Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys

Juyeon Jeong; Hanna Jang; Desy Caesary; In Seok Joung; AHyun Cho; Daeung Yoon; Myung Jin Nam

doi:10.32390/ksmer.2022.59.4.379

All Issue

2022 Vol.59, Issue 4 Preview Page Next Page

Technical Report

Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys 전기전자탐사에 적용되는 심층 학습 연구 동향 및 적용 사례 분석

31 August 2022. pp. 379-397

PDF XML

Abstract

Technological innovations within the context of electrical and electromagnetic (EM) surveys have allowed for a rapid, efficient, and easier acquisition of a high quantity of data. Such innovations have been integral in mineral exploration and groundwater surveys. On the other hand, conventional inversion of electrical or EM survey data is computationally time-consuming and expensive. To circumvent the limitations of conventional inversion, the implementation of deep learning (DL) using improved neural networks has garnered substantial attention. In this study, we review various DL methods that can be used as substitutes for traditional inversion methods. Specifically, we investigate cases highlighting the successful implementation of DL to electrical or EM surveys and also comprehensively examine the advantages and disadvantages of such an application of DL.

Keywords

deep learning

electromagnetic survey

data processing

inversion

interpretation

전기전자탐사의 장비가 발전하면서, 탐사 시 측정할 수 있는 자료의 양이 증가하고 있을 뿐만 아니라 최근에는 광물 탐사 외에도 지하수 탐사나 오염 영역 탐사 등 매우 다양한 분야에서 적용되고 있다. 전기전자탐사 자료의 역산은, 계산 비용이 많이 들면서도 초기모델과 정규화 인자에 영향을 받는 한계가 있다. 이에 심층학습을 적용하여 자료를 처리하고 해석하는 기술에 대한 관심이 증가하여, 개선된 신경망을 이용한 심층 학습으로 역산을 구현하고자 하는 연구들이 수행되고 있다. 이 논문에서는 자료 처리에 적용될 수 있는 심층학습 방법들을 먼저 간략히 소개하고, 기존의 전기전자탐사 역산법을 간단히 설명하며 심층학습을 이용한 방법론을 분석하였다. 이후 전자탐사에 적용되는 기계학습 연구 동향과 그 사례들을 소개하고, 심층학습 적용의 장단점을 분석하였다.

키워드

기계학습

전기전자탐사

자료처리

역산

자료해석

References

Abdelrahman, E.M., Ammar, A.A., Sharafeldin, S.M., and Hassanein, H.I., 1997a. Shape and depth solutions from numerical horizontal self-potential gradients, Journal of Applied Geophysics, 37(1), p.31-43. 10.1016/S0926-9851(96)00058-4

Abdelrahman, E.M., El-Araby, T.M., Ammar, A.A., and Hassanein, H.I., 1997b. A least-squares approach to shape determination from residual self-potential anomalies, Pure and Applied Geophysics, 150(1), p.121-128. 10.1007/s000240050067

Abdelrahman, E.S. M., Ammar, A.A.B., Hassanein, H.I., and Hafez, M.A., 1998. Derivative analysis of SP anomalies. Geophysics, 63(3), p.890-897. 10.1190/1.1444399

Abdelrahman, E.S.M. and Sharafeldin, S.M., 1997. A least- squares approach to depth determination from self-potential anomalies caused by horizontal cylinders and spheres, Geophysics, 62(1), p.44-48. 10.1190/1.1444143

Aleardi, M., Vinciguerra, A., and Hojat, A., 2021. A convolutional neural network approach to electrical resistivity tomography, Journal of Applied Geophysics, 193, p.104434. 10.1016/j.jappgeo.2021.104434

Aleardi, M., Vinciguerra, A., Stucchi, E., and Hojat, A., 2022. Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator, Geophysical Prospecting, 70, p.938-995. 10.1111/1365-2478.13189

Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K., 2019. A state-of-the-art survey on deep learning theory and architectures, Electronics, 8(3), p.292. 10.3390/electronics8030292

Araya-Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T., and Hohl, D., 2017. Automated fault detection without seismic processing, The Leading Edge, 36(3), p.208-214. 10.1190/tle36030208.1

Aster, R.C., Borchers, B., and Thurber, C.H., 2018. Parameter estimation and inverse problems, Elsevier, 404p. 10.1016/B978-0-12-804651-7.00015-8

