10.5880/fidgeo.2024.031 Li, Yan Yan Li 0009-0004-5013-8988 MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China GFZ German Research Centre for Geosciences, Potsdam, Germany Bott, Judith Judith Bott 0000-0002-2018-4754 GFZ German Research Centre for Geosciences, Potsdam, Germany Liu, Shaowen Shaowen Liu 0000-0002-9358-9648 MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China Tan, Pingchuan Pingchuan Tan 0000-0002-3448-7051 Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China Anikiev, Denis Denis Anikiev 0000-0002-4729-2659 GFZ German Research Centre for Geosciences, Potsdam, Germany Scheck-Wenderoth, Magdalena Magdalena Scheck-Wenderoth 0000-0003-0426-8269 GFZ German Research Centre for Geosciences, Potsdam, Germany RWTH Aachen University, Faculty of Georesources and Material Engineering, Aachen, Germany GFZ Data Services 2024 South China Sea Hainan plume plume-ridge interaction EARTH SCIENCE > SOLID EARTH EARTH SCIENCE > SOLID EARTH > GRAVITY/GRAVITATIONAL FIELD EARTH SCIENCE > SOLID EARTH > GRAVITY/GRAVITATIONAL FIELD > GRAVITY EARTH SCIENCE > SOLID EARTH > TECTONICS EARTH SCIENCE > SOLID EARTH > TECTONICS > PLATE TECTONICS EARTH SCIENCE > SOLID EARTH > TECTONICS > PLATE TECTONICS > STRATIGRAPHIC SEQUENCE EARTH SCIENCE SERVICES > MODELS EARTH SCIENCE SERVICES > MODELS > GEOLOGIC/TECTONIC/PALEOCLIMATE MODELS Li, Yan Yan Li 0009-0004-5013-8988 MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China GFZ German Research Centre for Geosciences, Potsdam, Germany Li, Yan MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China Li, Yan MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China Dataset DOI of paper when available 10.6038/cjg20140215 10.1016/j.margeo.2017.07.022 10.1016/j.marpetgeo.2021.105450 10.1029/2021TC007127 10.1016/j.jog.2021.101877 10.1038/s41467-020-18448-y 10.1007/s11001-013-9173-9 10.1016/j.tecto.2012.09.026 10.1260/0144-5987.32.1.243 10.1007/s11001-014-9237-5 10.1016/j.jseaes.2015.09.013 10.1016/j.epsl.2018.02.011 10.1016/j.epsl.2019.115932 10.1360/csb2008-53-19-2342 10.1002/2013JB010395 10.1016/j.margeo.2018.12.007 10.5880/ICGEM.2015.1 10.1016/j.marpetgeo.2011.01.004 10.1016/j.marpetgeo.2007.11.004 10.1016/j.tecto.2015.03.003 10.1002/2016GC006247 10.1016/j.gr.2022.09.005 10.1002/gj.2842 10.1002/cjg2.20234 10.1007/s12583-009-0003-6 10.1093/gji/ggz219 10.1029/2020TC006260 10.1016/j.pepi.2022.106966 10.1016/j.margeo.2010.01.007 10.5194/essd-11-647-2019 10.1016/j.tecto.2007.11.012 10.1016/j.marpetgeo.2016.07.022 10.1016/j.marpetgeo.2018.02.020 10.1130/B35001.1 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https://www.researchgate.net/publication/285770114 10.1016/j.tecto.2012.05.027 10.1002/gj.3034 1.2 CC BY 4.0 We present a comprehensive 3D lithospheric-scale model of the South China Sea region (SCS), which reveals the structural configuration of the area. This model delineates seven distinct geological units: (1) seawater, (2) sedimentary cover, (3) continental crystalline crust, (4) oceanic crust, (5) upper lithospheric mantle, (6) lower lithospheric mantle, and (7) sub-lithospheric mantle. The model covers an area of 960 km × 1260 km and reach down to a depth of 250 km. It is provided as uniformly spaced grids with 10 km intervals for each unit. The geometries and density distributions within the crust have been compiled and interpolated from a variety of datasets, predominantly seismic data (see section 6). To eliminate boundary effects, the model boundaries have been extended by more than 500 km in all horizontal directions, incorporating additional constraining data from the extended region. Additionally, we provide gridded gravity field data, a density voxel cube for the sub-lithospheric mantle, and relevant tomography data. Notably, the density of the lower lithospheric mantle was derived from 3D gravity inversion modeling.
Topography and bathymetry data were obtained from the ETOPO_2022 dataset (NOAA National Centers for Environmental Information, 2022). Then, we integrated reflection and refraction seismic profiles (Table 1 and 2) to constrain the sediment base and the Moho interface, and where seismic profiles were lacking, we used a global crustal model-ECM1 (Mooney et al., 2023) to fill gaps.

To derive sediment thickness from Multi-Channel Seismic (MCS) reflection data (as listed in Table 1, section 6), the two-way travel time (TWT) was converted to depth below the seafloor using specific time-depth conversion formulas (Table 3). For the Moho interface, the TWT within the crystalline crust layer is converted to depth below sediment basement by using the time-depth relationship established by Huang et al. (2023) (Table 3), which is based on velocity-depth profiles from seismic refraction data in the SCS. Additionally, the Ocean Bottom Seismometer (OBS) refraction data (Table 2), presented in depth terms, were extracted directly through digitization. For the upper mantle, we converted the Vs tomography data of Tang & Zheng (2013) into mantle temperature using the method of Priestley & McKenzie (2006) and defined the depth of the 1300°C isotherm as the Lithosphere-Asthenosphere Boundary (LAB).

To enhance the gravity response of our 3D density models, we referenced the free-air gravity disturbance at an altitude of 6 km above sea level, as derived from the EIGEN-6C4 model (Förste et al., 2014; Ince et al., 2019), a global gravity model that combines satellite and terrestrial data sources. We selected a height of 6 km that is above the highest topographical point of the model in order to ensure that all gravity observations are outside the subsurface space of relevant mass variations.




Study area in the South China Sea 111.1 120.3 9.5 20.9