Asif, M. R., Foged, N., Maurya, P. K., Grombacher, D. J., Christiansen, A.
V., Auken, E., and Larsen, J. J.: Integrating neural networks in
least-squares inversion of airborne time-domain electromagnetic data,
Geophysics, 87, E177–E187, https://doi.org/10.1190/geo2021-0335.1, 2022b.
Auken, E., Christiansen, A. V., Kirkegaard, C., Fiandaca, G., Schamper, C.,
Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., and
Christensen, N. B.: An overview of a highly versatile forward and stable
inverse algorithm for airborne, ground-based and borehole electromagnetic
and electric data, Explor. Geophys., 46, 223–235, https://doi.org/10.1071/EG13097, 201
5 (data available at:
https://hgg.au.dk/software/aarhusinv, last access: 16 March 2023).
Auken, E., Foged, N., Larsen, J. J., Lassen, K. V. T., Maurya, P. K., Dath,
S. M., and Eiskjær, T. T.: tTEM – A towed transient electromagnetic
system for detailed 3D imaging of the top 70 m of the subsurface,
Geophysics, 84, E13–E22, 2018.
Bai, P., Vignoli, G., Viezzoli, A., Nevalainen, J., and Vacca, G.: (Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via
Artificial Neural Network, Remote Sensing, 12, 3440, https://doi.org/10.3390/rs12203440, 2020.
Bang, M., Oh, S., Noh, K., Seol, S. J., and Byun, J.: Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural network, Geophysics, 86, E407–E419, https://doi.org/10.1190/geo2020-0871.1, 2021.
Bergen, K. J., Johnson, P. A., Maarten, V., and Beroza, G. C.: Machine
learning for data-driven discovery in solid Earth geoscience, Science, 363,
eaau0323, https://doi.org/10.1126/science.aau0323, 2019.
Birken, R. A. and Poulton, M. M.: Neural Network Interpretation of High
Frequency Electromagnetic Ellipticity Data Part II: Analyzing 3D Responses,
Journal of Environmental Engineering Geophysics, 4, 149–165, 1999.
Birken, R. A., Poulton, M. M., and Lee, K. H.: Neural Network Interpretation
of High Frequency Electromagnetic Ellipticity Data Part I: Understanding the
Half-Space and Layered Earth Response, Journal of Environmental Engineering
Geophysics, 4, 93–103, 1999.
Bording, T. S., Asif, M. R., Barfod, A. S., Larsen, J. J., Zhang, B.,
Grombacher, D. J., Christiansen, A. V., Engebretsen, K. W., Pedersen, J. B.,
Maurya, P. K., and Auken, E.: Machine learning based fast forward modelling
of ground-based time-domain electromagnetic data, J. Appl.
Geophys., 187, 104290, https://doi.org/10.1016/j.jappgeo.2021.104290, 2021.
Christiansen, A. V. and Auken, E.: Layered 2-D inversion of profile data,
evaluated using stochastic models, ASEG Extended Abstracts, 1–8, https://doi.org/10.1071/ASEG2003_3DEMab005, 2003.
Christiansen, A. V., Auken, E., and Sørensen, K.: The transient
electromagnetic method, in: Groundwater geophysics, edited by: Kirsch, R., Springer, ISBN 978-3-540-29383-5, https://doi.org/10.1007/3-540-29387-6_6, 2006.
Colombo, D., Li, W., Rovetta, D., Sandoval-Curiel, E., and Turkoglu, E.:
Physics-driven deep learning joint inversion, in: SEG Technical Program
Expanded Abstracts 2020, Society of Exploration Geophysicists, 1775–1779, https://doi.org/10.1190/segam2020-3424997.1, 2020a.
Colombo, D., Li, W., Sandoval-Curiel, E., and McNeice, G. W.:
Electromagnetic reservoir monitoring with machine-learning inversion and
fluid flow simulators, in: Fifth International Conference on Engineering
Geophysics (ICEG), Al Ain, UAE, 21–24 October 2019, 167–170, https://doi.org/10.1190/iceg2019-043.1, 2020b.
Colombo, D., Turkoglu, E., Li, W., and Rovetta, D.: Coupled physics-deep
learning inversion, Comput. Geosci., 157, 104917, https://doi.org/10.1016/j.cageo.2021.104917, 2021a.
Colombo, D., Turkoglu, E., Li, W., Sandoval-Curiel, E., and Rovetta, D.:
Physics-driven deep learning inversion with application to transient
electromagnetics, Geophysics, 86, E209–E224, https://doi.org/10.1190/geo2020-0760.1, 2021b.