Azevedo, L. and Soares, A., 2017. Geostatistical methods for reservoir geophysics, Switzerland: Springer. 10.1007/978-3-319-53201-1

Bang, M., Byun, J., and Seol, S.J., 2022. Deep neural network-based airborne EM data inversion suitable for mountainous field sites, In expanded abstract of the 83rd EAGE Annual Conference & Exhibition, European Association of Geoscientists & Engineers, p.1-5. 10.3997/2214-4609.202210341

Bang, M., Oh, S., Noh, K., Seol, S. J., and Byun, J., 2021. Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural network, Geophysics, 86(6), p.407-419. 10.1190/geo2020-0871.1

Barfod, A.S., Lévy, L., and Larsen, J.J., 2021. Automatic processing of time domain induced polarization data using supervised artificial neural networks, Geophysical Journal International, 224(1), p.312-325. 10.1093/gji/ggaa460

Bedrosian, P.A., Ball, L.B., and Bloss, B.R., 2014. Airborne Electromagnetic Data and Processing Within Leech Lake Basin, Fort Irwin, California, US Department of the Interior, US Geological Survey, 20p. 10.3133/ofr20131024G

Bérubé, C.L., Chouteau, M., Shamsipour, P., Enkin, R.J., and Olivo, G.R., 2017. Bayesian inference of spectral induced polarization parameters for laboratory complex resistivity measurements of rocks and soils, Computers & Geosciences, 105, p.51-64. 10.1016/j.cageo.2017.05.001

Binley, A., Hubbard, S.S., Huisman, J.A., Revil, A., Robinson, D.A., Singha, K., and Slater, L.D., 2015. The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales, Water resources research, 51(6), p.3837-3866. 10.1002/2015WR01701626900183PMC4744786

Botev, Z.I., Grotowski, J.F., and Kroese, D.P., 2010. Kernel density estimation via diffusion, The annals of Statistics, 38(5), p.2916-2957. 10.1214/10-AOS799

Boyd, J., Chambers, J., Wilkinson, P., Peppa, M., Watlet, A., Kirkham, M., Jones, L., Swift, R., Meldrum, P.I., Uhlemann, S., and Binley, A., 2021. A linked geomorphological and geophysical modelling methodology applied to an active landslide, Landslides, 18(8), p.2689-2704. 10.1007/s10346-021-01666-w

Chen, J., Kemna, A., and Hubbard, S.S., 2008. A comparison between Gauss-Newton and Markov-chain Monte Carlo-based methods for inverting spectral induced-polarization data for Cole-Cole parameters, Geophysics, 73(6), p.F247-F259. 10.1190/1.2976115

Chen, K., Pu, X., Ren, Y., Qiu, H., Lin, F., and Zhang, S., 2020. TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal with Signal-to-Image Transformation, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-18. 10.1109/TGRS.2020.3034752

Cho, D.H., 2006. Electromagnetic survey in Korea, Economic and Environmental Geology, 39(4), p.427-440.

Cole, K.S. and Cole, R.H., 1942. Dispersion and absorption in dielectrics II. Direct current characteristics, The Journal of Chemical Physics, 10(2), p.98-105. 10.1063/1.1723677

Constable, S., Orange, A., and Key, K., 2015. And the geophysicist replied:"Which model do you want?", Geophysics, 80(3), p.E197-E212. 10.1190/geo2014-0381.1

Delon, J. and Houdard, A., 2018. Gaussian priors for image denoising. In Denoising of Photographic Images and Video, Springer, p.125-149. 10.1007/978-3-319-96029-6_5

Dias, C.A., 2000. Developments in a model to describe low-frequency electrical polarization of rocks, Geophysics, 65(2), p.437-451. 10.1190/1.1444738

El-Kaliouby, H.M., and Al-Garni, M.A., 2009. Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks, Journal of geophysics and engineering, 6(1), p.29-34. 10.1088/1742-2132/6/1/003

El-Qady, G. and Ushijima, K., 2001. Inversion of DC resistivity data using neural networks, Geophysical Prospecting, 49(4), p.417-430. 10.1046/j.1365-2478.2001.00267.x

Fallon, G.N., Fullagar, P.K., and Sheard, S.N., 1997. Application of geophysics in metalliferous mines, Australian Journal of Earth Sciences, 44(4), p.391-409. 10.1080/08120099708728321

