||Advanced Geosciences, Inc. (2009) Instruction Manual for EarthImager 2D, Version 2.4.0, Resistivity and IP Inversion Software.|
Araya-Polo, M., Jennings, J., Adler, A. & Dahlke, T. (2018) Deep-learning tomography. The Leading Edge, 37, 58–66.
Baydin, A.G., Pearlmutter, B.A., Radul, A.A. & Siskind, J.M. (2018) Automatic differentiation in machine learning: a survey. Journal of Marchine Learning Research, 18, 1–43.
Bengio, Y. & LeCun, Y. (2007) Scaling learning algorithms towards AI. Large-scale kernel machines, 34, 1–41.
Calderón‐Macías, C., Sen, M.K. & Stoffa, P.L. (2000) Artificial neural networks for parameter estimation in geophysics. Geophysical Prospecting, 48, 21–47. doi:10.1046/j.1365-2478.2000.00171.x
Conway, D., Alexander, B., King, M., Heinson, G. & Kee, Y. (2019) Inverting magnetotelluric responses in a three-dimensional earth using fast forward approximations based on artificial neural networks. Computers & Geosciences, 127, 44–52. doi:10.1016/j.cageo.2019.03.002
Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2, 303–314.
Dey, A. & Morrison, H.F. (1979) Resistivity modelling for arbitrarily shaped two‐dimensional structures. Geophysical Prospecting, 27, 106–136.
El-Qady, G. & Ushijima, K. (2001) Inversion of DC resistivity data using neural networks. Geophysical Prospecting, 49, 417–430.
Gatys, L.A., Ecker, A.S. & Bethge, M. (2016) Image style transfer using convolutional neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2414–2423.
Goodfellow, I., Bengio, Y. & Courville, A. (2016) Deep learning, MIT press.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., et al. (2014) Generative adversarial nets. Advances in neural information processing systems, pp. 2672–2680.
Graves, A., Mohamed, A. & Hinton, G. (2013) Speech recognition with deep recurrent neural networks. 2013 IEEE international conference on acoustics, speech and signal processing, pp. 6645–6649, IEEE.
He, K., Zhang, X., Ren, S. & Sun, J. (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision, pp. 1026–1034.
He, K., Zhang, X., Ren, S. & Sun, J. (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., et al. (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Signal processing magazine, 29.
Ho, T.L. (2009) 3-D inversion of borehole-to-surface electrical data using a back-propagation neural network. Journal of Applied Geophysics, 68, 489–499.
Hornik, K., Stinchcombe, M. & White, H. (1989) Multilayer feedforward networks are universal approximators. Neural networks, 2, 359–366.
Karpathy, A. (2018) Stanford University CS231n: convolutional neural networks for visual recognition. URL: http://cs231n. stanford. edu/syllabus. html.
Krizhevsky, A., Sutskever, I. & Hinton, G.E. (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, pp. 1097–1105.
LeCun, Y., Bengio, Y. & Hinton, G. (2015) Deep learning. nature, 521, 436.
Levenberg, K. (1944) A method for the solution of certain non-linear problems in least squares. Quarterly of applied mathematics, 2, 164–168.
Long, J., Shelhamer, E. & Darrell, T. (2015) Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440.
Maas, A.L., Hannun, A.Y. & Ng, A.Y. (2013) Rectifier nonlinearities improve neural network acoustic models. Proc. icml, Vol. 30, p. 3.
Maiti, S., Erram, V.C., Gupta, G. & Tiwari, R.K. (2012) ANN based inversion of DC resistivity data for groundwater exploration in hard rock terrain of western Maharashtra (India). Journal of Hydrology, 464, 294–308.
Marquardt, D.W. (1963) An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11, 431–441.
McCulloch, W.S. & Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115–133.
Mhaskar, H., Liao, Q. & Poggio, T. (2017) When and Why Are Deep Networks Better Than Shallow Ones? Thirty-First AAAI Conference on Artificial Intelligence, Presented at the Thirty-First AAAI Conference on Artificial Intelligence. Retrieved from https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14849
Moradi, S., Trad, D. & Innanen, K.A. (2018) Quantum computing in geophysics: Algorithms, computational costs, and future applications. in SEG Technical Program Expanded Abstracts 2018, pp. 4649–4653, Society of Exploration Geophysicists.
Neyamadpour, A., Taib, S. & Abdullah, W.W. (2009) Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB application. Computers & Geosciences, 35, 2268–2274.
Perol, T., Gharbi, M. & Denolle, M. (2018) Convolutional neural network for earthquake detection and location. Science Advances, 4, e1700578.
Pidlisecky, A. & Knight, R. (2008) FW2_5D: A MATLAB 2.5-D electrical resistivity modeling code. Computers & Geosciences, 34, 1645–1654.
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. (2017) Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review. International Journal of Automation and Computing, 14, 503–519.
Puzyrev, V. (2018) Deep learning electromagnetic inversion with convolutional neural networks. arXiv preprint arXiv:1812.10247.
Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. (2016) You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788.
Ren, S., He, K., Girshick, R. & Sun, J. (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, pp. 91–99.
Rosenblatt, F. (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65, 386.
Rosenblatt, F. (1961) Principles of neurodynamics. perceptrons and the theory of brain mechanisms, Cornell Aeronautical Lab Inc Buffalo NY.
Ruder, S. (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1988) Learning representations by back-propagating errors. Cognitive modeling, 5, 1.
Simonyan, K. & Zisserman, A. (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Sonoda, S. & Murata, N. (2017) Neural network with unbounded activation functions is universal approximator. Applied and Computational Harmonic Analysis, 43, 233–268.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15, 1929–1958.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., et al. (2015) Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.
Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., et al. (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Zhang, G., Wang, Z. & Chen, Y. (2018) Deep learning for seismic lithology prediction. Geophysical Journal International, 215, 1368–1387.
Zhu, W. & Beroza, G.C. (2018) PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216, 261–273.