The Application of Big Data and Machine Learning in Petrogeochemistry

With the advancement of big data and artificial intelligence technologies, geoscience research has entered a new phase. This paper examines the application of machine learning in petrogeochemistry, addressing areas such as geodynamic numerical simulation, metallogenic geological process modeling, tectonic environment discrimination of igneous, magma provenance, metallogenic prediction and ore deposit genetic type classification. Unlike traditional two-dimensional or three-dimensional methods of discrimination and visualization, machine learning techniques efficiently manage large-scale and high-dimensional data, greatly enhancing both accuracy and efficiency. This paper highlights the benefits of machine learning in petrological geochemistry research and provides recommendations for improving data processing and model application, supported by case studies. Ultimately, it asserts that the integration of machine learning with big data offers geologists powerful tools, facilitating the advanced development of petrological geochemistry research.