A knowledge graph for standard carbonate microfacies and its application in the automatical reconstruction of the relative sea-level curve

[Display omitted] •The concepts, attributes and interrelations of carbonate microfacies and construction of ontology.•Knowledge graph constructed by using the ontology layer and entity layer.•Application algorithm for identifying microfacies and reconstructing relative sea-curve.•The relative sea level curve at the end of Ediacaran is reconstructed. The reconstruction of high-resolution sea-level variation curves in deep time based on the standard carbonate microfacies knowledge graph (SMFKG) is of great scientific significance for exploring the Earth system evolution and predicting future sea-level and climate changes. In this study, the concepts, attributes, and relationships among standard carbonate microfacies (SMF) are comprehensively analyzed; an ontology layer is established and its data layer is constructed using thin-section descriptions; and finally, the SMFKG is established. Additionally, based on the knowledge graph, an application for automatically identifying SMF using identification markers and reconstructing the high-resolution relative sea-level variation curve using the SMF and facies zones is compiled. Then, all thin sections of the late Ediacaran Dengying Formation in the western margin of the Yangtze Platform are observed and described in detail, the SMF and facies zones are identified automatically, and the relative sea-level curve is reconstructed automatically using the SMFKG. The reconstruction results show that the Yangtze Platform experienced four sea-level rise and fall cycles in the late Ediacaran, of which two intense regressions led to subaerial-exposed unconformities in the interior and top of the Dengying Formation, which is highly consistent with previous research results. This shows that the high-resolution relative sea-level variation curve in deep time can be reconstructed efficiently and intelligently using the SMFKG. Additionally, in the near future, the combination of an automatic digital slide-scanning system, machine-learning techniques, and the SMFKG can achieve one-stop fully automatic SMF recognition and reconstruction of high-resolution relative sea-level variation curves in deep time, which has a high application value.