• Ao Y, Lu W, Jiang B, Monkam P (2020) Seismic structural curvature volume extraction with convolutional neural networks. IEEE Trans Geosci Remote Sens 59(9):7370–7384

    Article 

    Google Scholar
     

  • Araya-Polo M, Jennings J, Adler A, Dahlke T (2018) Deep-learning tomography. Lead Edge 37(1):58–66

    Article 

    Google Scholar
     

  • Bouziat A, Guy N, Frey J, Colombo D, Colin P, Cacas-Stentz M-C, Cornu T (2019) An assessment of stress states in passive margin sediments: iterative hydro-mechanical simulations on basin models and implications for rock failure predictions. Geosciences 9(11):469

    Article 

    Google Scholar
     

  • Chen A, Darbon J, Morel J-M (2014) Landscape evolution models: a review of their fundamental equations. Geomorphology 219:68–86. https://doi.org/10.1016/j.geomorph.2014.04.037

    Article 

    Google Scholar
     

  • de la Varga M, Schaaf A, Wellmann F (2019) GemPy 1.0: open-source stochastic geological modeling and inversion. Geosci Model Dev 12(1):1–32

    Article 

    Google Scholar
     

  • Fauzi A, Mizutani N (2020) Potential of deep predictive coding networks for spatiotemporal tsunami wavefield prediction. Geosci Lett 7(1):1–13

    Article 

    Google Scholar
     

  • Fauzi A, Mizutani N (2020) Machine learning algorithms for real-time tsunami inundation forecasting: a case study in Nankai region. Pure Appl Geophys 177(3):1437–1450

    Article 

    Google Scholar
     

  • Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. arXiv preprint arXiv:1406.2661

  • Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) GANs trained by a two time-scale update rule converge to a local nash equilibrium. arXiv preprint arXiv:1706.08500

  • Karras T, Aittala M, Hellsten J, Laine S, Lehtinen J, Aila T (2020) Training generative adversarial networks with limited data. arXiv preprint arXiv:2006.06676

  • Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8110–8119

  • Li S, Liu B, Ren Y, Chen Y, Yang S, Wang Y, Jiang P (2020) Deep-learning inversion of seismic data. IEEE Trans Geosci Remote Sens 58(3):2135–2149. https://doi.org/10.1109/TGRS.2019.2953473

    Article 

    Google Scholar
     

  • Liu B, Yang S, Ren Y, Xu X, Jiang P, Chen Y (2021) Deep-learning seismic full-waveform inversion for realistic structural models. Geophysics 86(1):31–44

    Article 

    Google Scholar
     

  • Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784

  • Mulia IE, Gusman AR, Satake K (2020) Applying a deep learning algorithm to tsunami inundation database of megathrust earthquakes. J Geophys Res Solid Earth 125(9):2020–019690

    Article 

    Google Scholar
     

  • Oh S, Byun J (2021) Bayesian uncertainty estimation for deep learning inversion of electromagnetic data. IEEE Geosc Remote Sens Lett 19:1–5

    Article 

    Google Scholar
     

  • Ovcharenko O, Kazei V, Peter D, Alkhalifah T (2019) Style transfer for generation of realistically textured subsurface models. In: SEG technical program expanded abstracts 2019, pp 2393–2397

  • Puzyrev V (2019) Deep learning electromagnetic inversion with convolutional neural networks. Geophys J Int 218(2):817–832

    Article 

    Google Scholar
     

  • Puzyrev V, Swidinsky A (2021) Inversion of 1D frequency-and time-domain electromagnetic data with convolutional neural networks. Comput Geosci 149:104681

    Article 

    Google Scholar
     

  • Ren Y, Nie L, Yang S, Jiang P, Chen Y (2021) Building complex seismic velocity models for deep learning inversion. IEEE Access 9:63767–63778

    Article 

    Google Scholar
     

  • Salles T, Ding X, Webster JM, Vila-Concejo A, Brocard G, Pall J (2018) A unified framework for modelling sediment fate from source to sink and its interactions with reef systems over geological times. Sci Rep 8:5252. https://doi.org/10.1038/s41598-018-23519-8

    Article 

    Google Scholar
     

  • Sandfort V, Yan K, Pickhardt PJ, Summers RM (2019) Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci Rep 9(1):1–9

    Article 

    Google Scholar
     

  • Song S, Mukerji T, Hou J (2021) Geological facies modeling based on progressive growing of generative adversarial networks (GANs). Comput Geosci 25(3):1251–1273

    Article 

    Google Scholar
     

  • Tucker GE, Hancock GR (2010) Modelling landscape evolution. Earth Surf Proc Land 35(1):28–50. https://doi.org/10.1002/esp.1952

    Article 

    Google Scholar
     

  • Wu Y, Lin Y (2019) InversionNet: an efficient and accurate data-driven full waveform inversion. IEEE Trans Comput Imaging 6:419–433

    Article 

    Google Scholar
     

  • Wu X, Geng Z, Shi Y, Pham N, Fomel S, Caumon G (2020) Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics 85(4):27–39

    Article 

    Google Scholar
     

  • Yang F, Ma J (2019) Deep-learning inversion: a next-generation seismic velocity model building method. Geophysics 84(4):583–599

    Article 

    Google Scholar
     

  • Yang Q, Hu X, Liu S, Jie Q, Wang H, Chen Q (2021) 3-D gravity inversion based on deep convolution neural networks. IEEE Geosci Remote Sens Lett 19:1–5


    Google Scholar
     

  • Zhang T, Tilke P, Dupont E, Zhu L, Liang L, Bailey W (2019) Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks. In: International petroleum technology conference . OnePetro

  • Zhu W, Mousavi SM, Beroza GC (2019) Seismic signal denoising and decomposition using deep neural networks. IEEE Trans Geosci Remote Sens 57(11):9476–9488

    Article 

    Google Scholar
     

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