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2024 Vol.61, Issue 1 Preview Page

General Remarks

28 February 2024. pp. 61-81
Abbasi, S., Yu, S., Akram, J., Alam, M.I., and Sarosh, B., 2023. An adaptive linear-mode decomposition for effective separation of linear and nonlinear seismic events, ground roll, and random noise, Geophysics, 88(4), p.V303-V315. 10.1190/geo2022-0470.1
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(5), p.938-957. 10.1111/1365-2478.13189
Alfarraj, M. and AlRegib, G., 2019. Semi-supervised learning for acoustic impedance inversion, Proceedings of the SEG Technical Program Expanded Abstracts 2019, SEG, San Antonio, USA, p.2298-2302. 10.1190/segam2019-3215902.1
Alpdemir, M.N. and Sezgin, M., 2023. Analysis of Deep CNN-based Ground Penetrating Radar (GPR) Image Classification Process using Explainable AI, 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), Sivas, Turkiye, IEEE, p.1-7. 10.1109/ASYU58738.2023.10296758
Azevedo, L. and Soares, A., 2017. Geostatistical Methods for Reservoir Geophysics, Berlin: Springer, 141p. 10.1007/978-3-319-53201-1
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.E407-E419. 10.1190/geo2020-0871.1
Birnie, C. and Ravasi, M., 2023. Explainable artificial intelligence-driven mask design for self-supervised seismic denoising, Geophysical Prospecting, p.1-16. 10.1111/1365-2478.13480
Biswas, R., Sen, M.K., Das, V., and Mukerji, T., 2019. Prestack and poststack inversion using a physics-guided convolutional neural network, Interpretation, 7(3), p.SE161-SE174. 10.1190/INT-2018-0236.1
Chen, G., Yang, W., Liu, Y., Luo, J., and Jing, H., 2022. Envelope-Based Sparse-Constrained Deconvolution for Velocity Model Building, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-13. 10.1109/TGRS.2021.3063514
Choi, B., Pyun, S., Choi, W., Jo, C., and Yoon, J., 2022. Deep-Learning-Based GPR Data Interpretation Technique for Detecting Cavities in Urban Roads, Geophysics and Geophysical Exploration, 25(4), p.189-200.
Choi, W.C., Lee, G.H., Cho, S.G., Choi, B.H., and Pyun S.J., 2020. Fault Detection for Seismic Data Interpretation Based on Machine Learning: Research Trends and Technological Introduction, Geophysics and Geophysical Exploration, 23(2), p.97-114.
Colombo, D., Li, W., Rovetta, D., Sandoval-Curiel, E., and Turkoglu, E., 2020a. Physics-driven deep learning joint inversion, Proceedings of SEG Technical Program Expanded Abstracts, SEG, Virtual, p.1775-1779. 10.1190/segam2020-3424997.1
Colombo, D., Li, W., Sandoval-Curiel, E., and McNeice, G. W., 2020b. Deep-learning electromagnetic monitoring coupled to fluid flow simulators, Geophysics, 85(4), p.WA1-WA12. 10.1190/geo2019-0428.1
Devlin, J., Chang, M.W., Lee, K., and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805.
