| Citation: | Chen Weiling, Xiao Fan. Advances in numerical modeling of metallogenic dynamics: A review of theories, methods and technologies[J]. Bulletin of Geological Science and Technology, 2023, 42(3): 234-249. doi: 10.19509/j.cnki.dzkq.2022.0125
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Based on geological surveys and experimental data, numerical modeling of metallogenic dynamics (NMMD) establishes a mathematical model (mathematical-physical equation) that quantitatively characterizes metallogenic processes using the basic laws of mathematics, physics and chemistry. Then, using the finite element or finite difference method, the model is built through efficient computer calculation, simulating the metallogenic dynamic process and its metallogenic response, revealing metallogenic law and guiding prospecting. NMMD integrates theories and methods of geology, mathematics, physics, chemistry, computers and other disciplines and has distinct characteristics of interdisciplinary integration. In recent years, driven by the rapid development of computational science and mathematical geology, important progress has been made in NMMD. This paper summarizes the basic theories and methods of NMMD, compares the characteristics of four metallogenic numerical simulation software programs, and introduces the development and application status of NMMD with progress of the author′s team in the past decade. The main conclusions and understandings are as follows: ①Multi-field coupled metallogenic dynamics numerical simulation is the only feasible method to reproduce the large-scale complex metallogenic process. With the rapid development and improvement of high-performance computing technology and nonlinear dynamics theory, it becomes one of the research hotspots and development directions of modern mathematical geoscience. It is important to reveal the metallogenic mechanism and obtain mineral exploration information, which has great potential for development; ②At present, there are some limitations in NMMD, such as uncertain simulation parameters and incomplete coupling of multifield processes, which will be the focus of its future development. Numerous studies have been devoted to solving these problems; ③Under a new paradigm of scientific research driven by big data, a combination of NMMD and machine learning can effectively invert the metallogenic process and quantitatively predict mineral resources. This method is an important breakthrough in the application of NMMD in deposit genesis and mineral exploration. This paper clarifies the basic methods and key problems of NMMD in promoting the study of deposit genesis and exploration, and expounds the frontier direction of NMMD, which provides basic guidance for the study of computational modeling of metallogenic dynamics.
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