References
- Araya-Polo, M., J. Jennings, A. Adler, and T. Dahlke. 2018. Deep-learning tomography. The Leading Edge 37: 58–66. doi: 10.1190/tle37010058.1
- Constable, S., R. Parker, and C. Constable. 1987. Occam’s inversion: a practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics 52: 289–300. doi: 10.1190/1.1442303
- Deszcz-Pan, M., D.V. Fitterman, and V.F. Labson. 1998. Reduction of inversion errors in helicopter Em data using auxiliary information. Exploration Geophysics 29: 142–6. doi: 10.1071/EG998142
- Di, H., Z. Wang, and G. AlRegib. 2018. Real-time seismic-image interpretation via deconvolutional neural network. SEG Technical Program Expanded Abstracts 2018, 2051–5.
- Dramsch, J., and M. Lüthje. 2018. Deep-learning seismic facies on state-of-the-art Cnn architectures. SEG Technical Program Expanded Abstracts 2018, 2036–40.
- Farquharson, C.G., D.W. Oldenburg, and P.S. Routh. 2003. Simultaneous 1d inversion of loop–loop electromagnetic data for magnetic susceptibility and electrical conductivity. Geophysics 68: 1857–69. doi: 10.1190/1.1635038
- Fournier, D., S. Kang, M.S. McMillan, and D.W. Oldenburg. 2017. Inversion of airborne geophysics over the DO-27/DO-18 kimberlites — part 2: electromagnetics. Interpretation 5: T313–25. doi: 10.1190/INT-2016-0140.1
- Fraser, D.C. 1978. Resistivity mapping with an airborne multicoil electromagnetic system. Geophysics 43: 144–72. doi: 10.1190/1.1440817
- Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press.
- Gramstad, O., and M. Nickel. 2018. Automated interpretation of top and base salt using deep-convolutional networks. SEG Technical Program Expanded Abstracts 2018, 1956–60.
- Guo, R., M. Li, F. Yang, S. Xu, G. Fang, and A. Abubakar. 2018. Application of supervised descent method for transient Em data inversion. SEG Technical Program Expanded Abstracts 2018, 2126–30.
- Hansen, P.C. 2000. The L-curve and its use in the numerical treatment of inverse problems. In Computational inverse problems in electrocardiology, ed. P. Johnston, Advances in Computational Bioengineering, vol. 4, 119–142. WIT Press.
- Kingma, D.P., and J. Ba. 2014. Adam: a method for stochastic optimization. [Online]. Available: https://arxiv.org/abs/1412.6980.
- LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521: 436–44. doi: 10.1038/nature14539
- Liu, D., W. Wang, W. Chen, X. Wang, Y. Zhou, and Z. Shi. 2018. Random-noise suppression in seismic data: what can deep learning do? SEG Technical Program Expanded Abstracts 2018, 2016–20.
- Noh, K., S. Oh, S.J. Seol, and J. Byun. 2018. 3D sequential inversion of frequency-domain airborne electromagnetic data to determine conductive and magnetic heterogeneities. Geophysics 83: E357–69. doi: 10.1190/geo2017-0668.1
- Oh, S., K. Noh, D. Yoon, S.J. Seol, and J. Byun. 2019. Salt delineation from electromagnetic data using convolutional neural networks. IEEE Geoscience and Remote Sensing Letters 16: 519–523. doi: 10.1109/LGRS.2018.2877155
- Pham, N., S. Fomel, and D. Dunlap. 2018. Automatic channel detection using deep learning. SEG Technical Program Expanded Abstracts 2018, 2026–30.
- Sun, H., and L. Demanet. 2018. Low-frequency extrapolation with deep learning. SEG Technical Program Expanded Abstracts 2018, 2011–15.
- Wu, X., Y. Shi, S. Fomel, and L. Liang. 2018. Convolutional neural networks for fault interpretation in seismic images. SEG Technical Program Expanded Abstracts 2018, 1946–50.
- Yang, D., and D.W. Oldenburg. 2012. Three-dimensional inversion of airborne time-domain electromagnetic data with applications to a porphyry deposit. Geophysics 77: B23–34. doi: 10.1190/geo2011-0194.1
- Zhdanov, M.S., and E. Tartaras. 2002. Three-dimensional inversion of multitransmitter electromagnetic data based on the localized quasi-linear approximation. Geophysical Journal International 148: 506–19. doi: 10.1046/j.1365-246x.2002.01591.x