| The study of sandstone-type uranium mineralization system has been a hot issue in uranium mining and exploration all over the world,and it has also become the focus of digital geological science research.Solving the complexity of digital geosciences requires creating models that quantify,standardize,and describe the extent of geological processes(time and space)more accurately.Therefore,it has more adaptability and academic value to use stochastic model of geological process to express geological meaning.Starting from the complexity analysis of sandstone-type uranium mineralization system,this paper is an exploratory study on the basis of previous research results.Sandstone type uranium deposit is under supergene geological process,provided by the peripheral different uranium parent rock erosion source area of uranium and associated elements after weathering,stripping,hydrolysis,migration,deposition,aggregation,and a series of deep evolution,formed in the course of the surface soil and water dispersion halo or stream sediment,on behalf of the trace element migration,the geochemical data is multivariate process for various period of time,to establish the discrete sample space which is formed by the grid sampling it is in the nature of superposition.As a result of the difference between the basins and the basins,the migration direction of ore-bearing fluids generally releases energy from high to low terrains.Therefore,reflected in the geochemical element ions in data grid characteristics,can be abstracted as material of particle orientation movement(limited Brownian motion),since mobile directional property of the process,can think space particle properties related to its adjacent above particle source,that is to say,according to the fluid direction,the space character ofparticle and its upstream neighboring points only show a strong correlation,and has nothing to do with between upstream or downstream spacing points or weak correlation,this kind of space operation state inspired us,element particle movement presents strong without memory,namely comply with markov property.In view of the discrete nature of the sampling grid,it can be considered that the element ion migration particles form markov chains.The migration process of surficial uranium geochemical elements is originally continuous or quasi-continuous,but due to the discretization of process sampling space,the movement of element particles can be characterized by Markov chains,which is the reason why this paper USES Markov chains to measure and explain the migration evolution process of uranium and related geochemical elements.The whole mineralization process can be divided into: the study of the metallogenic spatial distribution of sandstone-type uranium ore marked by the markov chain model of logging data and the markov chain model of supergene geochemical element migration process and the combination of the two stochastic models.Thus,the markov chain model of sandstone type uranium mineralization system is verified,and its position and supporting function in the quantitative evaluation of sandstone type uranium resources are proved.The content of this paper is a part of the fifth topic "Uranium Resource Potential Evaluation based on Big data"(subject No.2015CB453005)in the project of "Sedimentary environment and Large-scale mineralization of continental basin of giant sandstone uranium metallogenic Belt in Northern China" of National plan 973.In the east margin of ordos basin borehole logging data and geochemical elements as the data support,create a markov chain model and geochemical logging data element migration process two stochastic model,markov chain model and according to the results measured inside the basin structure and the analysis of characteristics of ore formation,sedimentary facies and explain the outside migration of uranium and associated elements represent evolutionary process,finally to continental basin sandstone type uranium deposit geochemical element migration capacity analysis and provides the theory basis for mineralization process.Its main results are as follows:1.Formation state space Markov chain model analysis based on borehole loggingdata(1)Establish the markov chain model of uranium ore host formation by using borehole logging data,and determine the transfer size of lithologic states of each layer through formation transfer probability calculation;(2)Establish the markov entropy of the ore bearing formation by using borehole logging data,and reveal the random occurrence law of formation lithologic transfer probability;(3)Conduct standardized processing of logging borehole data,establish bayesian model of borehole logging data of sandstone-type uranium deposits,and infer the structure of sand and mud in the basin;(4)According to the curve shape of borehole logging hole curve,infer the lithologic state of the target area,the internal structure of sand body and the control of sedimentary relative sandstone uranium mineralization;2.Markov chain model analysis based on discrete sampling data of uranium and related geochemical elements(1)After the pretreatment of geochemical elements and the elimination of "singular value",through the correlation analysis of geochemical elements,taking boron(B),uranium(U)and alum(V)three elements with high correlation as examples,the Markov transition probability model of element migration was established,and the content two-dimensional diagram and the three-dimensional diagram of transfer probability were drawn;(2)through the markov transition probability geochemical element migration,drawing with boron(B),uranium(U),vanadium(V)as an example of three elements transfer path graph,and the application of clustering analysis,clustering three elements to three main beam path and stack. |
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