Freeborn, T.J., Maundy, B., and Elwakil, A.S., 2012. Least squares estimation technique of Cole-Cole parameters from step response, Electronics letters, 48(13), p.1. 10.1049/el.2012.0360

Gad, E., Ushijima, K., and Hassanen, G., 1999. Reconnaissance electrical resistivity survey of geothermal reservoir at Hamam Faraun area, Sinai Peninsular, Egypt; Egypt {center {underscore} dot} Sinai hanto Faraun chiiki no chinetsu choryuso no denki tansa, Geophysical Exploration, 52.

Géron, A., 2019. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems, O'Reilly Media, Inc.

Ghorbani, A., Camerlynck, C., Florsch, N., Cosenza, P., and Revil, A., 2007. Bayesian inference of the Cole-Cole parameters from time‐and frequency‐domain induced polarization, Geophysical prospecting, 55(4), p.589-605. 10.1111/j.1365-2478.2007.00627.x

Giampaolo, V., Capozzoli, L., Grimaldi, S., and Rizzo, E., 2016. Sinkhole risk assessment by ERT: The case study of Sirino Lake (Basilicata, Italy), Geomorphology, 253, p.1-9. 10.1016/j.geomorph.2015.09.028

Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep learning, MIT press.

Haykin, S., 1994. Neural networks, A comprehensive foundation, 842p.

He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition. p.770-778. 10.1109/CVPR.2016.9026180094

Hellman, K., Günther, T., and Dahlin, T., 2012. September. Application of joint inversion and fuzzy c-means cluster analysis for road pre-investigations, In Near Surface Geoscience 2012-18th European Meeting of Environmental and Engineering Geophysics, European Association of Geoscientists & Engineers. p.306. 10.3997/2214-4609.20143417

Ho, T.L., 2009. 3-D inversion of borehole-to-surface electrical data using a back-propagation neural network, Journal of Applied Geophysics, 68(4), p.489-499. 10.1016/j.jappgeo.2008.06.002

Hornik, K., Stinchcombe, M., and White, H., 1990. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural networks, 3(5), p.551-560. 10.1016/0893-6080(90)90005-6

Ioffe, S. and Szegedy, C., 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift, In International conference on machine learning, p.448-456.

Islami, N., 2017. Evaluation of the Fate of Nitrate and Analysis of Shallow Soil Water using Geo-electrical Resistivity Survey, Journal of Engineering & Technological Sciences, 49(4). 10.5614/j.eng.technol.sci.2017.49.4.5

Ji, Y., Li, D., Yu, M., Wang, Y., Wu, Q., and Lin, J., 2016. A de-noising algorithm based on wavelet threshold-exponential adaptive window width-fitting for ground electrical source airborne transient electromagnetic signal, Journal of Applied Geophysics, 128, p.1-7. 10.1016/j.jappgeo.2016.03.001

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, Exploration Geophysics, 49(3), p.243-252. 10.1071/EG16046

Jiang, F.B., Dai, Q.W., and Dong, L., 2016. Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks, Applied Geophysics, 13(2), p.267-278. 10.1007/s11770-016-0561-1

Karaoulis, M., Revil, A., Tsourlos, P., Werkema, D.D., and Minsley, B.J., 2013. IP4DI: A software for time-lapse 2D/3D DC-resistivity and induced polarization tomography, Computers & Geosciences, 54, p.164-170. 10.1016/j.cageo.2013.01.008

Karaoulis, M., Tsourlos, P., Kim, J.H., and Revil, A., 2014. 4D time‐lapse ERT inversion: introducing combined time and space constraints, Near Surface Geophysics, 12(1), p.25-34. 10.3997/1873-0604.2013004

Kataoka, S. and Yasuda, M., 2019. Bayesian image denoising with multiple noisy images, The Review of Socionetwork Strategies, 13(2), p.267-280. 10.1007/s12626-019-00043-3

Kim, J.H., Yi, M.J., Song, Y., and Chung, S.H., 2001. A Study on the Modified Electrode Arrays in Two-Dimensional Resistivity Survey, Geophysics and Geophysical Exploration, 4(3), p.59-69.

Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization, arXiv preprint arXiv, p.1412-6980.

