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http://dx.doi.org/10.7582/GGE.2020.23.3.00117

Introduction to Geophysical Exploration Data Denoising using Deep Learning  

Caesary, Desy (Department of Energy & Mineral Resources Engineering, Sejong University)
Cho, AHyun (Department of Energy & Mineral Resources Engineering, Sejong University)
Yu, Huieun (Department of Energy & Mineral Resources Engineering, Sejong University)
Joung, Inseok (Department of Energy & Mineral Resources Engineering, Sejong University)
Song, Seo Young (Department of Energy & Mineral Resources Engineering, Sejong University)
Cho, Sung Oh (Department of Energy & Mineral Resources Engineering, Sejong University)
Kim, Bitnarae (Department of Energy & Mineral Resources Engineering, Sejong University)
Nam, Myung Jin (Department of Energy & Mineral Resources Engineering, Sejong University)
Publication Information
Geophysics and Geophysical Exploration / v.23, no.3, 2020 , pp. 117-130 More about this Journal
Abstract
Noises can distort acquired geophysical data, leading to their misinterpretation. Potential noises sources include anthropogenic activity, natural phenomena, and instrument noises. Conventional denoising methods such as wavelet transform and filtering techniques, are based on subjective human investigation, which is computationally inefficient and time-consuming. Recently, many researchers attempted to implement neural networks to efficiently remove noise from geophysical data. This study aims to review and analyze different types of neural networks, such as artificial neural networks, convolutional neural networks, autoencoders, residual networks, and wavelet neural networks, which are implemented to remove different types of noises including seismic, transient electromagnetic, ground-penetrating radar, and magnetotelluric surveys. The review analyzes and summarizes the key challenges in the removal of noise from geophysical data using neural network, while proposes and explains solutions to the challenges. The analysis support that the advancement in neural networks can be powerful denoising tools for geophysical data.
Keywords
deep learning; denoising; geophysical data;
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1 Mousavi, S.M., and Langston, C.A., 2016, Adaptive noise estimation and suppression for improving microseismic event detection. J. Appl. Geophy., 132, 116-124, doi: 10.1016/j.jappgeo.2016.06.008.   DOI
2 Naghizadeh, M., 2012, Seismic data interpolation and denoising in the frequency-wavenumber domain. Geophysics, 77(2), V71-V80, doi: 10.1190/geo2011-0172.1.   DOI
3 Neelamani, R., Baumstein, A.I., Gillard, D.G., Hadidi, M.T., and Soroka, W.L., 2008, Coherent and random noise attenuation using the curvelet transform. The Leading Edge, 27(2), 240-248, doi: 10.1190/1.2840373.   DOI
4 Nemirovski, A., Juditsky, A., Lan, G., and Shapiro, A., 2009, Robust stochastic approximation approach to stochastic programming. SIAM. J. Optim., 19(4), 1574-1609, doi: 10.1137/070704277.   DOI
5 Nemirovski, A., and Yudin, D.B., 1983, Problem complexity and method efficiency in optimization, SIAM Rev, 27(2), 264-265, doi: 10.1137/1027074.   DOI
6 Rajaraman, A., and Ullman, J.D., 2011, Mining of Massive Datasets, Cambridge University Press.
7 Richardson, A., and Feller, C., 2019, Seismic data denoising and deblending using deep learning, arXiv preprint arXiv:1907.01497.
8 Robbins, H., and Monro, S., 1951, A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400-407, doi: 10.1214/aoms/1177729586.   DOI
9 Ronneberger, O., Fischer, P., and Brox, T., 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, Med. Image Comput. Comput. Assist Interv., 234-241, doi:10.1007/978-3-319-24574-4_28.
10 Ruder, S., 2016, An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747.
11 Sheremet, O., Sheremet, K., Sadovoi, O., and Sokhina, Y., 2018, Convolutional Neural Networks for Image Denoising in Infocommunication Systems. In 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology, 429-432, doi: 10.1109/INFOCOMMST.2018.8632109.
