95 citations as recorded by crossref.

1. Synthetic ground motions in heterogeneous geologies from various sources: the HEMEWS-3D database F. Lehmann et al.
2. U-Trans: a foundation model for seismic waveform representation and enhanced downstream earthquake tasks O. Saad et al.
3. RockNet: Rockfall and Earthquake Detection and Association via Multitask Learning and Transfer Learning W. Liao et al.
4. A Mitigation Strategy for the Prediction Inconsistency of Neural Phase Pickers Y. Park et al.
5. Data-driven analysis of the tectonic history in Greece (1975–2025) and neural network-based modeling of seismic response spectra D. Kontoni et al.
6. Small earthquake location via machine learning with insufficient data J. Zhang et al.
7. A Lightweight Python Recovery Tool for Waveform Gap Recovery in Seismic–Volcanic Monitoring Networks S. Arrais et al.
8. Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms A. Lomax et al.
9. Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review A. Navarro-Rodríguez et al.
10. 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.
11. TXED: The Texas Earthquake Dataset for AI Y. Chen et al.
12. 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.
13. Universal neural networks for real-time earthquake early warning trained with generalized earthquakes X. Zhang & M. Zhang
14. MLAAPDE: A Machine Learning Dataset for Determining Global Earthquake Source Parameters H. Cole et al.
15. 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.
16. Seismic Event Classification With a Lightweight Fourier Neural Operator Model A. Abdullin et al.
17. PickerXL, A Large Deep Learning Model to Measure Arrival Times from Noisy Seismic Signals C. Chai et al.
18. IsoMapGen: Framework for early prediction of peak ground acceleration using tripartite feature extraction and gated attention model A. Joshi et al.
19. The CWA Benchmark: A Seismic Dataset from Taiwan for Seismic Research K. Tang et al.
20. SAIPy: A Python package for single-station earthquake monitoring using deep learning W. Li et al.
21. Application of artificial intelligence technology in the study of anthropogenic earthquakes: a review J. Li et al.
22. On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey S. Menon et al.
23. S-ProvFlow. Storing and Exploring Lineage Data as a Service A. Spinuso et al.
24. EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking W. Li et al.
25. Customization of a deep neural network using local data for seismic phase picking Y. Hong et al.
26. Evaluation of Seismic Artificial Intelligence with Uncertainty S. Myren et al.
27. Challenges and Opportunities of Machine Learning Earthquake Detection for Regional Monitoring S. Noel & M. West
28. SeisBench—A Toolbox for Machine Learning in Seismology J. Woollam et al.
29. A machine learning modelling for the seismicity in the region of Greece from 2000 and thereafter A. Savvides et al.
30. Deep-learning seismology S. Mousavi & G. Beroza
31. AI based 1-D P- and S-wave velocity models for the greater alpine region from local earthquake data B. Braszus et al.
32. Real-time seismic signal classification using deep vision architectures for earthquake early warning systems A. Navarro-Rodríguez et al.
33. Cross-Regional Seismic Event Discrimination via Convolutional Neural Networks: Exploring Fine-Tuning and Ensemble Averaging V. Kasburg et al.
34. Divide and conquer: separating the two probabilities in seismic phase picking Y. Park et al.
35. Insights into the 2021 Arkalochori (Crete Island, southern Greece) foreshock swarm through an enhanced deep-learning seismic catalog F. Vallianatos et al.
36. A near real-time framework for monitoring very-long-period signals at volcanoes S. Gammaldi et al.
37. MSEP-TFormer: a multitask source estimation parameter transformer network for earthquake monitoring X. Huang & Y. Zhang
38. Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning W. Zhu et al.
39. RPNet: Robust P-Wave First-Motion Polarity Determination Using Deep Learning J. Han et al.
40. EQCCT: A Production-Ready Earthquake Detection and Phase-Picking Method Using the Compact Convolutional Transformer O. Saad et al.
41. A Novel Generative Adversarial Network for the Removal of Noise and Baseline Drift in Seismic Signals Y. Chen et al.
42. Seis-PnSn: A Global Million-Scale Benchmark Data Set of Pn and Sn Seismic Phases for Deep Learning H. Kong et al.
43. SeisAug: A data augmentation python toolkit D. Pragnath et al.
44. SeismoDual: A dual-domain deep learning framework for robust seismic phase picking K. Tang & K. Chen
45. Seismology in the cloud: guidance for the individual researcher Z. Krauss et al.
46. Seismicity catalogue of the entire Chilean margin (18 to 56° S) from an automated approach M. Riedel-Hornig et al.
47. Ojos del Salado: how active is this sleeping giant? L. Murray-Bergquist et al.
48. Spatio-temporal deep networks with feature disentangling for advancing earthquake monitoring F. Meng et al.
49. Machine Learning Applications in Seismology K. Jia & S. Zhou
50. CREDIT-X1local: A reference dataset for machine learning seismology from ChinArray in Southwest China L. Li et al.
51. 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.