Langley, P., 2011. The changing science of machine learning, Machine learning, 82(3), p.275-279. 10.1007/s10994-011-5242-y

LeCun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning, Nature, 521, p.436-444. 10.1038/nature1453926017442

Lin, F., Chen, K., Wang, X., Cao, H., Chen, D., and Chen, F., 2019. Denoising stacked autoencoders for transient electromagnetic signal denoising, Nonlinear Processes in Geophysics, 26(1), p.13-23. 10.5194/npg-26-13-2019

Liu, B., Guo, Q., Li, S., Liu, B., Ren, Y., Pang, Y., Guo, X., Liu, L., and Jiang, P., 2020. Deep learning inversion of electrical resistivity data, IEEE Transactions on Geoscience and Remote Sensing, 58(8), p.5715-5728. 10.1109/TGRS.2020.2969040

Liu, W., Chen, R., and Yang, L., 2021a. Cole-Cole model parameter estimation from multi-frequency complex resistivity spectrum based on the artificial neural network, Journal of Environmental and Engineering Geophysics, 26(1), p.71-77. 10.32389/JEEG20-054

Liu, Z., Chen, H., Ren, Z., Tang, J., Xu, Z., Chen, Y., and Liu, X., 2021b. Deep learning audio magnetotellurics inversion using residual-based deep convolution neural network, Journal of Applied Geophysics, 188, p.104309. 10.1016/j.jappgeo.2021.104309

Loke, M.H. and Barker, R.D., 1995. Least-squares deconvolution of apparent resistivity pseudosections, Geophysics, 60(6), p.1682-1690. 10.1190/1.1443900

Marzán, I., Martí, D., Lobo, A., Alcalde, J., Ruiz, M., Alvarez- Marrón, J., and Carbonell, R., 2021. Joint interpretation of geophysical data: Applying machine learning to the modeling of an evaporitic sequence in Villar de Cañas (Spain), Engineering Geology, 288, p.106126. 10.1016/j.enggeo.2021.106126

Miranda, D.A. and Rivera, S.L., 2008. Determination of Cole-Cole parameters using only the real part of electrical impedivity measurements, Physiological Measurement, 29(5), p.669. 10.1088/0967-3334/29/5/01118460752

Monajemi, H., Donoho, D.L., and Stodden, V., 2016. Making massive computational experiments painless, In 2016 IEEE International Conference on Big Data (Big Data), p.2368-2373. 10.1109/BigData.2016.7840870

Müller, D., Kwan, K., and Groves, D.I., 2022. Integrated geophysical signatures and structural geometry of the Kabinakagami Lake greenstone belt, Superior Province, Ontario, Canada: Exploration implications for concealed Archean orogenic gold deposits, Journal of Applied Geophysics, 199, p.104613. 10.1016/j.jappgeo.2022.104613

Murthy, I.R., Sudhakar, K.S., and Rao, P.R., 2005. A new method of interpreting self-potential anomalies of two- dimensional inclined sheets, Computers & geosciences, 31(5), p.661-665. 10.1016/j.cageo.2004.11.017

Naudet, V., Gourry, J.C., Mathieu, F., Girard, J.F., Blondel, A., and Saada, A., 2011. 3D electrical resistivity tomography to locate DNAPL contamination in an urban environment, In Near Surface 2011-17th EAGE European Meeting of Environmental and Engineering Geophysics, European Association of Geoscientists & Engineers, p.253. 10.3997/2214-4609.20144385

Neyamadpour, A., Taib, S., and 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(11), p.2268-2274. 10.1016/j.cageo.2009.04.004

Noh, K., Yoon, D., and Byun, J., 2020. Imaging subsurface resistivity structure from airborne electromagnetic induction data using deep neural network, Exploration Geophysics, 51(2), p.214-220. 10.1080/08123985.2019.1668240

Oh, S., 2020. Cooperative Deep Learning Inversion of Electromagnetic Data with Seismic Constraint, Doctoral dissertation, Hanyang University.

