| Characteristics and Spatial Variability of Wind Noise on Near‐Surface Broadband Seismometers SN Dybing, AT Ringler, DC Wilson, RE Anthony Bulletin of the Seismological Society of America 109 (3), 1082-1098, 2019 | 49 | 2019 |
| Worth a closer look: Raman spectra of lead-pipe scale JD Pasteris, Y Bae, DE Giammar, SN Dybing, CH Yoder, J Zhao, Y Hu Minerals 11 (10), 1047, 2021 | 4 | 2021 |
| ROSES: Remote online sessions for emerging seismologists FKD Dugick, S van der Lee, GA Prieto, SN Dybing, L Toney, HM Cole Seismological Research Letters 92 (4), 2657-2667, 2021 | 3 | 2021 |
| Detecting earthquakes in noisy real-time gnss data with machine learning S Dybing, D Melgar, A Thomas, K Hodgkinson, D Mencin AGU Fall Meeting Abstracts 2021, S14A-04, 2021 | 2 | 2021 |
| Deep learning for denoising high-rate global navigation satellite system data A Thomas, D Melgar, SN Dybing, JR Searcy Seismica 2 (1), 2023 | 1 | 2023 |
| Rapid Estimation of Single‐Station Earthquake Magnitudes with Machine Learning on a Global Scale SN Dybing, WL Yeck, HM Cole, D Melgar Bulletin of the Seismological Society of America, 2024 | | 2024 |
| From Strong Motion to InSAR: Efficient Generation of Large Magnitude Synthetic Data for Machine Learning Applications D Melgar, I Rodero, M Adair, JT Lin, S Dybing, D Small, T Nye, ... Fall Meeting 2022, 2022 | | 2022 |
| Towards Inclusive Teaching Through Online Learning: An Example from ROSES. FK Dannemann Dugick, S van der Lee, S Dybing, L Toney, H Cole Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| Investigating early earthquake rupture characteristics with borehole strainmeters S Dybing, D Melgar, AJ Barbour, A Canitano, DE Goldberg AGU Fall Meeting Abstracts 2020, S026-06, 2020 | | 2020 |
| Characterizing Wind Noise and Spatial Variability on Near-Surface Broadband Seismometers S Dybing, AT Ringler, DC Wilson, RE Anthony AGU Fall Meeting Abstracts 2018, S51D-0355, 2018 | | 2018 |
