地球化学场及其在隐伏矿体三维预测中的作用

本文从物理学"场论"的角度介绍了地球化学场的概念,并从场源、场作用及地球化学指标分布三个基本要素出发阐述了地球化学场的扩散、对流-扩散的动力学机制。首先,按照采样介质,将地球化学场分为原生的岩石地球化学场,以及次生的土壤、水系沉积物、水文和气体地球化学场,描述了组成各类地球化学场的要素。其次,将地球化学场的分析方法概括为静态的空间结构分析和动态的时空结构分析两种,提出要以场的动力学机制为基础,利用地球物理学中的正、反演理论来研究四维时空中地球化学场的发展和演化。最后,探讨了地球化学场时空结构分析与三维地学模拟两者之间彼此补充和相互验证的关系,三维地学模拟构建的场源及空间介质模型,为地球化学场的正、反演提供了初始条件,地球化学场反演的结果又可用来修正三维地质模型;探讨了地球化学场与大数据分析技术间的关系,即采用大数据的"数据驱动"的思路来挖掘其与多元地学数据之间的隐性联系,探索其与成矿过程的关联性。地球化学场与三维地学模拟、大数据分析技术的结合将为隐伏矿体三维预测中地下成矿物质的分布和演化提供依据。

Geochemical field and its roles on the 3D prediction of concealed ore-bodies

From the perspective of field theory in physics, the concept of geochemical field is introduced, and its Fick diffusion and convection-diffusion kinetic mechanisms are explained from three basic components, i.e., the source, the field interaction and the geochemical index distribution of a geochemical field. Firstly, geochemical fields can be divided into rock primary geochemical fields and secondary geochemical fields, such as soil, stream sediment, hydro and gas geochemical fields, according to their sampling media, and the components of each geochemical field are described. Secondly, the analysis methods of geochemical fields are classified into static spatial structure analysis and dynamic spatio-temporal structure analysis, and the 4D spatio-temporal development and evolution of the geochemical field should be performed by forward/inversion modeling theory in geophysics based on its kinetic mechanism. Thirdly, the mutual complementary and verified relationship between the geochemical field forward/inversion modeling and 3D geosciences modeling is discussed, in which 3D geosciences modeling can provide the geochemical field forward/inversion modeling with the 3D models of source and spatial media as the initial conditions, meanwhile, the inversion results of geochemical field can modify the 3D geological model. Finally, the relationship between the geochemical field and big data analysis techniques is discussed, in which the data-driven thinking of big data can mine the implicit association between geochemical field and multi-source geosciences data to study the ore-forming processes. The combination of geochemical field forward/inversion modeling, 3D geosciences modeling and big data analysis techniques can provide 3D prediction of concealed ore-body with the evidences of underground distribution and evolution of ore-forming materials.