Conway, D., Alexander, B., King, M., Heinson, G., and Kee, Y.: Inverting
magnetotelluric responses in a three-dimensional earth using fast forward
approximations based on artificial neural networks, Comput.
Geosci., 127, 44–52, 2019.
Dong, C., Loy, C. C., and Tang, X.: Accelerating the super-resolution
convolutional neural network, in: European conference on computer vision, Amsterdam, the Netherlands, 11–14 October 2016,
391–407, https://doi.org/10.1007/978-3-319-46475-6_25, 2016.
Dramsch, J. S.: 70 years of machine learning in geoscience in review,
Adv. Geophys., 61, 1–55, https://doi.org/10.1016/bs.agph.2020.08.002, 2020.
Feng, X.-T. and Seto, M.: Neural network dynamic modelling of rock
microfracturing sequences under triaxial compressive stress conditions,
Tectonophysics, 292, 293–309, 1998.
Guo, R., Li, M., Fang, G., Yang, F., Xu, S., and Abubakar, A.: Application
of supervised descent method to transient electromagnetic data inversion,
Geophysics, 84, E225–E237, 2019.
Hopfield, J. J.: Neural networks and physical systems with emergent
collective computational abilities, P. Natl. Acad.
Sci. USA, 79, 2554–2558, 1982.
Jørgensen, F., Sandersen, P. B., Auken, E., Lykke-Andersen, H., and
Sørensen, K.: Contributions to the geological mapping of Mors, Denmark – a
study based on a large-scale TEM survey, B. Geol. Soc.
Denmark, 52, 53–75, 2005.
Karshakov, E. V., Podmogov, Y. G., Kertsman, V. M., and Moilanen, J.:
Combined Frequency Domain and Time Domain Airborne Data for Environmental
and Engineering Challenges, J. Environ. Eng. Geoph.,
22, 1–11, https://doi.org/10.2113/JEEG22.1.1, 2017.
Khatibi, S. and Aghajanpour, A.: Machine Learning: A Useful Tool in
Geomechanical Studies, a Case Study from an Offshore Gas Field, Energies,
13, 3528, https://doi.org/10.3390/en13143528, 2020.
Kirsch, R.: Groundwater geophysics: a tool for hydrogeology, Springer, ISBN 978-3-540-29383-5, https://doi.org/10.1007/3-540-29387-6, 2006.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet classification with deep convolutional neural networks, Adv. Neur. Inf. Proc. Sys., 25, 1097–1105,
https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf (last access: 16 March 2023), 2012.
Kwan, K., Reford, S., Abdoul-Wahab, D. M., Pitcher, D. H., Bournas, N.,
Prikhodko, A., Plastow, G., and Legault, J. M.: Supervised neural network
targeting and classification analysis of airborne EM, magnetic and gamma-ray
spectrometry data for mineral exploration, ASEG Extended Abstracts, 2015,
1–5, https://doi.org/10.1071/ASEG2015ab306, 2015.
Li, J., Liu, Y., Yin, C., Ren, X., and Su, Y.: Fast imaging of time-domain
airborne EM data using deep learning technology, Geophysics, 85, E163–E170, https://doi.org/10.1190/geo2019-0015.1, 2020.
Lin, F., Chen, K., Wang, X., Cao, H., Chen, D., and Chen, F.: Denoising stacked autoencoders for transient electromagnetic signal denoising, Nonlin. Processes Geophys., 26, 13–23, https://doi.org/10.5194/npg-26-13-2019, 2019.
Liu, W., Lü, Q., Yang, L., Lin, P., and Wang, Z.: Application of
Sample-Compressed Neural Network and Adaptive-Clustering Algorithm for
Magnetotelluric Inverse Modeling, IEEE Geoscience Remote Sensing Letters,
18, 1540–1544, https://doi.org/10.1109/LGRS.2020.3005796, 2020.
Moghadas, D.: One-dimensional deep learning inversion of electromagnetic
induction data using convolutional neural network, Geophys. J.
Int., 222, 247–259, 2020.
Moghadas, D., Behroozmand, A. A., and Christiansen, A. V.: Soil electrical
conductivity imaging using a neural network-based forward solver: Applied to
large-scale Bayesian electromagnetic inversion, J. Appl.
Geophys., 176, 104012, https://doi.org/10.1016/j.jappgeo.2020.104012, 2020.
Noh, K., Yoon, D., and Byun, J.: Imaging subsurface resistivity structure
from airborne electromagnetic induction data using deep neural network,
Explor. Geophys., 51, 214–220, 2020.
Pang, X., Zhou, Y., Wang, P., Lin, W., and Chang, V.: An innovative neural
network approach for stock market prediction, J. Supercomput.,
76, 2098–2118, 2020.
