| A theory-guided deep learning formulation and optimization of seismic waveform inversion J Sun, Z Niu, KA Innanen, J Li, DO Trad Geophysics 85 (2), R87-R99, 2020 | 165 | 2020 |
| Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis J Sun, KA Innanen, C Huang Geophysics 86 (3), R303-R317, 2021 | 92 | 2021 |
| Aenet: Automatic picking of p-wave first arrivals using deep learning C Guo, T Zhu, Y Gao, S Wu, J Sun IEEE Transactions on Geoscience and Remote Sensing 59 (6), 5293-5303, 2020 | 59 | 2020 |
| Automatic detection of volcanic surface deformation using deep learning J Sun, C Wauthier, K Stephens, M Gerva, G Cervone, PL Femina, ... Journal of Geophysical Research: Solid Earth, e2020JB019840, 2020 | 40 | 2020 |
| Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for localized surface temperature forecasting in an urban environment M Yu, F Xu, W Hu, J Sun, G Cervone IEEE Access 9, 137406-137418, 2021 | 37 | 2021 |
| Seismic attributes of the Permian and Triassic in Qingdao depression,South Yellow Sea Basin and their geological significance N Lin, D Gao, J Sun, L Wei, J Peng ACTA PETROLEI SINICA, 987-995, 2012 | 22* | 2012 |
| Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network J Sun, F Xu, G Cervone, M Gervais, C Wauthier, M Salvador ISPRS Journal of Photogrammetry and Remote Sensing 174, 117-131, 2021 | 18 | 2021 |
| 1.5D Internal multiple prediction in the plane wave domain J Sun, KA Innanenn GeoConvention 2015: New Horizons, 1-4, 2015 | 17 | 2015 |
| A theory-guided deep learning formulation of seismic waveform inversion J Sun, Z Niu, K Innanen, J Li, D Trad SEG Technical Program Expanded Abstracts 2019, 2019 | 15 | 2019 |
| Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion T Zhang, KA Innanen, J Sun, DO Trad SEG Technical Program Expanded Abstracts 2020, 1531-1535, 2020 | 14 | 2020 |
| Implicit seismic full waveform inversion with deep neural representation J Sun, K Innanen, T Zhang, D Trad Journal of Geophysical Research: Solid Earth, e2022JB025964, 2023 | 12 | 2023 |
| Multidimensional inverse scattering series internal multiple prediction in the coupled plane-wave domain J Sun, KA Innanen Geophysics 83 (2), v73-v82, 2018 | 9 | 2018 |
| Porosity prediction using cokriging with multiple secondary datasets H Xu, J Sun, B Russell, KA Innanen GeoConvention 2016: Optimizing Resources, 1-5, 2016 | 8 | 2016 |
| Interbed multiple prediction on land: which technology, and which domain? J Sun, KA Innanenn RECORDER 41 (8), 24-29, 2016 | 8 | 2016 |
| A review of converted wave AVO analysis J Sun, K Innanen CREWES Res. Rep 26, 1-13, 2014 | 7 | 2014 |
| A plane-wave formulation and numerical analysis of elastic multicomponent inverse scattering series internal multiple prediction J Sun, KA Innanen Geophysics 84 (5), V255-V269, 2019 | 6 | 2019 |
| Nonstationary L1 adaptive subtrraction with application to internal multiple attenuation S Keating, J Sun, P Pan, K Innanen GeoConvention 2016: Optimizing Resources, 1-4, 2016 | 6 | 2016 |
| Inverse-scattering series internal-multiple prediction in the double plane-wave domain J Sun, KA Innanen SEG Annual Meeting 2016, 4555-4560, 2016 | 6 | 2016 |
| Multicomponent inverse scattering series internal multiple prediction in the τ-p domain J Sun, KA Innanen SEG Technical Program Expanded Abstracts 2017, 2491-2496, 2017 | 5 | 2017 |
| 1D and 1.5D internal multiple prediction in MatLab M Eaid, J Sun, S Keating, KA Innanen CREWES Annual report 28, 2016 | 5 | 2016 |
Kris InnanenProfessor of Geophysics, University of CalgaryVerified email at ucalgary.ca