Dhara, A. and Sen, M.K., 2023. Elastic Full Waveform Inversion using a Physics guided deep convolutional encoder-decoder, IEEE Transactions on Geoscience and Remote Sensing, 61, p.5913118. 10.1109/TGRS.2023.3294427
Di, H., Chen, X., Maniar, H., and Abubakar, A., 2020. Semi-supervised seismic and well log integration for reservoir property estimation, Proceedings of SEG International Exposition and Annual Meeting, SEG, Virtual, p.D031S061R004. 10.1190/segam2020-3425747.1
Ding, M., Zhou, Y., and Chi, Y., 2024. Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously, MDPI remote sensing, 16, p.1-28. 10.3390/rs16020305
Dong, X., Ling, J., Lu, S., Wang, H., and Li, Y., 2022. Multiscale Spatial Attention Network for Seismic Data Denoising, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-17. 10.1109/TGRS.2022.3178212
Dou, Q., Wei, L., Magee, D.R., and Cohn, A.G., 2016. Real-Time Hyperbola Recognition and Fitting in GPR Data, IEEE Transactions on Geoscience and Remote Sensing, 55(1), p.51-62. 10.1109/TGRS.2016.2592679
Fang, W., Fu, L., Xu, W., Bian, A., and Li, H., 2023. CCNet-5D: 5D convolutional neural network for seismic data interpolation, Geophysics, 88(4), p.V333-V344. 10.1190/geo2022-0420.1
Feng, Q., Li, Y., and Wang, H., 2021. Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation, Geophysics, 86(1), p.T19-T31. 10.1190/geo2019-0815.1
Gal, Y. and Ghahramani, Z., 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning, Proceedings of the International Conference on Machine Learning, PMLR, NY, USA, p.1050-1059.
Gao, K., Huang, L., Zheng, Y., Lin, R., Hu, H., and Cladohous, T., 2022. Automatic fault detection on seismic images using a multiscale attention convolutional neural network, Geophysics, 87(1), p.N13-N29. 10.1190/geo2020-0945.1
Geng, Z., Hu, Z., Wu, X., Liang, L., and Fomel, S., 2022. Semisupervised salt segmentation using mean teacher, Interpretation, 10(3), p.SE21-SE29. 10.1190/INT-2021-0191.1
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y., 2016. Deep Learning, MIT press, Cambridge, England, p.767.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., and Bengio, Y., 2014. Generative adversarial nets, Advances in Neural Information Processing Systems, 27.
Guo, J., Li, Y., Jessell, M. W., Giraud, J., Li, C., Wu, L., Li, F., and Liu, S., 2021. 3D geological structure inversion from Noddy-generated magnetic data using deep learning methods, Computers & Geosciences, 149, p.1-11. 10.1016/j.cageo.2021.104701
Guo, Y., Fu, L., and Li, H., 2023. Seismic Data Interpolation Based on Multi-Scale Transformer, IEEE Geoscience and Remote Sensing Letters, 20, p.1-5. 10.1109/LGRS.2023.3298101
He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, p.770-778. 10.1109/CVPR.2016.9026180094
Hinton, G.E., Osindero, S., and Teh, Y.W., 2006. A fast learning algorithm for deep belief nets, Neural Computation, 18(7), p.1527-1554. 10.1162/neco.2006.18.7.152716764513
Ho, J., Jain, A., and Abbeel, P., 2020. Denoising diffusion probabilistic models, Advances in neural information processing systems, 33, p.6840-6851.