Puzyrev, V.: Deep learning electromagnetic inversion with convolutional
neural networks, Geophys. J. Int., 218, 817–832, 2019.
Puzyrev, V. and Swidinsky, A.: Inversion of 1D frequency-and time-domain
electromagnetic data with convolutional neural networks, Comput.
Geosci., 149, 104681, https://doi.org/10.1016/j.cageo.2020.104681, 2021.
Qin, S., Wang, Y., Tai, H.-M., Wang, H., Liao, X., and Fu, Z.: TEM apparent
resistivity imaging for grounding grid detection using artificial neural
network, IET Generation, Transmission, Distribution, 13, 3932–3940, https://doi.org/10.1049/iet-gtd.2018.6450, 2019.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.: You only look once:
Unified, real-time object detection, in: Proceedings of the IEEE conference on
computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June 2016, 779–788, https://doi.org/10.1109/CVPR.2016.91, 2016.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and
Carvalhais, N.: Deep learning and process understanding for data-driven
Earth system science, Nature, 566, 195–204, 2019.
Ren, S., He, K., Girshick, R., and Sun, J.: Faster r-cnn: Towards real-time
object detection with region proposal networks, Adv. Neur.
In., 1, 91–99, 2015.
Smith, R., Fountain, D., and Allard, M.: The MEGATEM fixed-wing transient EM
system applied to mineral exploration: a discovery case history, First
Break, 21, https://doi.org/10.3997/1365-2397.21.7.25570, 2003.
Smith, R. S.: Airborne electromagnetic methods: Applications to minerals,
water and hydrocarbon exploration, Canadian Society of Explortation
Geophysics, 35, 7–10, https://csegrecorder.com/articles/view/airborne-electromagnetic-methods-app-to-minerals-water-hydrocarbon-expl (last access: 16 March 2023), 2010.
Viezzoli, A., Christiansen, A. V., Auken, E., and Sørensen, K.: Quasi-3D
modeling of airborne TEM data by spatially constrained inversion,
Geophysics, 73, F105–F113, 2008.
Vignoli, G., Fiandaca, G., Christiansen, A. V., Kirkegaard, C., and Auken,
E.: Sharp spatially constrained inversion with applications to transient
electromagnetic data, Geophys. Prospect., 63, 243–255, 2015.
Wang, W., Zhang, M., Chen, G., Jagadish, H., Ooi, B. C., and Tan, K.-L.:
Database meets deep learning: Challenges and opportunities, ACM SIGMOD
Record, 45, 17–22, 2016.
Wu, S., Huang, Q., and Zhao, L.: Convolutional neural network inversion of
airborne transient electromagnetic data, Geophys. Prospect., 69,
1761–1772, 2021a.
Wu, S., Huang, Q., and Zhao, L.: De-noising of transient electromagnetic
data based on the long short-term memory-autoencoder, Geophys. J.
Int., 224, 669–681, 2021b.
Xue, G., Li, H., He, Y., Xue, J., and Wu, X.: Development of the Inversion
Method for Transient Electromagnetic Data, IEEE Access, 8, 146172–146181, https://doi.org/10.1109/ACCESS.2020.3013626,
2020.
Zhang, R., Liu, Y., and Sun, H.: Physics-guided convolutional neural network
(PhyCNN) for data-driven seismic response modeling, Eng. Struct.,
215, 110704, https://doi.org/10.1016/j.engstruct.2020.110704, 2020.
Zhang, Y. and Paulson, K.: Regularized hopfield neural networks and its
application to one-dimensional inverse problem of magnetotelluric
observations, Inverse Probl. Eng., 5, 33–53, 1997.
Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Bengio, C. L. Y., and
Courville, A.: Towards end-to-end speech recognition with deep convolutional
neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1701.02720, 10 January 2017.
Zhang, Z., Qi, C., and Asif, M. R.: Investigation on Projection Space Pairs
in Neighbor Embedding Algorithms, 2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China, 12–16 August 2018, 125–128, https://doi.org/10.1109/ICSP.2018.8652441, 2018.
Zhdanov, M. S.: Inverse theory and applications in geophysics, 2nd edn., Elsevier, ISBN 978-0-444-62674-5,
https://doi.org/10.1016/C2012-0-03334-0,
2015.
Zhu, K.-G., Ma, M.-Y., Che, H.-W., Yang, E.-W., Ji, Y.-J., Yu, S.-B., and
Lin, J.: PC-based artificial neural network inversion for airborne
time-domain electromagnetic data, J. Appl. Geophys., 9, 1–8, https://doi.org/10.1007/s11770-012-0307-7,
2012.