52. En echelon faults reactivated by wastewater disposal near Musreau Lake, Alberta R. Schultz et al.
53. Learning source, path and site effects: CNN-based on-site intensity prediction for earthquake early warning H. Zhang et al.
54. EQConvMixer: A Deep Learning Approach for Earthquake Location From Single-Station Waveforms H. Elsayed et al.
55. OpenSWI: a massive-scale benchmark dataset for surface wave dispersion curve inversion F. Liu et al.
56. Deep Learning and Machine Learning Applied to the Detection and Classification of Volcano-Seismic Events at Piton de la Fournaise Volcano R. Machacca et al.
57. Better Together: Ensemble Learning for Earthquake Detection and Phase Picking C. Yuan et al.
58. Deep learning based earthquake and vehicle detection algorithm D. Ertuncay et al.
59. Application of a Deep Learning Phase Picker to Improve the Performance of Deep Borehole Seismic Data Analysis Y. Hong & D. Sheen
60. Detection of mining-induced microseismicity through a deep convolutional neural network S. Vafaei Shoushtari et al.
61. CubeNet: Array-Based Seismic Phase Picking with Deep Learning G. Chen & J. Li
62. (Re)Discovering the Seismicity of Antarctica: A New Seismic Catalog for the Southernmost Continent A. Peña Castro et al.
63. Intelligent solutions for earthquake data analysis and prediction for future smart cities B. Dey et al.
64. Machine Learning in Earthquake Seismology S. Mousavi & G. Beroza
65. A multitask encoder–decoder to separate earthquake and ambient noise signal in seismograms J. Yin et al.
66. GTUNE: An Assembled Global Seismic Dataset of Underground Nuclear Test Blasts L. Barama et al.
67. Impact of machine-learning phase picking on seismic tomography at Popocatépetl Volcano, Mexico K. Bernal-Manzanilla & M. Calò
68. Picking Induced Seismicity with Deep Learning (piSDL) J. Heuel et al.
69. From flat to steep subduction: the South Peru margin imaged by a new high-density seismic catalogue C. Chalumeau et al.
70. Comparing and integrating artificial intelligence and similarity search detection techniques: application to seismic sequences in Southern Italy F. Scotto di Uccio et al.
71. Test-Time Augmentations and Quality Controls for Improving Regional Seismic Phase Picking B. Han et al.
72. Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic E. Myklebust & A. Köhler
73. Local Inhomogeneous Weighted Summary Statistics for Marked Point Processes N. D’Angelo et al.
74. Predicting peak ground acceleration using the ConvMixer network M. Mohammed et al.
75. DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology M. Zhao et al.
76. Ground-Shaking Intensity Prediction for Onsite Earthquake Early Warning Using Deep Learning M. Jiang et al.
77. 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.
78. Denoising Seismograms in the Time Domain Using a Deep Learning Model C. Chai et al.
79. Recent advances in earthquake seismology using machine learning H. Kubo et al.
80. The magmatic web beneath Hawai‘i J. Wilding et al.
81. Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence W. Li et al.
82. 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.
83. Enhancing the resolution of microseismicity through dense array monitoring in complex extensional settings F. Scotto di Uccio et al.
84. A New High-Resolution Seismic Catalog for Southwestern Australia (2020–2025) and Analysis of Long-Term Clustering Behavior R. Pickle et al.
85. Which is better: deep-learning or manual seismic arrival-time picking? S. Cianetti et al.
86. Advances in geophysical forensic event monitoring M. Pasyanos et al.
87. CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas G. Messuti et al.
88. Seismic Intensity Estimation for Earthquake Early Warning Using Optimized Machine Learning Model M. Abdalzaher et al.
89. Reexamination confirming additional seismic evidence for the 12 May 2010 low-yield nuclear test M. Zhang & L. Wen
90. Deep Learning Peak Ground Acceleration Prediction Using Single-Station Waveforms O. Saad et al.
91. Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning M. Abdalzaher et al.
92. PyOcto: A high-throughput seismic phase associator J. Münchmeyer
93. Fault Structure Characterization in the Gulf of Evia (Central Greece): Insights from an Enhanced, Relocated Seismic Catalog (2018–2023) A. Karakonstantis et al.
94. TranSeis: A high precision multitask seismic waveform detector Y. Zhou et al.
95. Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy M. Esposito et al.