12 Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., and Lin, C. W., 2020, Deep Learning on Image Denoising: An overview, Neural Networks, 131, 251-275, doi: 10.1016/j.neunet.2020.07.025.   DOI
13 Tian, C., Xu, Y., Fei, L., Wang, J., Wen, J., and Luo, N., 2019, Enhanced CNN for image denoising. CAAI Trans. Intell. Technol., 4(1), 17-23, doi: 10.1049/trit.2018.1054.   DOI
14 Qian, N., 1999, On the momentum t erm in gradient descent learning algorithms. Neural Networks, 12(1), 145-151, doi: 10.1016/S0893-6080(98)00116-6.   DOI
15 Tieleman, T., and Hinton, G., 2012, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4(2), 26-31.
16 Wu, X., Xue, G., Xiao, P., Li, J., Liu, L., and Fang, G., 2019, The Removal of the High-Frequency Motion-Induced Noise in Helicopter-Borne Transient Electromagnetic Data Based on Wavelet Neural Networks. Geophysics, 84(1), K1-K9, doi: 10.1190/geo2018-0120.1.   DOI
17 Yu, S., Ma, J., and Wang, W., 2018, Deep learning tutorial for denoising, arXiv preprint arXiv:1412.6980.
18 Zhang, Y., 2018, A Better Autoencoder for Image: Convolutional Autoencoder, In ICONIP17-DCEC. Available online: http://users. cecs. anu. edu. au/Tom. Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58. pdf.
19 Zhang, Y., Tian, X., Deng, X., and Cao, Y., 2010, Seismic Denoising Based on Modified BP Neural Network. In 2010 Sixth International Conference on Natural Computation, 4, 1825-1829, doi: 10.1109/ICNC.2010.5584501.
20 Zhao, A., 2016, Image Denoising with Deep Convolutional Neural Networks.
21 Zhao, X., Lu, P., Zhang, Y., Chen, J., and Li, X., 2019, Swellnoise attenuation : A deep learning approach. The Leading Edge, 38(12), 934-942, doi: 10.1190/tle38120934.1.   DOI
22 Zhu, W., Mousavi, S.M., and Beroza, G.C., 2019, Seismic Signal Denoising and Decomposition Using Deep Neural Networks. IEE Trans. Geosci. Remote Sens., 57(11), 9476-9488, doi: 10.1109/TGRS.2019.2926772.   DOI
23 Zhang, Q., and Benveniste, A., 1992, Wavelet Networks. IEEE Trans. Neural Netw., 3(6), 889-898, doi: 10.1109/72.165591.   DOI
24 Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T., 2013, DeCAF : A Deep Convolutional Activation Feature for Generic Visual Recognition. In International conference on machine learning, 647-655.
25 Alom, M. Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Essen, B.C. Van, Awwal, A.A.S., and Asari, V.K., 2019, A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3), 292, doi: 10.3390/electronics8030292.   DOI
26 Andersen, K.K., Kirkegaard, C., Foged, N., Christiansen, A. V, and Auken, E., 2016, Artificial neural networks for removal of couplings in airborne transient electromagnetic data. Geophys. Prospect., 64(3), 741-752, doi: 10.1111/1365-2478.12302.   DOI
27 Carbonari, R., Maio, R. Di, Piegari, E., Auria, L.D., Esposito, A., and Petrillo, Z., 2018, Filtering of noisy magnetotelluric signals by SOM neural networks. Phys. Earth Planet. Inter., 285, 12-22, doi: 10.1016/j.pepi.2018.10.004.   DOI
28 Chave, A.D., Thomson, D.J., and Ander, M.E., 1987, On the robust estimation of power spectra, coherences and, transfer functions. J. Geophys. Res., 92(B1), 633-648, doi: 10.1029/JB092iB01p00633.   DOI
29 Chen, X. L., Tian, M., and Yao, W. B., 2005, Gpr signals denoising by using wavelet networks. In 2005 International Conference on Machine Learning and Cybernetics, 8, 4690-4693, doi: 10.1109/ICMLC.2005.1527766.
30 Duchi, J., Hazan, E., and Singer, Y., 2011, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J. Mach. Learn. Res., 12(7), 2121-2159.
31 Ebrahimi, M.S., and Abadi, H.K., 2018, Study of Residual Networks for Image Recognition, arXiv preprint arXiv:1805.00325.