Hu, H., Fang, H., Wang, N., Ma, D., Dong, J., Li, B., Di, D., Zheng, H., and Wu, J., 2023a. Defects identification and location of underground space for ground penetrating radar based on deep learning, Tunnelling and Underground Space Technology, 140, p.1-12. 10.1016/j.tust.2023.105278
Hu, Y., Chen, J., Wu, X., and Huang, Y., 2021a. Deep Learning Enhanced Joint Inversion of Multiphysics Data with Nonconforming Discretization, Proceedings of the IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), Marina Bay Sands, Singapore, p.1489-1490. 10.1109/APS/URSI47566.2021.9703802PMC8623917
Hu, Y., Wei, X., Wu, X., Sun, J., Chen, J., Huang, Y., and Chen, J., 2023b. A deep learning-enhanced framework for Multiphysics joint inversion, Geophysics, 88(1), p.K13-K26. 10.1190/geo2021-0589.1
Hu, Y., Wei, X., Wu, X., Sun, J., Huang, Y., and Chen, J., 2023c. 3D Joint Inversion of Multi-physics Data Using Deep Learning Techniques, Proceedings of the URSI GASS 2023, Sapporo, Hokkaido, Japan, p.1-4. 10.23919/URSIGASS57860.2023.10265612
Hu, Z., Liu, S., Hu, X., Fu, L., Qu, J., Wang, H., and Chen, Q., 2021b. Inversion of magnetic data using deep neural networks, Physics of the Earth and Planetary Interiors, 311, p.1-18. 10.1016/j.pepi.2021.106653
Huang, R., Liu, S., Qi, R., and Zhang, Y., 2021, Deep learning 3D sparse inversion of gravity data, Journal of Geophysical Research: Solid Earth, 126(11), e2021JB022476. 10.1029/2021JB022476
Iqbal, N., 2023. DeepSeg: Deep Segmental Denoising Neural Network for Seismic Data, IEEE Transactions on Neural Networks and Learning System, 34(7), p.3397-3404. 10.1109/TNNLS.2022.320542136150003
Jia, Y. and Ma, J., 2017. What can machine learning do for seismic data processing? An interpolation application, Geophysics, 82(3), p.V163-V177. 10.1190/geo2016-0300.1
Jiao, J., Dong, S., Zhou, S., Zeng, Z., and Lin, T., 2023. 3-D Gravity and Magnetic Joint Inversion Based on Deep Learning Combined with Measurement Data Constraint. IEEE Transactions on Geoscience and Remote Sensing, 62, p.5900814. 10.1109/TGRS.2023.3339303
Jo, Y., Choi, Y., Seol, S., and Byun, J., 2022. Machine learning-based vertical resolution enhancement considering the seismic attenuation, Journal of Petroleum Science and Engineering, 208:109657 10.1016/j.petrol.2021.109657
Kang, S., Seol, S.J., Chung, Y., and Kwon, H.S., 2013. Pitfalls of 1D inversion of small-loop electromagnetic data for detecting man-made objects, Journal of applied geophysics, 90, p.96-109. 10.1016/j.jappgeo.2013.01.003
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
Khosro Anjom, F., Vaccarino, F., and Socco, L.V., 2024. Machine learning for seismic exploration: Where are we and how far are we from the holy grail?, Geophysics, 89(1), p.WA157-WA178. 10.1190/geo2023-0129.1
Kim, D. and Byun, J., 2020. Data augmentation using CycleGAN for overcoming the imbalance problem in petrophysical facies classification, Proceedings of the SEG International Exposition and Annual Meeting, SEG, Virtual, p.D031S041R004. 10.1190/segam2020-3427510.1
Kim, Y. and Nakata, N., 2018. Geophysical inversion versus machine learning in inverse problems, The Leading Edge, 32(12), p.894-901. 10.1190/tle37120894.1
Kingma, D.P. and Welling, M., 2013. Auto-encoding variational bayes, arXiv preprint arXiv:1312.6114.
Kong, S., Oh, J., Yoon, D., Ryu, D.W., and Kwon, H.S., 2023. Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys, Applied Sciences, 13(10), p.6250. 10.3390/app13106250
Konyushkova, K., Sznitman, R., and Fua, P., 2017. Learning active learning from data, Advances in Neural Information Processing Systems, 30.
Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25.
Lee, D., Shin, S.R., Yeo, E.M., and Chung, W., 2024. Denoising sparker seismic data with Deep BiLSTM in fractional Fourier transform, Elsevier Computers & Geosciences, 184, p.1-9. 10.1016/j.cageo.2024.105519
Li, J., Liu, Y., Yin, C., Ren, X., and Su, Y., 2020. Fast imaging of time-domain airborne EM data using deep learning technology, Geophysics, 85(5), p.E163-E170. 10.1190/geo2019-0015.1
Li, J., Wu, X., and Hu, Z., 2022a. Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-11. 10.1109/TGRS.2021.3057857
Li, S., Liu, B., Ren, Y., Chen, Y., Yang, S., Wang, Y., and Jiang, P., 2019. Deep-learning inversion of seismic data. arXiv preprint arXiv:1901.07733.
