QUANTITATIVE STUDY ON SEDIMENTARY ENVIRONMENT

This paper introduces a new method, the Most Predictable Surface, which is used for quantitative study on sedimentary enviromcnt. It is known that some g-geological parameters and the geographical parameters are closely related. For the purpose of quantitative study on sedimentary environment the following mathematical model is set up: Where Zi is a value of the ith geological parameter,And ai is a coefficient of the ith geological parmeter which depemnds on the geographical parameters and characterizes the relationship between two groups of parameters. So U means a value of quantification of sedim-ntary environment. The coefficients (ai) may be obtained by means of the mathematical processing. As soon as Z and a are determined, we can get U in each section.Many values of U can be linked and formed contour map of quantitative sedimentary environments. Same values of U show the same properties of lithological paragenetic association, that is the same sedimentary environments.The form of contours shows the outlines of the sedimentary environments.The density of distribution of the contours means the degree of variation of the sedimentary environments. The denser sontours in a region shows acute sedimentary environments changeing, on the other hand, the steader. The sedimentary environments are researched in this paper as an example of the Upper Sinian Epoch phosphate bearing sequence in the South of China. The sequence consists of the phosphate rocks, dolomite rocks, clay and silica rocks. Depending on the relation between lithologies and sedimentary environments, the following five lithological states, five geological parameters, are determined, i.e., coarse-grained intraclastic rock (A),medium or mediumfine grained intra-poclastic rock (B),micritic dolomite rock (c), clay rocfc ( D ), and silica rock (E).Therefore, the lithologies from ( A ) to ( E ) are the different products de-osited in the different sedimentary environments that the hydrodynamical conditions are changed from strong to weak. The mathematical model is set up depending on the five geological parameters determined and the geographic positions, geographic parameters, in each section.The maps of the quantitative sedimentary environments, the contour maps of U, are made for the five sedimentary cycles in all period respectively. Depending on these maps the sedimentary environments of each cyclic period and the changing of the environments are researched. The researches show that the regions with positive values of U indicate a carbonate platform under the water, but the regions with negative values of U show a shllow sea basin,and the regions with dense U contours show the regions where the environments chauge acntely.The regions with zero contour and its neiphou-ring regions show the regions where two environments mentioned above transfer each other. In the study area, from beginning deposite in the wide area to the end of tipper Sinian Epoch, the basic framework of sedimentary environments is steady. The zones, i.e., Southwest Guizhou -Northeast Guizhou-Northwest Hunan-middle Hubei, are the trasitional zones between the carbonate platform and the shallow-sea basin, and the carbonate platform distributes mainly in the region to the northwest of the zones,bnt the shallow-sea basin distributes mainly in the region to the eastsouth of the zones. In the transitional zone between two environments that is the favourable environment for P-deposits, because the environments change acutely in this zone, and the properties of the deposits and the hydro-dyn amical conditions change frequently with the change of environments.Specially, as there are closed or semi-closed basin or "gulf" under the water in the platform or in its borderland, it is more favourable environments where P-deposits are deposited. However,the size,thinkness and P-bearing grade of the P-deposits in the other environments can not be compared with those of P-deosits in the enviroments mentioned above. The results of this research have demonstrated that the Most Predictable Surface me