32 Egbert, G.D., 1997, Robust multiple-station magnetotelluric data processing. Geophys. J. Int., 130(2), 475-496, doi: 10.1111/j.1365-246X.1997.tb05663.x.   DOI
33 Fukushima, K., 1988, Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition. Neural Networks, 1, 119-130, doi: 10.1016/0893-6080(88)90014-7.   DOI
34 Gower, R.M., Loizou, N., Qian, X., Sailanbayev, A., Shulgin, E., and Richtarik, P., 2019, SGD : General Analysis and Improved Rates, arXiv preprint arXiv:1901.09401.
35 Gai, S., and Bao, Z., 2019, New image denoising algorithm via improved deep convolutional neural network with perceptive loss. Expert. Syst. Appl., 138, 112815, doi: 10.1016/j.eswa.2019.07.032.   DOI
36 Gamble, T.D., Goubau, W.M., and Clarke, J., 1979, Magnetotellurics with a remote magnetic reference. Geophysics, 44(1), 53-68, doi: 10.1190/1.1440923.   DOI
37 Goodfellow, I., 2016, Deep Learning. MIT Press.
38 Chen, Y., and Ma, J., 2014, Random noise attenuation by f-x empirical mode decomposition predictive filtering. Geophysics, 79(3), V81-V91, doi: 10.1190/geo2013-0080.1.   DOI
39 Hardt, M., Recht, B., and Singer, Y., 2016, Train faster, generalize better: stability of stochastic gradient descent. In International Conference on Machine Learning, 1225-1234.
40 He, K., Zhang, X., Ren, S., and Sun, J., 2016, Deep Residual Learning for Image Recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Patter Recognit., 770-778, doi: 10.1109/CVPR.2016.90.
41 Jain, V., and Seung, S., 2009, Natural image denoising with convolutional networks. Adv. Neural Neural. Inf. Process. Syst., 769-776.
42 Ji, Y., Wu, Q., Wang, Y., Lin, J., Li, D., Du, S., Yu, S., and Guan, S., 2018, Noise reduction of grounded electrical source airborne transient electromagnetic data using an exponential fitting-adaptive Kalman filter. Explor. Geophys., 49(3), 243-252, doi: 10.1071/EG16046.   DOI
43 Jo, J. H., and Ha, W. S., 2020, Case Analysis of Applications of Seismic Data Denoising Methods using DeepLearning Techniques. Geophys. And Geophys. Explor., 23(2), 72-88, doi: 10.7582/GGE.2020.23.2.072.   DOI
44 Lin, F., Chen, K., Wang, X., Cao, H., Chen, D., and Chen, F., 2019, A denoising stacked autoencoders for transient electromagnetic signal denoising. Nonlinear Processes in Geophysics, 26(1), 13-23, doi: 10.5194/npg-26-13-2019.   DOI
45 Kaya, A., Keceli, A. S., Catal, C., Yalic H. Y., Temucin, H., and Tekinerdogan, B., 2019, Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture, 158, 20-29, doi: 10.1016/j.compag.2019.01.041.   DOI
46 Kingma, D.P., and Ba, J., 2015, ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION 1-15, arXiv preprint arXiv:1412.6980.
47 Li, D., Wang, Y., Lin, J., Yu, S., and Ji, Y., 2017, Electromagnetic noise reduction in grounded electrical-source airborne transient electromagnetic signal using a stationarywavelet- based denoising algorithm. Near Surface Geophysics, 15(2), 163-173, doi: 10.3997/1873-0604.2017003.   DOI
48 Liu, F., Li, J., Liu, L., Huang, L., and Fang, G., 2017, Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system. J. Appl. Geophy., 139, 109-116, doi: 10.1016/j.jappgeo.2017.02.013   DOI
49 Lo, S.-C.B., Lou, S.-L.A., Lin, J.-S., Freedman, M.T., Chien, M. V, and Mun, S.K., 1995, Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans. Med. Imaging, 14(4), 711-718, doi:10.1109/42.476112.   DOI
50 Mandelli, S., Lipari, V., Bestagini, P., and Tubaro, S., 2019, Interpolation and Denoising of Seismic Data using Convolutional Neural Networks, arXiv preprint arXiv:1901.07927.
51 Manoj, C., and Nagarajan, N., 2003, The application of artificial neural networks to magnetotelluric time-series analysis. Geophys. J. Int.,, 153(2), 409-423, doi: 10.1046/j.1365-246X.2003.01902.x.   DOI