Li, X., Wu, B., Zhu, X., and Yang, H., 2022b. Consecutively Missing Seismic Data Interpolation Based on Coordinate Attention Unet, IEEE Geoscience and Remote Sensing Letters, 19, p.1-5. 10.1109/LGRS.2021.3128511
Liu, B., Guo, Q., Li, S., Liu, B., Ren, Y., Pang, Y., Guo, X., Liu, L., and Jiang, P., 2020a. 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, B., Guo, Q., Wang, K., Pang, Y., Nie, L., and Jiang, P., 2020b. Adaptive Convolution Neural Networks for Electrical Resistivity Inversion, IEEE Sensors Journal, 21(2), p.2055-2066. 10.1109/JSEN.2020.3021280
Liu, B., Pang, Y, Jiang, P, Liu, Z., Liu, B., Zhang, Y., Cai, Y., and Liu, J., 2023. Physics-Driven Deep Learning Inversion for Direct Current Resistivity Survey Data, IEEE Transactions on Geoscience and Remote Sensing, 61, p.1-11. 10.1109/TGRS.2023.3263842
Liu, J., Lin, Z., Padhy, S., Tran, D., Bedrax Weiss, T., and Lakshminarayanan, B., 2020c. Simple and principled uncertainty estimation with deterministic deep learning via distance awareness, Advances in Neural Information Processing Systems, 33, p.7498-7512.
Liu, N., He, T., Tian, Y., Wu, B., Gao, J., and Xu, Z., 2020d. Common-azimuth seismic data fault analysis using residual UNet, Interpretation, 8(3), p.SM25-SM37. 10.1190/INT-2019-0173.1
Liu, N., Wang, J., Gao, J., Chang, S., and Lou, Y., 2022a. Similarity-Informed Self-Learning and Its Application on Seismic Image Denoising, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-13. 10.1109/TGRS.2022.3210217
Liu, N., Zhang, Y., Yang, Y., Wang, Z., Liu, R., and Gao, J., 2024. Domain Adaptation-based Sparse Time-frequency Analysis and Its Application on Seismic Attenuation Estimation, Geophysics, 89(3), p.1-55. 10.1190/geo2023-0309.1
Liu, W., Wang, H., Xi, Z., Zhang, R., and Huang, X., 2022b. Physics-driven deep learning inversion with application to magnetotelluric, Remote Sensing, 14(13), p.3218. 10.3390/rs14133218
Liu, X., Li, B., Li, J., Chen, X., Li, Q., and Chen, Y., 2021a. Semi-supervised deep autoencoder for seismic facies classification, Geophysical Prospecting, 69(6), p.1295-1315. 10.1111/1365-2478.13106
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
Loginov, G.N., Duchkov, A.A., Litvichenko, D.A., and Alyamkin, S.A., 2022. Convolution neural network application for first-break picking for land seismic data, EAGE Geophysical Prospecting, 70, p.1093-1115. 10.1111/1365-2478.13192
Luiken, N., Ravasi, M., and Birnie, C., 2024. Integrating self-supervised denoising in inversion-based seismic deblending, Geophysics, 89(1), p.WA39-WA51. 10.1190/geo2023-0131.1
Maffezzoli, F., 2022. Salt segmentation of geophysical images through explainable CNNs, MS Thesis, Polytechnic University of Milan, Italy, 108p.