Oh, S., Noh, K., Seol, S.J., and Byun, J., 2020. Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineationCooperative DL inversion, Geophysics, 85(4), p.E121-E137. 10.1190/geo2019-0532.1

Oh, S., Noh, K., Yoon, D., Seol, S.J., and Byun, J., 2019. Salt delineation from electromagnetic data using convolutional neural networks, IEEE Geoscience and Remote Sensing Letters, 16(4), p.519-523. 10.1109/LGRS.2018.2877155

Pelton, W.H., Ward, S.H., Hallof, P.G., Sill, W.R., and Nelson, P.H., 1978. Mineral discrimination and removal of inductive coupling with multifrequency IP, Geophysics, 43(3), p.588- 609. 10.1190/1.1440839

Pidlisecky, A. and Knight, R., 2008. FW2_5D: A MATLAB 2.5-D electrical resistivity modeling code, Computers & Geosciences, 34(12), p.1645-1654. 10.1016/j.cageo.2008.04.001

Prechelt, L., 1998. Automatic early stopping using cross validation: quantifying the criteria, Neural Networks, 11(4), p.761-767. 10.1016/S0893-6080(98)00010-0

Puzyrev, V. and Cela, J.M., 2015. A review of block Krylov subspace methods for multisource electromagnetic modelling, Geophysical Journal International, 202(2), p.1241-1252. 10.1093/gji/ggv216

Puzyrev, V., 2019. Deep learning electromagnetic inversion with convolutional neural networks, Geophysical Journal International, 218(2), p.817-832. 10.1093/gji/ggz204

Rajaraman, A. and Ullman, J.D., 2011. Mining of massive datasets, Cambridge University Press, 327p. 10.1017/CBO9781139058452

Rao, D.A., Babu, H.V., and Sinha, G.D.J., 1982. A Fourier transform method for the interpretation of self-potential anomalies due to two-dimensional inclined sheets of finite depth extent, Pure and Applied Geophysics, 120(2), p.365-374. 10.1007/BF00877042

Rao, S.J., Rao, P.R., and Murthy, I.R., 1993. Automatic inversion of self-potential anomalies of sheet-like bodies, Computers & Geosciences, 19(1), p.61-73. 10.1016/0098-3004(93)90043-5

Revil, A., Florsch, N., and Camerlynck, C., 2014. Spectral induced polarization porosimetry, Geophysical Journal International, 198(2), p.1016-1033. 10.1093/gji/ggu180

Ronneberger, O., Fischer, P., and Brox, T., 2015. October. U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention, Springer, Cham, p.234-241. 10.1007/978-3-319-24574-4_28

Rumelhart, D.E., Hinton, G.E., and Williams, R.J., 1986. Learning representations by back-propagating errors, Nature, 323(6088), p.533-536. 10.1038/323533a0

Sasaki, Y., 1989. Two-dimensional joint inversion of magnetotelluric and dipole-dipole resistivity data, Geophysics, 54(2), p.254-262. 10.1190/1.1442649

Shavlik, J.W., Dietterich, T., and Dietterich, T.G., 1990. Readings in machine learning, Morgan Kaufmann, 853p.

Shima, H., 1992. 2-D and 3-D resistivity image reconstruction using crosshole data, Geophysics, 57(10), p.1270-1281. 10.1190/1.1443195

Shin, S., Li, Y., Park, S., and Cho, S.J., 2018. Supervised machine learning for lithology estimation using spectral induced polarization data, In SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, p.1898-1902. 10.1190/segam2018-2998355.1

Shin, S.W., Park, S., and Shin, D.B., 2016. Spectral-induced polarization characterization of rocks from the Handuk iron mine, South Korea, Environmental Earth Sciences, 75(9), p.1-16. 10.1007/s12665-016-5618-1

Tábořík, P., Lenart, J., Blecha, V., Vilhelm, J., and Turský, O., 2017. Geophysical anatomy of counter-slope scarps in sedimentary flysch rocks (Outer Western Carpathians), Geomorphology, 276, p.59-70. 10.1016/j.geomorph.2016.09.038

Tarantola, A., 2005. Inverse problem theory and methods for model parameter estimation, Society for Industrial and Applied Mathematics. 10.1137/1.9780898717921

Ulyanov, D., Vedaldi, A., and Lempitsky, V., 2018. Deep image prior, In Proceedings of the IEEE conference on computer vision and pattern recognition, p.9446-9454.

