Deep-Sea Sulfide Orebodies Velocity Modeling from Near-Bottom Seismic Data: A Hybrid Inversion Based on Physical Simulation

References

[1] Araya-Polo M, Jennings J, Adler A, et al. Deep-Learning Tomography. The Leading Edge, 2018, 3715866.

[2] Asakawa E, McIntyre J. The Application of Equivalent Offset Migration (EOM) to Vertical Cable Seismic (VCS) Data. SEG Technical Program Expanded Abstracts 2015, 2015, New Orleans: Society of Exploration Geophysicists

[3] Bailey J, Asakawa E, Humphries M, et al. Vertical Cable Seismic Processed Using VSP Methods. Eage Borehole Geophysics Workshop, 2017

[4] Chapman N R, Gettrust J F, Walia R, et al. High-Resolution, Deep-Towed, Multichannel Seismic Survey of Deep-Sea Gas Hydrates off Western Canada. Geophysics, 2002, 67410381047.

[5] Chen, G., 2024. Accurate Background Velocity Model Building Method Based on Iterative Deep Learning in Sparse Transform Domain. arXiv preprint arXiv: 2407.19419

[6] Chen G X, Li J, Chen J X, et al. 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, 2025, 362830834.

[7] Fichtner A, Trampert J, Cupillard P, et al. Multiscale Full Waveform Inversion. Geophysical Journal International, 2013, 1941534556.

[8] Fouquet Y, Cambon P, Etoubleau J, et al. 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, 2010, Washington, D. C.: American Geophysical Union

[9] Görszczyk A, Operto S, Schenini L, et al. 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, 2019, 10: 765-784.

[10] Humphris S E, Herzig P M, Miller D J, et al. The Internal Structure of an Active Sea-Floor Massive Sulphide Deposit. Nature, 1995, 3776551713716.

[11] Lewis W, Vigh D. Deep Learning Prior Models from Seismic Images for Full-Waveform Inversion. SEG Technical Program Expanded Abstracts 2017, 2017, Houston, Texas: Society of Exploration Geophysicists

[12] Lindsey J P. The Fresnel Zone and Its Interpretive Significance. The Leading Edge, 1989, 8103339.

[13] Marsset B, Ker S, Thomas Y, et al. Deep-Towed High Resolution Seismic Imaging II: Determination of P-Wave Velocity Distribution. Deep Sea Research Part I: Oceanographic Research Papers, 2018, 132: 29-36.

[14] Murton B J, Lehrmann B, Dutrieux A M, et al. Geological Fate of Seafloor Massive Sulphides at the TAG Hydrothermal Field (Mid-Atlantic Ridge). Ore Geology Reviews, 2019, 107: 903-925.

[15] Pontbriand C W, Sohn R A. 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, 2014, 1192822839.

[16] Qi Y L, Chen G X, Chen J X, et al. Seismic Denoising via Multiround SCU-Net. IEEE Geoscience and Remote Sensing Letters, 2026, 23: 1-5

[17] Shen H L, Tao C H, et al. Optimizing Spatial Distribution to Minimize the Inline Directivity for a Marine Air-Gun Source. Geophysics, 2020, 853P37P44.

[18] Tivey M A, Dyment J. The Magnetic Signature of Hydrothermal Systems in Slow Spreading Environments. Diversity of Hydrothermal Systems on Slow Spreading Ocean Ridges, 2010, Washington, D. C.: American Geophysical Union

[19] Wang J, Xiao Z W, Liu C, et al. Deep Learning for Picking Seismic Arrival Times. Journal of Geophysical Research: Solid Earth, 2019, 124766126624.

[20] Widess M B. How Thin Is a Thin Bed?. Geophysics, 1973, 38611761180.

[21] Yang F S, Ma J W. Deep-Learning Inversion: A Next-Generation Seismic Velocity Model Building Method. Geophysics, 2019, 844R583R599.

[22] Zhang X Y, Min F, Pan S L, et al. DD-Net: Dual Decoder Network with Curriculum Learning for Full Waveform Inversion. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-17

[23] Zhang Z D, Alkhalifah T. Regularized Elastic Full-Waveform Inversion Using Deep Learning. Advances in Subsurface Data Analytics, 2022, Amsterdam: Elsevier

[24] Zhu Z, Tao C, Shen J, et al. Self-Potential Tomography of a Deep-Sea Polymetallic Sulfide Deposit on Southwest Indian Ridge. Journal of Geophysical Research: Solid Earth, 2020, 12511e2020JB019738.

Rights & permissions

China University of Geosciences (Wuhan) and Springer-Verlag GmbH Germany, Part of Springer Nature