|
Araya-Polo, M., Jennings, J., Adler, A., et al., 2018. Deep-Learning Tomography. The Leading Edge, 37(1): 58–66. https://doi.org/10.1190/tle37010058.1 |
|
Asakawa, E., McIntyre, J., 2015. The Application of Equivalent Offset Migration (EOM) to Vertical Cable Seismic (VCS) Data. SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, New Orleans. https://doi.org/10.1190/segam2015-5897444.1 |
|
Bailey, J., Asakawa, E., Humphries, M., et al., 2017. Vertical Cable Seismic Processed Using VSP Methods, Eage Borehole Geophysics Workshop. https://doi.org/10.3997/2214-4609.201702476 |
|
Chapman, N. R., Gettrust, J. F., Walia, R., et al., 2002. High-Resolution, Deep-Towed, Multichannel Seismic Survey of Deep-Sea Gas Hydrates off Western Canada. Geophysics, 67(4): 1038–1047. https://doi.org/10.1190/1.1500364 |
|
Chen, G., 2024. Accurate Background Velocity Model Building Method Based on Iterative Deep Learning in Sparse Transform Domain. arXiv preprint arXiv: 2407.19419 |
|
Chen, G. X., Li, J., Chen, J. X., et al., 2025. High-Precision Sub-Seafloor Velocity Building Based on Joint Tomography and Deep Learning on OBS Data in the South China Sea. Journal of Earth Science, 36(2): 830–834. https://doi.org/10.1007/s12583-025-0170-0 |
|
Fichtner, A., Trampert, J., Cupillard, P., et al., 2013. Multiscale Full Waveform Inversion. Geophysical Journal International, 194(1): 534–556. https://doi.org/10.1093/gji/ggt118 |
|
Fouquet, Y., Cambon, P., Etoubleau, J., et al., 2010. Geodiversity of Hydrothermal Processes along the Mid-Atlantic Ridge and Ultramafic-Hosted Mineralization: A New Type of Oceanic Cu-Zn-Co-Au Volcanogenic Massive Sulfide Deposit. Diversity of Hydrothermal Systems on Slow Spreading Ocean Ridges. American Geophysical Union, Washington, D. C. https://doi.org/10.1029/2008gm000746 |
|
Górszczyk, A., Operto, S., Schenini, L., et al., 2019. Crustal-Scale Depth Imaging via Joint Full-Waveform Inversion of Ocean-Bottom Seismometer Data and Pre-Stack Depth Migration of Multichannel Seismic Data: A Case Study from the Eastern Nankai Trough. Solid Earth, 10: 765–784. https://doi.org/10.5194/se-10-765-2019 |
|
Humphris, S. E., Herzig, P. M., Miller, D. J., et al., 1995. The Internal Structure of an Active Sea-Floor Massive Sulphide Deposit. Nature, 377(6551): 713–716. https://doi.org/10.1038/377713a0 |
|
Lewis, W., Vigh, D., 2017. Deep Learning Prior Models from Seismic Images for Full-Waveform Inversion. SEG Technical Program Expanded Abstracts 2017. Houston, Texas. Society of Exploration Geophysicists. https://doi.org/10.1190/segam2017-17627643.1 |
|
Lindsey, J. P., 1989. The Fresnel Zone and Its Interpretive Significance. The Leading Edge, 8(10): 33–39. https://doi.org/10.1190/1.1439575 |
|
Murton, B. J., Lehrmann, B., Dutrieux, A. M., et al., 2019. Geological Fate of Seafloor Massive Sulphides at the TAG Hydrothermal Field (Mid-Atlantic Ridge). Ore Geology Reviews, 107: 903–925. https://doi.org/10.1016/j.oregeorev.2019.03.005 |
|
Pontbriand, C. W., Sohn, R. A., 2014. Microearthquake Evidence for Reaction-Driven Cracking within the Trans-Atlantic Geotraverse Active Hydrothermal Deposit: Microearthquakes at TAG Active Mound. Journal of Geophysical Research: Solid Earth, 119(2): 822–839. https://doi.org/10.1002/2013jb010110 |
|
Qi, Y. L., Chen, G. X., Chen, J. X., et al., 2026. Seismic Denoising via Multiround SCU-Net. IEEE Geoscience and Remote Sensing Letters, 23: 1–5. https://doi.org/10.1109/lgrs.2025.364 0968 doi: 10.1109/lgrs.2025.3640968 |
|
Shen, H. L., Tao, C. H., et al., 2020. Optimizing Spatial Distribution to Minimize the Inline Directivity for a Marine Air-Gun Source. Geophysics, 85(3): P37–P44. https://doi.org/10.1190/geo2019-0544.1 |
|
Tivey, M. A., Dyment, J., 2010. The Magnetic Signature of Hydrothermal Systems in Slow Spreading Environments. Diversity of Hydrothermal Systems on Slow Spreading Ocean Ridges. American Geophysical Union, Washington, D. C. https://doi.org/10.1029/2008gm000773 |
|
Wang, J., Xiao, Z. W., Liu, C., et al., 2019. Deep Learning for Picking Seismic Arrival Times. Journal of Geophysical Research: Solid Earth, 124(7): 6612–6624. https://doi.org/10.1029/2019jb017536 |
|
Widess, M. B., 1973. How Thin Is a Thin Bed? Geophysics, 38(6): 1176–1180. https://doi.org/10.1190/1.1440403 |
|
Yang, F. S., Ma, J. W., 2019. Deep-Learning Inversion: A Next-Generation Seismic Velocity Model Building Method. Geophysics, 84(4): R583–R599. https://doi.org/10.1190/geo201 8-0249.1 doi: 10.1190/geo2018-0249.1 |
|
Zhang, X. Y., Min, F., Pan, S. L., et al., 2024. DD-Net: Dual Decoder Network with Curriculum Learning for Full Waveform Inversion. IEEE Transactions on Geoscience and Remote Sensing, 62: 1–17. https://doi.org/10.1109/tgrs.2024.3358492 |
|
Zhang, Z. D., Alkhalifah, T., 2022. Regularized Elastic Full-Waveform Inversion Using Deep Learning. Advances in Subsurface Data Analytics. Elsevier, Amsterdam. https://doi.org/10.1016/b978-0-12-822295-9.00009-1 |
|
Zhu, Z., Tao, C., Shen, J., et al., 2020. Self-Potential Tomography of a Deep-Sea Polymetallic Sulfide Deposit on Southwest Indian Ridge. Journal of Geophysical Research: Solid Earth, 125(11): e2020JB019738. https://doi.org/10.1029/2020jb019738 |