Zhen-Jie Zhang a,b,c,*, Timothy Kusky d, Xin-Kun Yang a, Qiu-Ming Cheng b,c,e
a School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China

b State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, China

c Frontiers Science Center for Deep-time Digital Earth, China University of Geosciences, Beijing 100083, China

d State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China

e School of Earth Science and Engineering, Sun Yat-sen University, Zhuhai 51900, China

The enigmatic nature of the Precambrian era poses ongoing challenges for researchers seeking to uncover its secrets. With the exponential growth of scientific data, there exists a vast repository of information through which these mysteries can be explored. However, this data is often derived from fragmented investigations of geological phenomena within specific disciplinary domains and limited spatiotemporal boundaries, resulting in untapped potential for extracting undiscovered knowledge. To address this limitation, the field of big data science provides a foundation for multidisciplinary research on the geological evolution of the Precambrian. By harnessing the power of large-scale data integration and analysis, valuable insights into this pivotal era in Earth’s history can be revealed. This paper offers a comprehensive overview of big data types in geoscience and elucidates common analysis methods. Through a case study focused on the North China Craton (NCC), we demonstrate the application of conjoint analysis to a dataset comprising rock and mineral geochemistry data. Employing local singularity analysis and wavelet analysis on zircon age frequency and Hf isotopic time series, we reveal a persistent long-term periodicity of 800–500 million years since 3.5 billion years ago. This finding indicates a co-evolution between the NCC and the global supercontinent cycle dating back to the Archean period. To investigate the formation of the NCC, we employ machine learning-based crustal thickness evolution reconstruction, which highlights arc formation during subduction as the primary factor, with regional mantle activities playing a secondary role in the convergence. Combining spatio-temporal evolution analysis of magmatic intensity and εHf(t) values, we infer that the development of the NCC primarily resulted from a prolonged process of accretion targeting the Eastern Block (the primary continent nuclei) through the incorporation of diverse arc massifs. This examination of the NCC serves as an example of how data collection, processing, and utilization can enhance our understanding of formational and evolutionary processes, signaling a paradigm shift in Precambrian research driven by the integration of big data.

Original source: Zhang Z J, Kusky, T., Yang, X.K., Cheng, Q.M. A paradigm shift in Precambrian research driven by big data. Precambrian Research. 2023; 399: 107235. https://doi.org/10.1016/j.precamres.2023.107235