68 citations as recorded by crossref.

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3. RockNet: Rockfall and Earthquake Detection and Association via Multitask Learning and Transfer Learning W. Liao et al. 10.1109/TGRS.2023.3284008
4. Machine Learning Applications in Seismology K. Jia & S. Zhou 10.3390/app14177857
5. CREDIT-X1local: A reference dataset for machine learning seismology from ChinArray in Southwest China L. Li et al. 10.1016/j.eqs.2024.01.018
6. A Theory-Guided Encoder-Decoder model for short- and Long-Horizon seismic response prediction of nonlinear Single-Degree-of-Freedom systems Z. Pan et al. 10.1016/j.compstruc.2025.107719
7. A Mitigation Strategy for the Prediction Inconsistency of Neural Phase Pickers Y. Park et al. 10.1785/0220230003
8. Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms A. Lomax et al. 10.26443/seismica.v3i1.1164
9. Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review A. Navarro-Rodríguez et al. 10.3390/app15073492
10. En echelon faults reactivated by wastewater disposal near Musreau Lake, Alberta R. Schultz et al. 10.1093/gji/ggad226
11. Relocation of the 2018–2022 seismic sequences at the Central Gulf of Corinth: New evidence for north-dipping, low angle faulting V. Kapetanidis et al. 10.1016/j.tecto.2024.230433
12. TXED: The Texas Earthquake Dataset for AI Y. Chen et al. 10.1785/0220230327
13. A rapid and automatic procedure for seismic analysis based on deep learning and template matching: a case study on the M 4.1 Goesan earthquake on October 29, 2022 D. Sheen et al. 10.14770/jgsk.2023.010
14. Universal neural networks for real-time earthquake early warning trained with generalized earthquakes X. Zhang & M. Zhang 10.1038/s43247-024-01718-8
15. MLAAPDE: A Machine Learning Dataset for Determining Global Earthquake Source Parameters H. Cole et al. 10.1785/0220230021
16. Fine Seismogenic Fault Structures and Complex Rupture Characteristics of the 2022 M6.8 Luding, Sichuan Earthquake Sequence Revealed by Deep Learning and Waveform Modeling X. Zhao et al. 10.1029/2023GL102976
17. Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning H. Zhang et al. 10.1093/gji/ggac325
18. EQConvMixer: A Deep Learning Approach for Earthquake Location From Single-Station Waveforms H. Elsayed et al. 10.1109/LGRS.2023.3312324
19. PickerXL, A Large Deep Learning Model to Measure Arrival Times from Noisy Seismic Signals C. Chai et al. 10.1785/0220240353
20. Better Together: Ensemble Learning for Earthquake Detection and Phase Picking C. Yuan et al. 10.1109/TGRS.2023.3320148
21. IsoMapGen: Framework for early prediction of peak ground acceleration using tripartite feature extraction and gated attention model A. Joshi et al. 10.1016/j.cageo.2024.105849
22. The CWA Benchmark: A Seismic Dataset from Taiwan for Seismic Research K. Tang et al. 10.1785/0220230393
23. Deep learning based earthquake and vehicle detection algorithm D. Ertuncay et al. 10.1007/s10950-024-10267-8
24. Application of a Deep Learning Phase Picker to Improve the Performance of Deep Borehole Seismic Data Analysis Y. Hong & D. Sheen 10.9719/EEG.2024.57.6.701
25. CubeNet: Array-Based Seismic Phase Picking with Deep Learning G. Chen & J. Li 10.1785/0220220147
26. SAIPy: A Python package for single-station earthquake monitoring using deep learning W. Li et al. 10.1016/j.cageo.2024.105686
27. (Re)Discovering the Seismicity of Antarctica: A New Seismic Catalog for the Southernmost Continent A. Peña Castro et al. 10.1785/0220240076
28. Application of artificial intelligence technology in the study of anthropogenic earthquakes: a review J. Li et al. 10.1007/s10462-025-11157-2
29. Intelligent solutions for earthquake data analysis and prediction for future smart cities B. Dey et al. 10.1016/j.cie.2022.108368
30. Machine Learning in Earthquake Seismology S. Mousavi & G. Beroza 10.1146/annurev-earth-071822-100323
31. A multitask encoder–decoder to separate earthquake and ambient noise signal in seismograms J. Yin et al. 10.1093/gji/ggac290
32. GTUNE: An Assembled Global Seismic Dataset of Underground Nuclear Test Blasts L. Barama et al. 10.1785/0220220036
33. S-ProvFlow. Storing and Exploring Lineage Data as a Service A. Spinuso et al. 10.1162/dint_a_00128
34. EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking W. Li et al. 10.3389/feart.2022.953007
