Mineral prospectivity mapping using a VNet convolutional neural network M McMillan, E Haber, B Peters, J Fohring The Leading Edge 40 (2), 99-105, 2021 | 19 | 2021 |
Adaptive A-optimal experimental design for linear dynamical systems J Fohring, E Haber SIAM/ASA Journal on Uncertainty Quantification 4 (1), 1138-1159, 2016 | 13 | 2016 |
Geophysical imaging of fluid flow in porous media J Fohring, E Haber, L Ruthotto SIAM Journal on Scientific Computing 36 (5), S218-S236, 2014 | 9 | 2014 |
Using machine learning to interpret 3D airborne electromagnetic inversions E Haber, J Fohring, M McMillan, J Granek ASEG Extended Abstracts 2019 (1), 1-4, 2019 | 8 | 2019 |
Orogenic gold prospectivity mapping using machine learning M McMillan, J Fohring, E Haber, J Granek ASEG Extended Abstracts 2019 (1), 1-4, 2019 | 5 | 2019 |
Adaptive optimal experimental design and inversion of a coupled fluid flow and geophysical imaging model for reservoir monitoring J Fohring University of British Columbia, 2016 | 2 | 2016 |
Geophysical Imaging, Reservoir History Matching and Forecasting EH J Fohring, L Ruthotto 2013 SEG Annual Meeting, 2013 | 2* | 2013 |
Machine Learning Applications and Examples for Natural Resources N Phillips, M McMillan, J Fohring, B Peters, E Haber NSG2020 3rd Conference on Geophysics for Mineral Exploration and Mining 2020 …, 2020 | | 2020 |
Multi-resolution neural networks for subsurface exploration with sparse labels. J Fohring, E Haber AGU Fall Meeting Abstracts 2019, H34B-05, 2019 | | 2019 |
Estimation of porous fluid properties through inversion of geophysical imaging data for reservoir characterization and forecasting J Fohring, E Haber, L Ruthotto EGU General Assembly Conference Abstracts, 3143, 2014 | | 2014 |
Estimation of the initial tracer distribution and fluid velocity field from the inversion of geophysical imaging data J Fohring, L Ruthotto, E Haber AGU Fall Meeting Abstracts 2013, NS33A-1688, 2013 | | 2013 |