10.5880/fidgeo.2024.031 eng dataset GFZ German Research Centre for Geosciences datapub@gfz-potsdam.de http://www.gfz-potsdam.de Helmholtz-Centre Potsdam - GFZ German Research Centre for Geosciences Helmholtz-Centre Potsdam - GFZ German Research Centre for Geosciences pointOfContact 2024-09-13 urn:ogc:def:crs:EPSG:4326 3D-SCS: Three-dimensional lithospheric-scale structural and density model of the South China Sea 2024-09-13 revision doi:10.5880/fidgeo.2024.031 Li, Yan MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China author Bott, Judith GFZ German Research Centre for Geosciences, Potsdam, Germany author Liu, Shaowen MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China author Tan, Pingchuan Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China author Anikiev, Denis GFZ German Research Centre for Geosciences, Potsdam, Germany author Scheck-Wenderoth, Magdalena GFZ German Research Centre for Geosciences, Potsdam, Germany author GFZ Data Services publisher 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. Li, Yan MOE Key Laboratory of Coast and Island Development, School of Geography and Ocean Science, Nanjing University, Nanjing, China Phd student dg20270062@smail.nju.edu.cn pointOfContact 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 NASA/GCMD Earth Science Keywords publication CC BY 4.0 CC BY 4.0 DOI of paper when available DOI IsSupplementTo 10.6038/cjg20140215 DOI Cites 10.1016/j.margeo.2017.07.022 DOI Cites 10.1016/j.marpetgeo.2021.105450 DOI Cites 10.1029/2021TC007127 DOI Cites 10.1016/j.jog.2021.101877 DOI Cites 10.1038/s41467-020-18448-y DOI Cites 10.1007/s11001-013-9173-9 DOI Cites 10.1016/j.tecto.2012.09.026 DOI Cites 10.1260/0144-5987.32.1.243 DOI Cites 10.1007/s11001-014-9237-5 DOI Cites 10.1016/j.jseaes.2015.09.013 DOI Cites 10.1016/j.epsl.2018.02.011 DOI Cites 10.1016/j.epsl.2019.115932 DOI Cites 10.1360/csb2008-53-19-2342 DOI Cites 10.1002/2013JB010395 DOI Cites 10.1016/j.margeo.2018.12.007 DOI Cites 10.5880/ICGEM.2015.1 DOI Cites 10.1016/j.marpetgeo.2011.01.004 DOI Cites 10.1016/j.marpetgeo.2007.11.004 DOI Cites 10.1016/j.tecto.2015.03.003 DOI Cites 10.1002/2016GC006247 DOI Cites 10.1016/j.gr.2022.09.005 DOI Cites 10.1002/gj.2842 DOI Cites 10.1002/cjg2.20234 DOI Cites 10.1007/s12583-009-0003-6 DOI Cites 10.1093/gji/ggz219 DOI Cites 10.1029/2020TC006260 DOI Cites 10.1016/j.pepi.2022.106966 DOI Cites 10.1016/j.margeo.2010.01.007 DOI Cites 10.5194/essd-11-647-2019 DOI Cites 10.1016/j.tecto.2007.11.012 DOI Cites 10.1016/j.marpetgeo.2016.07.022 DOI Cites 10.1016/j.marpetgeo.2018.02.020 DOI Cites 10.1130/B35001.1 DOI Cites 10.1007/s11001-019-09381-x DOI Cites 10.1029/2019JB018610 DOI Cites 10.1016/j.epsl.2013.06.007 DOI Cites 10.1002/2013JB010639 DOI Cites 10.1007/s12583-015-0588-x DOI Cites 10.1007/s11001-007-9014-9 DOI Cites 