35. Customization of a deep neural network using local data for seismic phase picking Y. Hong et al. 10.3389/feart.2023.1306488
36. Comparing and integrating artificial intelligence and similarity search detection techniques: application to seismic sequences in Southern Italy F. Scotto di Uccio et al. 10.1093/gji/ggac487
37. SeisBench—A Toolbox for Machine Learning in Seismology J. Woollam et al. 10.1785/0220210324
38. A machine learning modelling for the seismicity in the region of Greece from 2000 and thereafter A. Savvides et al. 10.1007/s42452-025-07012-2
39. Deep-learning seismology S. Mousavi & G. Beroza 10.1126/science.abm4470
40. Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic E. Myklebust & A. Köhler 10.1093/gji/ggae298
41. Local Inhomogeneous Weighted Summary Statistics for Marked Point Processes N. D’Angelo et al. 10.1080/10618600.2023.2206441
42. Predicting peak ground acceleration using the ConvMixer network M. Mohammed et al. 10.1016/j.eqs.2024.11.005
43. DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology M. Zhao et al. 10.1016/j.eqs.2022.01.022
44. AI based 1-D P- and S-wave velocity models for the greater alpine region from local earthquake data B. Braszus et al. 10.1093/gji/ggae077
45. Ground-Shaking Intensity Prediction for Onsite Earthquake Early Warning Using Deep Learning M. Jiang et al. 10.1785/0220230263
46. A Robust and Rapid Grid-Based Machine Learning Approach for Inside and Off-Network Earthquakes Classification in Dynamically Changing Seismic Networks D. Annunziata et al. 10.1785/0220240173
47. Cross-Regional Seismic Event Discrimination via Convolutional Neural Networks: Exploring Fine-Tuning and Ensemble Averaging V. Kasburg et al. 10.1785/0120230198
48. Recent advances in earthquake seismology using machine learning H. Kubo et al. 10.1186/s40623-024-01982-0
49. The magmatic web beneath Hawai‘i J. Wilding et al. 10.1126/science.ade5755
50. Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence W. Li et al. 10.5194/se-15-197-2024
51. Comparison of Deep Learning Techniques for the Investigation of a Seismic Sequence: An Application to the 2019, Mw 4.5 Mugello (Italy) Earthquake S. Cianetti et al. 10.1029/2021JB023405
52. CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas G. Messuti et al. 10.3389/feart.2023.1223686
53. Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning W. Zhu et al. 10.1038/s41467-023-43355-3
54. Seismic Intensity Estimation for Earthquake Early Warning Using Optimized Machine Learning Model M. Abdalzaher et al. 10.1109/TGRS.2023.3296520
55. RPNet: Robust P-Wave First-Motion Polarity Determination Using Deep Learning J. Han et al. 10.1785/0220240384
56. EQCCT: A Production-Ready Earthquake Detection and Phase-Picking Method Using the Compact Convolutional Transformer O. Saad et al. 10.1109/TGRS.2023.3319440
57. A Novel Generative Adversarial Network for the Removal of Noise and Baseline Drift in Seismic Signals Y. Chen et al. 10.1109/TGRS.2024.3358901
58. Seis-PnSn: A Global Million-Scale Benchmark Data Set of Pn and Sn Seismic Phases for Deep Learning H. Kong et al. 10.1785/0220230379
59. Reexamination confirming additional seismic evidence for the 12 May 2010 low-yield nuclear test M. Zhang & L. Wen 10.1016/j.eqrea.2024.100350
60. SeisAug: A data augmentation python toolkit D. Pragnath et al. 10.1016/j.acags.2025.100232
61. Seismology in the cloud: guidance for the individual researcher Z. Krauss et al. 10.26443/seismica.v2i2.979
62. Deep Learning Peak Ground Acceleration Prediction Using Single-Station Waveforms O. Saad et al. 10.1109/TGRS.2024.3367725
63. Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning M. Abdalzaher et al. 10.3390/rs16122159
64. PyOcto: A high-throughput seismic phase associator J. Münchmeyer 10.26443/seismica.v3i1.1130
65. TranSeis: A high precision multitask seismic waveform detector Y. Zhou et al. 10.1016/j.cageo.2025.105867
66. Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy M. Esposito et al. 10.1016/j.dib.2024.110174
67. CREIME—A Convolutional Recurrent Model for Earthquake Identification and Magnitude Estimation M. Chakraborty et al. 10.1029/2022JB024595
68. INSTANCE – the Italian seismic dataset for machine learning A. Michelini et al. 10.5194/essd-13-5509-2021