Access | Times Cited: 107
Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network
Yangkang Chen, Guoyin Zhang, Min Bai, et al.
Earth and Space Science (2019) Vol. 6, Iss. 7, pp. 1244-1261
Open Access | Times Cited: 102
ADDCNN: An Attention-Based Deep Dilated Convolutional Neural Network for Seismic Facies Analysis With Interpretable Spatial–Spectral Maps
Fangyu Li, Huailai Zhou, Zengyan Wang, et al.
IEEE Transactions on Geoscience and Remote Sensing (2020) Vol. 59, Iss. 2, pp. 1733-1744
Closed Access | Times Cited: 95
A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification
Kaibo Zhou, Jianyu Zhang, Yusong Ren, et al.
Geophysics (2020) Vol. 85, Iss. 4, pp. WA147-WA158
Closed Access | Times Cited: 91
Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning
Yangkang Chen, Mi Zhang, Min Bai, et al.
Seismological Research Letters (2019)
Closed Access | Times Cited: 90
Facies Identification Based on Multikernel Relevance Vector Machine
Xingye Liu, Xiaohong Chen, Jingye Li, et al.
IEEE Transactions on Geoscience and Remote Sensing (2020) Vol. 58, Iss. 10, pp. 7269-7282
Closed Access | Times Cited: 76
Semiautomatic first-arrival picking of microseismic events by using the pixel-wise convolutional image segmentation method
Hao Wu, Bo Zhang, Fangyu Li, et al.
Geophysics (2019) Vol. 84, Iss. 3, pp. V143-V155
Closed Access | Times Cited: 75
Fast waveform detection for microseismic imaging using unsupervised machine learning
Yangkang Chen
Geophysical Journal International (2018) Vol. 215, Iss. 2, pp. 1185-1199
Closed Access | Times Cited: 69
Identifying microseismic events in a mining scenario using a convolutional neural network
Andrew Wilkins, Andrew D. Strange, Yi Duan, et al.
Computers & Geosciences (2020) Vol. 137, pp. 104418-104418
Closed Access | Times Cited: 66
Random Noise Attenuation Based on Residual Convolutional Neural Network in Seismic Datasets
Liuqing Yang, Wei Chen, Wei Liu, et al.
IEEE Access (2020) Vol. 8, pp. 30271-30286
Open Access | Times Cited: 62
Automatic noise attenuation based on clustering and empirical wavelet transform
Wei Chen, Hui Song
Journal of Applied Geophysics (2018) Vol. 159, pp. 649-665
Closed Access | Times Cited: 60
A Physics-Based Neural-Network Way to Perform Seismic Full Waveform Inversion
Yuxiao Ren, Xinji Xu, Senlin Yang, et al.
IEEE Access (2020) Vol. 8, pp. 112266-112277
Open Access | Times Cited: 60
Seismic Noise Attenuation Using Unsupervised Sparse Feature Learning
Mi Zhang, Yang Liu, Min Bai, et al.
IEEE Transactions on Geoscience and Remote Sensing (2019) Vol. 57, Iss. 12, pp. 9709-9723
Closed Access | Times Cited: 59
Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning
Christopher Johnson, Yehuda Ben‐Zion, Haoran Meng, et al.
Geophysical Research Letters (2020) Vol. 47, Iss. 15
Closed Access | Times Cited: 56
Lithofacies identification using support vector machine based on local deep multi-kernel learning
Xingye Liu, Lin Zhou, Xiaohong Chen, et al.
Petroleum Science (2020) Vol. 17, Iss. 4, pp. 954-966
Open Access | Times Cited: 53
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