Noh, K., Kim, D., and Byun, J., 2023a. Explainable deep learning for supervised seismic facies classification using intrinsic method, IEEE Transactions on Geoscience and Remote Sensing, 61, p.1-11. 10.1109/TGRS.2023.3236500
Noh, K., Pardo, D., and Torres-Verdin, C., 2023b. Physics-guided deep-learning inversion method for the interpretation of noisy logging-while-drilling resistivity measurements, Geophysical Journal International, 235(1), p.150-165. 10.1093/gji/ggad217
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. and Byun, J., 2022. Bayesian uncertainty estimation for deep learning inversion of electromagnetic data, IEEE Geoscience and Remote Sensing Letters, 19, p.1-5. 10.1109/LGRS.2021.3072123
Oh, S., Noh, K., Yoon, D., Seol, S.J., and Byun, J., 2018. 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
Oh, S., Noh, K., Yoon, D., Seol, S.J., and Byun, J., 2019. Cooperative deep learning inversion: Seismic-constrained CSEM inversion for salt delineation, Proceedings of the SEG Technical Program Expanded Abstracts, SEG, San Antonio, USA, p.1055-1059. 10.1190/segam2019-3208029.1
Park, H., Lee, J.W., Hwang, J., and Min, D.J., 2022. Coarse-Refine Network With Upsampling Techniques and Fourier Loss for the Reconstruction of Missing Seismic Data, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-15. 10.1109/TGRS.2022.3190292
Park, J., Choi, J., Jee Seol, S., Byun, J., and Kim, Y, 2021. A method for adequate selection of training data sets to reconstruct seismic data using a convolutional U-Net, Geophysics, 86(5), p.V375-V388. 10.1190/geo2019-0708.1
Pham, N. and Fomel, S., 2021. Uncertainty and interpretability analysis of encoder-decoder architecture for channel detection, Geophysics, 86(4), p.49-58. 10.1190/geo2020-0409.1
Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C.P., Wang, X.Z., and Wu, Q.J., 2022. A review of generalized zero-shot learning methods, IEEE transactions on pattern analysis and machine intelligence, 45(4), p.4051-5070. 10.1109/TPAMI.2022.319169635849673
Pratap Singh, A., Vashisth, D., and Srivastava, S., 2021. Deep learning for joint geophysical inversion of seismic and MT data sets, Proceedings of the SEG International Exposition and Annual Meeting, SEG, Houston, USA, p.D011S071R006. 10.1190/segam2021-3583955.1
Rasht-Behesht, M., Huber, C., Shukla, K., and Karniadakis, G. E., 2022. Physics-informed neural networks (PINNs) for wave propagation and full waveform inversions, Journal of Geophysical Research: Solid Earth, 127(5), p.e2021JB023120. 10.1029/2021JB023120
Shi, X., Jia, Z., Geng, H., Liu, S., and Li, Y., 2023. Deep Learning Inversion for Multivariate Magnetic Data, IEEE Transactions on Geoscience and Remote Sensing, 62, p.1-10. 10.1109/TGRS.2023.3337413
Shi, Y., Wu, X., and Fomel, S., 2019. SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network, Interpretation, 7(3), p.SE113-SE122. 10.1190/INT-2018-0235.1
Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
Song, C. and Wang, Y., 2023. Simulating seismic multifrequency wavefields with the Fourier feature physics-informed neural network, Geophysical Journal International, 232(3), p.1503-1514. 10.1093/gji/ggac399
Song, L., Yin, X., Zong, Z., and Jiang, M., 2022. Semi-supervised learning seismic inversion based on Spatio-temporal sequence residual modeling neural network, Journal of Petroleum Science and Engineering, 208, p.109549. 10.1016/j.petrol.2021.109549
Stahlschmidt, S. R., Ulfenborg, B., and Synnergren, J., 2022. Multimodal deep learning for biomedical data fusion: a review, Briefings in Bioinformatics, 23(2), bbab569. 10.1093/bib/bbab56935089332PMC8921642
Su, Y., Wang, J., Li, D., Wang, X., Hu, L., Yao, Y., and Kang, Y., 2023. End-to-end deep learning model for underground utilities localization using GPR, Automation in Construction, 149, p.1-16. 10.1016/j.autcon.2023.104776
Sun, Y., Denel, B., Daril, N., Evano, L., Williamson, P., and Araya-Polo, M., 2020. Deep learning joint inversion of seismic and electromagnetic data for salt reconstruction, Proceedings of the SEG Technical Program Expanded Abstracts, SEG, San Antonio, USA, p.550-554. 10.1190/segam2020-3426925.1
