| Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking SM Mousavi, WL Ellsworth, W Zhu, LY Chuang, GC Beroza Nature communications 11 (1), 3952, 2020 | 598 | 2020 |
| Seismic signal denoising and decomposition using deep neural networks W Zhu, SM Mousavi, GC Beroza IEEE Transactions on Geoscience and Remote Sensing 57 (11), 9476-9488, 2019 | 358 | 2019 |
| CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection SM Mousavi, W Zhu, Y Sheng, GC Beroza Scientific reports 9 (1), 1-14, 2018 | 312 | 2018 |
| Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform SM Mousavi, CA Langston, SP Horton Geophysics 81 (4), V341-V355, 2016 | 282 | 2016 |
| STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI SM Mousavi, Y Sheng, W Zhu, GC Beroza IEEE Access 7, 179464-179476, 2019 | 267 | 2019 |
| A machine‐learning approach for earthquake magnitude estimation SM Mousavi, GC Beroza Geophysical Research Letters 47 (1), e2019GL085976, 2019 | 224 | 2019 |
| Hybrid seismic denoising using higher‐order statistics and improved wavelet block thresholding SM Mousavi, CA Langston Bulletin of the Seismological Society of America 106 (4), 1380-1393, 2016 | 182 | 2016 |
| Deep-learning seismology SM Mousavi, GC Beroza Science 377 (6607), eabm4470, 2022 | 170 | 2022 |
| Unsupervised clustering of seismic signals using deep convolutional autoencoders SM Mousavi, W Zhu, W Ellsworth, G Beroza IEEE Geoscience and Remote Sensing Letters 16 (11), 1693-1697, 2019 | 148 | 2019 |
| Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data SM Mousavi, CA Langston Geophysics 82 (4), V211-V227, 2017 | 136 | 2017 |
| Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression SM Mousavi, SP Horton, CA Langston, B Samei Geophysical Journal International 207 (1), 29-46, 2016 | 136 | 2016 |
| A review of earth artificial intelligence Z Sun, L Sandoval, R Crystal-Ornelas, SM Mousavi, J Wang, C Lin, ... Computers & Geosciences 159, 105034, 2022 | 109 | 2022 |
| Bayesian-deep-learning estimation of earthquake location from single-station observations SM Mousavi, GC Beroza IEEE Transactions on Geoscience and Remote Sensing, 1 - 14, 2019 | 109 | 2019 |
| Adaptive noise estimation and suppression for improving microseismic event detection SM Mousavi, CA Langston Journal of Applied Geophysics 132, 116-124, 2016 | 94 | 2016 |
| Machine learning and earthquake forecasting—next steps GC Beroza, M Segou, S Mostafa Mousavi Nature communications 12 (1), 4761, 2021 | 93 | 2021 |
| Earthquake phase association using a Bayesian Gaussian mixture model W Zhu, IW McBrearty, SM Mousavi, WL Ellsworth, GC Beroza Journal of Geophysical Research: Solid Earth 127 (5), e2021JB023249, 2022 | 62 | 2022 |
| Seismic signal augmentation to improve generalization of deep neural networks W Zhu, SM Mousavi, GC Beroza Advances in geophysics 61, 151-177, 2020 | 57 | 2020 |
| Machine‐learning‐based analysis of the Guy‐Greenbrier, Arkansas earthquakes: A tale of two sequences Y Park, SM Mousavi, W Zhu, WL Ellsworth, GC Beroza Geophysical Research Letters 47 (6), e2020GL087032, 2020 | 56 | 2020 |
| Spatio-temporal evolution of frequency-magnitude distribution and seismogenic index during initiation of induced seismicity at Guy-Greenbrier, Arkansas SM Mousavi, PO Ogwari, SP Horton, CA Langston Physics of the Earth and Planetary Interiors 267, 53-66, 2017 | 53 | 2017 |
| Machine learning in earthquake seismology SM Mousavi, GC Beroza Annual Review of Earth and Planetary Sciences 51, 105-129, 2023 | 51 | 2023 |
Greg BerozaDepartment of Geophysics, Stanford UniversityBestätigte E-Mail-Adresse bei stanford.edu