10.1016/j.jseaes.2007.09.004 DOI Cites 10.1007/s11001-008-9059-4 DOI Cites http://publications.iodp.org/proceedings/349/349title.html URL Cites 10.1016/j.earscirev.2022.103917 DOI Cites 10.1016/j.gr.2021.06.015 DOI Cites https://www.jto.ac.cn/EN/10.11978/2016122 URL Cites 10.1016/j.tecto.2018.06.002 DOI Cites 10.1093/gji/ggab006 DOI Cites 10.1016/j.marpetgeo.2021.105079 DOI Cites 10.1080/00206814.2018.1553114 DOI Cites 10.1007/s11001-015-9248-x DOI Cites 10.1130/G34402.1 DOI Cites 10.1016/j.marpetgeo.2014.03.012 DOI Cites 10.1016/j.earscirev.2023.104493 DOI Cites 10.1029/95JB01866 DOI Cites 10.1007/s11001-018-9353-8 DOI Cites 10.1016/j.marpetgeo.2020.104711 DOI Cites 10.1016/j.marpetgeo.2013.10.008 DOI Cites 10.1016/j.epsl.2006.01.008 DOI Cites 10.6038/cjg20141008 DOI Cites 10.1016/S0040-1951(01)00222-0 DOI Cites 10.3969/j.issn.0001-5733.2011.12.012 DOI Cites 10.1002/cjg2.1682 DOI Cites 10.1016/j.tecto.2016.09.022 DOI Cites 10.1016/j.tecto.2013.07.010 DOI Cites 10.1016/j.jseaes.2019.03.008 DOI Cites 10.1016/j.marpetgeo.2013.12.010 DOI Cites 10.1007/s11430-014-4835-2 DOI Cites 10.3969/j.issn.0001-5733.2011.12.019 DOI Cites 10.1016/j.jseaes.2014.02.018 DOI Cites 10.14379/iodp.proc.367368.2018 DOI Cites 10.1016/j.jseaes.2012.10.037 DOI Cites 10.1007/s11001-018-09376-0 DOI Cites 10.1002/2016JB013481 DOI Cites 10.1029/2019JB017785 DOI Cites 10.1080/00206814.2019.1695002 DOI Cites 10.1007/s11430-020-9654-4 DOI Cites 10.1016/j.tecto.2005.10.039 DOI Cites 10.3969/j.issn.1001-909X.2021.03.004 DOI Cites 10.1007/s12583-020-1064-9 DOI Cites 10.1002/cjg2.1691 DOI Cites 10.1016/j.jseaes.2020.104557 DOI Cites 10.1016/j.marpetgeo.2021.105255 DOI Cites 10.1016/j.tecto.2013.12.013 DOI Cites 10.1002/gj.3145 DOI Cites 10.1080/00206814.2019.1597392 DOI Cites 10.1007/s11430-011-4324-9 DOI Cites 10.1007/s11430-021-9894-7 DOI Cites 10.1016/j.marpetgeo.2021.105140 DOI Cites 10.1007/s11001-019-09378-6 DOI Cites 10.1007/s11001-019-09384-8 DOI Cites 10.1016/S0040-1951(01)00062-2 DOI Cites 10.1007/s11001-005-0732-6 DOI Cites 10.1007/s11001-012-9154-4 DOI Cites 10.1016/j.gr.2020.11.012 DOI Cites 10.1016/j.jseaes.2020.104536 DOI Cites 10.1029/2020JB019827 DOI Cites 10.1007/s11001-016-9266-3 DOI Cites 10.1016/j.marpetgeo.2020.104396 DOI Cites 10.16539/j.ddgzyckx.2022.03.018 DOI Cites 10.1016/j.epsl.2019.115862 DOI Cites 10.1002/2017GC007034 DOI Cites 10.1029/2020TC006547 DOI Cites 10.1111/bre.12220 DOI Cites 10.1016/j.epsl.2018.08.048 DOI Cites https://www.researchgate.net/publication/285770114 URL Cites 10.1016/j.tecto.2012.05.027 DOI Cites 10.1002/gj.3034 DOI Cites eng geoscientificInformation Study area in the South China Sea 111.1 120.3 9.5 20.9 http://doi.org/10.5880/fidgeo.2024.031 WWW:LINK-1.0-http--link Data Access - DOI Data Access - DOI information 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.