Tao, B., Yang, Y. Zhou, H., Wang, Y., Lyu, F., and Li, W., 2023. Deep learning-based upgoing and downgoing wavefield separation for vertical seismic profile data, Geophysics, 88(6), p.D339-D355. 10.1190/geo2022-0577.1
Tarantola, A., 2005. Inverse Problem Theory and Methods for Model Parameter Estimation, Society for Industrial and Applied Mathematics, Philadelphia, USA, 333p. 10.1137/1.9780898717921
Telford, W.M., Geldart, L.P., and Sheriff, R.E., 1990. Applied geophysics (2nd Ed.), Cambridge University Press, Cambridge, England, 751p. 10.1017/CBO9781139167932
Terrasse, G., Nicolas, J.M., Trouvé, E., and Drouet, É., 2016. Sparse decomposition of the GPR useful signal from hyperbola dictionary, 24th European Signal Processing Conference (EUSIPCO), p.2400-2404. 10.1109/EUSIPCO.2016.7760679
Tikhonov, A.N. and Arsenin, V.Y., 1977. Solutions of ill-posed problems, Halsted Press, Winston, USA, 258p.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I., 2017. Attention is all you need, Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
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
Wang, F., Yang, B., Wang, Y., and Wang, M., 2022. Learning From Noisy Data: An Unsupervised Random Denoising Method for Seismic Data Using Model-Based Deep Learning, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-14. 10.1109/TGRS.2022.3165037
Wei, N., Yang, D., Li, Y., Shi, Y., and Wang, Y., 2021. Joint 3D inversion of gravity, magnetic and magnetotelluric data using deep learning, Proceedings of the Sixth International Conference on Engineering Geophysics, SEG, Virtual, p.307-310. 10.1190/iceg2021-078.1
Wei, N., Yang, D., Wang, Z., and Lu, Y., 2022a. Joint 3D inversion of gravity and magnetic data using deep learning neural networks, Proceedings of the SEG International Exposition and Annual Meeting, Houston, USA, p.D011S077R002. 10.1190/image2022-3751223.1PMC9615124
Wei, Y., Li, Y.E., Zong, J., Yang, J., Fu, H., and Sun, M., 2022b. Deep Learning-Based P- and S-Wave Separation for Multicomponent Vertical Seismic Profiling, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-16. 10.1109/TGRS.2021.3124413
Wilson, B., Singh, A., and Sethi, A., 2022. Appraisal of resistivity inversion models with convolutional variational encoder-decoder network, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-10. 10.1109/TGRS.2022.3217580
Wu, M., Fu, L., Fang, W., and Cao, J., 2024. Sparse prior-net: A sparse prior-based deep network for seismic data interpolation, Geophysics, 89(1), p.V37-V47. 10.1190/geo2022-0262.1
Wu, X., Liang, L., Shi, Y., and Fomel, S., 2019. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation, Geophysics, 84(3), p.IM35-IM45. 10.1190/geo2018-0646.1
Xu, Q., Wang, Z., and Li, X., 2023. Depth and Radius Joint Estimation for Underground Pipeline Using GPR, IEEE Sensors Journal, 23(13), p. 1-16. 10.1109/JSEN.2023.3280177
Yang, H., Li, P., Ma, F., and Zhang, J., 2023. Building near-surface velocity models by integrating the first-arrival traveltime tomography and supervised deep learning, Geophysical Journal International, 235(1), p.326-341. 10.1093/gji/ggad223
Yang, Q., Hu, X., Liu, S., Jie, Q., Wang, H., and Chen, Q., 2021. 3-D gravity inversion based on deep convolution neural networks, IEEE Geoscience and Remote Sensing Lettters, 19, p.1-5. 10.1109/LGRS.2020.3047131
Yang, Y., Zhang, X., Guan, Q., and Lin, Y., 2022. Making invisible visible: Data-driven seismic inversion with spatio-temporally constrained data augmentation, IEEE Transactions on Geoscience and Remote Sensing, 60, p.1-16. 10.1109/TGRS.2022.3144636
Yao, H., Ma, H., Li, Y., and Feng, Q., 2022. DnResNeXt Network for Desert Seismic Data Denoising, IEEE Geoscience and Remote Sensing Letters, 19, p.1-5. 10.1109/LGRS.2020.3044036
Yeeh, Z., Park, J., Seol, S.J., Yoon, D., and Byun, J., 2023, Trace-based Interpolation Using Machine Learning for Irregularly Missing Seismic Data, Geophysics and Geophysical Exploration, 26(2), p.62-76.
