58 citations as recorded by crossref.

1. Synthetic ground motions in heterogeneous geologies from various sources: the HEMEWS-3D database F. Lehmann et al. 10.5194/essd-16-3949-2024
2. RockNet: Rockfall and Earthquake Detection and Association via Multitask Learning and Transfer Learning W. Liao et al. 10.1109/TGRS.2023.3284008
3. Machine Learning Applications in Seismology K. Jia & S. Zhou 10.3390/app14177857
4. 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
5. A Mitigation Strategy for the Prediction Inconsistency of Neural Phase Pickers Y. Park et al. 10.1785/0220230003
6. 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
7. En echelon faults reactivated by wastewater disposal near Musreau Lake, Alberta R. Schultz et al. 10.1093/gji/ggad226
8. 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
9. TXED: The Texas Earthquake Dataset for AI Y. Chen et al. 10.1785/0220230327
10. 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
11. Universal neural networks for real-time earthquake early warning trained with generalized earthquakes X. Zhang & M. Zhang 10.1038/s43247-024-01718-8
12. MLAAPDE: A Machine Learning Dataset for Determining Global Earthquake Source Parameters H. Cole et al. 10.1785/0220230021
13. 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
14. Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning H. Zhang et al. 10.1093/gji/ggac325
15. EQConvMixer: A Deep Learning Approach for Earthquake Location From Single-Station Waveforms H. Elsayed et al. 10.1109/LGRS.2023.3312324
16. Better Together: Ensemble Learning for Earthquake Detection and Phase Picking C. Yuan et al. 10.1109/TGRS.2023.3320148
17. 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
18. The CWA Benchmark: A Seismic Dataset from Taiwan for Seismic Research K. Tang et al. 10.1785/0220230393
19. Deep learning based earthquake and vehicle detection algorithm D. Ertuncay et al. 10.1007/s10950-024-10267-8
20. 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
21. CubeNet: Array-Based Seismic Phase Picking with Deep Learning G. Chen & J. Li 10.1785/0220220147
22. SAIPy: A Python package for single-station earthquake monitoring using deep learning W. Li et al. 10.1016/j.cageo.2024.105686
23. (Re)Discovering the Seismicity of Antarctica: A New Seismic Catalog for the Southernmost Continent A. Peña Castro et al. 10.1785/0220240076
24. Intelligent solutions for earthquake data analysis and prediction for future smart cities B. Dey et al. 10.1016/j.cie.2022.108368
25. Machine Learning in Earthquake Seismology S. Mousavi & G. Beroza 10.1146/annurev-earth-071822-100323
26. A multitask encoder–decoder to separate earthquake and ambient noise signal in seismograms J. Yin et al. 10.1093/gji/ggac290
27. GTUNE: An Assembled Global Seismic Dataset of Underground Nuclear Test Blasts L. Barama et al. 10.1785/0220220036
28. S-ProvFlow. Storing and Exploring Lineage Data as a Service A. Spinuso et al. 10.1162/dint_a_00128
29. EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking W. Li et al. 10.3389/feart.2022.953007
30. Customization of a deep neural network using local data for seismic phase picking Y. Hong et al. 10.3389/feart.2023.1306488
31. 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
32. SeisBench—A Toolbox for Machine Learning in Seismology J. Woollam et al. 10.1785/0220210324
33. Deep-learning seismology S. Mousavi & G. Beroza 10.1126/science.abm4470
34. 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
35. Local Inhomogeneous Weighted Summary Statistics for Marked Point Processes N. D’Angelo et al. 10.1080/10618600.2023.2206441
36. DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology M. Zhao et al. 10.1016/j.eqs.2022.01.022
37. 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
38. Ground-Shaking Intensity Prediction for Onsite Earthquake Early Warning Using Deep Learning M. Jiang et al. 10.1785/0220230263
39. 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
40. Cross-Regional Seismic Event Discrimination via Convolutional Neural Networks: Exploring Fine-Tuning and Ensemble Averaging V. Kasburg et al. 10.1785/0120230198
41. Recent advances in earthquake seismology using machine learning H. Kubo et al. 10.1186/s40623-024-01982-0
42. The magmatic web beneath Hawai‘i J. Wilding et al. 10.1126/science.ade5755
43. 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
44. 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
45. 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
46. Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning W. Zhu et al. 10.1038/s41467-023-43355-3
47. Seismic Intensity Estimation for Earthquake Early Warning Using Optimized Machine Learning Model M. Abdalzaher et al. 10.1109/TGRS.2023.3296520
48. EQCCT: A Production-Ready Earthquake Detection and Phase-Picking Method Using the Compact Convolutional Transformer O. Saad et al. 10.1109/TGRS.2023.3319440
49. 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
50. 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
51. Reexamination confirming additional seismic evidence for the 12 May 2010 low-yield nuclear test M. Zhang & L. Wen 10.1016/j.eqrea.2024.100350
52. Seismology in the cloud: guidance for the individual researcher Z. Krauss et al. 10.26443/seismica.v2i2.979
53. Deep Learning Peak Ground Acceleration Prediction Using Single-Station Waveforms O. Saad et al. 10.1109/TGRS.2024.3367725
54. Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning M. Abdalzaher et al. 10.3390/rs16122159
55. PyOcto: A high-throughput seismic phase associator J. Münchmeyer 10.26443/seismica.v3i1.1130
56. 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
57. CREIME—A Convolutional Recurrent Model for Earthquake Identification and Magnitude Estimation M. Chakraborty et al. 10.1029/2022JB024595
58. INSTANCE – the Italian seismic dataset for machine learning A. Michelini et al. 10.5194/essd-13-5509-2021