Vinciguerra, A., Aleardi, M., Hojat, A., and Stucchi, E., 2020. Discrete cosines transform for parameter space reduction in Bayesian ERT inversion, In NSG2020 26th European Meeting of Environmental and Engineering Geophysics, European Association of Geoscientists & Engineers, 1, p.1-5. 10.3997/2214-4609.202020047

Vu, M.T. and Jardani, A., 2021. Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT, Geophysical Journal International, 225(2), p.1319-1331. 10.1093/gji/ggab024

Ward, W.O., Wilkinson, P.B., Chambers, J.E., Oxby, L.S., and Bai, L., 2014. Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection, Geophysical Journal International, 197(1), p.310-321. 10.1093/gji/ggu006

Watlet, A., Kaufmann, O., Triantafyllou, A., Poulain, A., Chambers, J.E., Meldrum, P.I., Wilkinson, P.B., Hallet, V., Quinif, Y., Ruymbeke, M.V., and Van Camp, M., 2018. Imaging groundwater infiltration dynamics in the karst vadose zone with long-term ERT monitoring, Hydrology and Earth System Sciences, 22(2), p.1563-1592. 10.5194/hess-22-1563-2018

Wu, S., Huang, Q., and Zhao, L., 2021. Convolutional neural network inversion of airborne transient electromagnetic data, Geophysical Prospecting, 69(8-9), p.1761-1772. 10.1111/1365-2478.13136

Wu, S., Huang, Q., and Zhao, L., 2022. Instantaneous inversion of airborne electromagnetic data based on deep learning, Geophysical Research Letters, e2021GL097165. 10.1029/2021GL097165

Wu, Y., Lu, C., Du, X., and Yu, X., 2014. A denoising method based on principal component analysis for airborne transient electromagnetic data, Computing Techniques for Geophysical and Geochemical Exploration, 36(2), p.170-176.

Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, Xiaobing., Kaiser, L., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W., Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., and Dean, J., 2016. Google's neural machine translation system: Bridging the gap between human and machine translation, arXiv preprint arXiv:1609.08144.

Xiang, J., Jones, N.B., Cheng, D., and Schlindwein, F.S., 2001. Direct inversion of the apparent complex-resistivity spectrum, Geophysics, 66(5), 1399-1404. 10.1190/1.1487085

Xu, H.L. and Wu, X.P., 2006. 2‐D Resistivity Inversion Using the Neural Network Method, Chinese Journal of Geophysics, 49(2), p.507-514. 10.1002/cjg2.861

Xu, Z., Xin, H., Li, J., and Lu, F., 2019. Electrical characteristics analysis of Mesozoic and Cenozoic evolution mechanisms of basins in the Dachaidan area, Qaidam Basin, Acta Geol. Sin., 93, p.3282-3298.

Yamada, Y., Iwamura, M., and Kise, K., 2018. Shakedrop regularization, arXiv preprint arXiv:1802.02375.

Yang, Y., Ni, W., Sun, Q., Wen, H., and Teng, Z., 2013. Improved Cole parameter extraction based on the least absolute deviation method. Physiological Measurement, 34(10), p.1239. 10.1088/0967-3334/34/10/123924021745

100

Yu, S. and Ma, J., 2021. Deep learning for geophysics: Current and future trends, Reviews of Geophysics, 59(3), p.e2021RG000742. 10.1029/2021RG000742

101

Zhang, K., Zuo, W., and Zhang, L., 2018. FFDNet: Toward a fast and flexible solution for CNN-based image denoising, IEEE Transactions on Image Processing, 27(9), p.4608-4622. 10.1109/TIP.2018.283989129993717

102

Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L., 2017a. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising, IEEE transactions on image processing, 26(7), p.3142-3155. 10.1109/TIP.2017.266220628166495

103

Zhang, K., Zuo, W., Gu, S., and Zhang, L., 2017b. Learning deep CNN denoiser prior for image restoration, In Proceedings of the IEEE conference on computer vision and pattern recognition, p.3929-3938. 10.1109/CVPR.2017.300PMC5717859

104

Zhdanov, M.S. and Keller, G.V., 1994. The geoelectrical methods in geophysical exploration, Methods in geochemistry and geophysics, 31, p.1-9.

Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 59
No :4
Pages :379-397
Received Date : 2022-07-06
Revised Date : 2022-08-19
Accepted Date : 2022-08-29
DOI :https://doi.org/10.32390/ksmer.2022.59.4.379

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

All Issue