Yu, J. and Yoon, D., 2023. Crossline Reconstruction of 3D Seismic Data Using 3D cWGAN: A Comparative Study on Sleipner Seismic Survey Data, MDPI applied sciences, 13(10), p.1-22. 10.3390/app13105999
Yuan, S.Y., Zhao, Y., Xie, Tao, Qi, J., and Wang, S.X., 2022, SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces, KeAi Petroleum Science, 19(1), p.162-179. 10.1016/j.petsci.2021.10.010
Yuan, Z., Huang, H., Jiang, Y., Tang, J., and Li, J., 2019. An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learning, Interpretation, 7(3), p.T713-T725. 10.1190/INT-2018-0180.1
Zeng, Z., Han, J., Wang, T., Liu, L., and Chen, X., 2023. 3D Sequential Joint Inversion of Magnetotelluric, Magnetic and Gravity Data Based on Co-reference Model and Wide-range Petrophysical Constraints, IEEE Transactions on Geoscience and Remote Sensing, 61, p.4506213. 10.1109/TGRS.2023.3313563
Zhang, S., Yin, C., Cao, X., Sun, S., Liu, Y., and Ren, X., 2022. DecNet: Decomposition network for 3D gravity inversion, Geophysics, 87(5), p.G103-G114. 10.1190/geo2021-0744.1
Zhang, X., Han, L., Robinson, M., and Gallagher, A., 2021. A GANs-Based Deep Learning Framework for Automatic Subsurface Object Recognition From Ground Penetrating Radar Data, IEEE Access, 9, p.39009-09018. 10.1109/ACCESS.2021.3064205
Zhang, Y., Zhu, X., and Gao, J., 2023. Seismic inversion based on acoustic wave equations using physics-informed neural network, IEEE transactions on geoscience and remote sensing, 61, p.1-11. 10.1109/TGRS.2023.3236973
Zhao, Y.X., Li, Y., and Wu, N., 2022. Data augmentation and its application in distributed acoustic sensing data denoising, Geophysical Journal International, 228(1), p.119-133. 10.1093/gji/ggab345
Zhdanov, M.S., 2015. Inverse theory and applications in geophysics (2nd Ed.), Elsevier, Amsterdam, Netherlands, 730p.
Zhou, X., Chen, Z., Lv, Y., and Wang, S., 2023. 3-D Gravity Intelligent Inversion by U-Net Network With Data Augmentation, IEEE Transactions on Geoscience and Remote Sensing, 61, p.1-13. 10.1109/TGRS.2023.3241310
Zwartjes, P. and Yoo, J., 2022, First break picking with deep learning - evaluation of network architectures, Geophysical Prospecting, 70, p.318-342. 10.1111/1365-2478.13162
  • 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 : 61
  • No :1
  • Pages :61-81
  • Received Date : 2024-02-13
  • Revised Date : 2024-02-23
  • Accepted Date : 2024-02-27