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[4] Baron L, Holliger K. Constraints on the permeability structure of alluvial aquifers from the
poro-elastic inversion of multifrequency P-wave sonic velocity logs. IEEE Trans Geosci
Remote Sens 2011;49(6):1937–48.
[5] Hong D, Yang S, Yang S. A separately determining anisotropic formation parameter method
for triaxial induction data. IEEE Geosci Remote Sens Lett 2014;11(5):1015–8.
[6] Wong PM, Gedeon TD, Taggart IJ. An improved technique in porosity prediction: a neural
network approach. IEEE Trans Geosci Remote Sens 1995;33(4):971–80.
[7] Chang H-C, Chen H-C, Fang J-H. Lithology determination from well logs with fuzzy
associative memory neural network. IEEE Trans Geosci Remote Sens 1997;35(3):773–80.
[8] Xu C, Misra S, Srinivasan P, Ma S, editors. When petrophysics meets big data: what
can machine do? SPE middle east oil and gas show and conference. Society of Petroleum
Engineers; 2019.
[9] He J, Misra S. Generation of synthetic dielectric dispersion logs in organic-rich shale
formations using neural-network models. Geophysics 2019;84(3):1–46.
[10] Li H, He J, Misra S, editors. Data-driven in-situ geomechanical characterization in
shale reservoirs. SPE annual technical conference and exhibition. Society of Petroleum
Engineers; 2018.
[11] Freedman R. Advances in NMR logging. J Petrol Technol 2006;58(1):60–6.
[12] Goodfellow I, Bengio Y, Courville A. Deep learning. MIT Press; 2016.
[13] Kingma DP, Welling M. Auto-encoding variational bayes [arXiv preprint arXiv:13126114];
2013.
[14] Doersch C. Tutorial on variational autoencoders [arXiv preprint arXiv:160605908]; 2016.
[15] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional
generative adversarial networks [arXiv preprint arXiv:151106434]; 2015.
[16] Reed S, Akata Z, Yan X, Logeswaran L, Schiele B, Lee H. Generative adversarial text to image
synthesis [arXiv preprint arXiv:160505396]; 2016.
[17] Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy PJ, editors. Training deep neural networks on
imbalanced data sets. 2016 international joint conference on neural networks (IJCNN). IEEE;
2016.