Research On The Polygenetic Compound Metallogenic Model And Spatial Information Metallogenic Prognosis Of The Deposit-Accumulated Area In Laojunshan, Yunnan

Laojunshan tin-zinc polymetallic ore-concentrating district, located in one of the mostest deposit-accumulated areas of Southeast Yunnan, is famous for its indium first reserves and one of the three tin producing base in China.Guided by the Diwa theories of Polygenetic and compound progressive metallogenic and metallogenic prognosis, starting with the geological geochemical features of the different type ore deposit (body) in Laojunshan deposit-accumulated area, reconstruction of protoliths of metamorphic rocks, metallogensis and extracting of remote sensing information etc, combined with the regional mineralization background,which summarized mineralization geological condition, trace elements distribution, characteristics of structural evolution, metallogenetic sequence and metallogenic evolution model. It has researched of the Ore-controlling mechanism, remote sensing interpretation, the geological modeling and spatial information metallogenic prognosis by adopting Polygenetic and compound progressive metallogenic theory, the mulfractal theory, the BP neural network evaluation model and spatial information metallogenic prognosis model etc. The main research findings and new viewpoints achieved in this paper are as follows:1. Through the ananlysis on the reveal features of the structure pattern , trackways and its suited structures, the author has fully and systematically explained the development and evolution features of the stress field and structure system in different stages, pointing out the importance of the Laojunshan vault,Wenshan-Malipo fracture and Maguan-Dulong fracture control over mineralization.2. By using multifractal method, 17 microelements have been studied on the base of factor analysis and factor analysis, according to the fractal dimension (b₂),trace elements has been divided into three classes.Class I which are composed by As,Co,Bi. is less than 2. Classâ…¡which are composed by Sn,Ag,Zn,Pb,Cu,W.is between 2 and 3. Classâ…¢which are composed by Mn,Sb,Mo,Ti,V,Ni,Cr,Hg.is more than 3. Then the author has pointed out existing polyphase metallogenic movements from the estimation about inflection and discontinuity of the fractal curve in the ore field.3.On the basis of detailed study on syngenetic and subsequent characteristics of the mineral deposits and compareing with the similar type mineral deposits at home and abroad, The author has divided those mineral deposits in Laojunshan polymetallic ore-concentrating district into three deposit metallogenic types, according to the spatiotemporal distribution characteristics of the metallogenic evolution and dominant mineralization, i.e. Exhalative sedimentation-metamorphism alteration-magmatic hydrothermal superimposition and enrichment types, magmatic hydrothermal superimposition and alteration types of late Yanshan epoch, epithermal vein type in medium to temperature types of late Yanshan epoch, which breaking through the viewpoint of traditional "the monism of granite forming tin deposit", broadening the thought of ore prospecting.4. Starting with the study of deposit metallogenic series and according to the ore genesis and characteristic of mineral assemblage, the author has also reclassified all the deposits in Laojunshan ore-concentrating district into seven genetic types which associated with deposit metallogenic types, i.e. layered Cassiterite-sulfide deposit, layered Tin polymetallic ore deposit, layered tungsten polymetallic ore deposit, tin tungsten deposit of flesite vein, tin tungsten deposit of flesite, tin tungsten deposit of quartz vein and layered zinc-lead deposit. Furthermore, the author has detailedly studied the metallogenic conditions, mineralization, ore-controlling regularity and geochemical characteristics of all types of the deposits.5. Contacting the mineralization with the crustobody geotectonic evolution-movement features, the author has studied in detail the characteristics of multistage geotectonic evolution, multifactor mineralization control, multisource ore-forming materials, multicycle and multimode mineralization, set up a integrated Polygenetic compound mineralization evolution mode of the deposits, and studied its intrinsic mechanism of the mineral deposit Polygenetic compound mineralization.6.By using remote sensing information extraction method, remote sensing linear information and mineralization information have been studied. Further, remote sensing information ore-forming rule and fractal features of structures and alteration of Laojunshan deposit-accumulated area have been analyzed. After comparing with other ore fields, the author has guessed its fractal structure is inclined to complicated, activities is inclined to strong and ore-forming rule is more complicated.7.The space analysis method and the information statistics unit method have been used to research the model establishment of the spatial information metallogenic prognosis in Laojunshan deposit-accumulated area, which can compute the mineralization beneficial degree of each grid information unit, according to the mineralization beneficial degree, taking 0.7,0.6 and 0.5 as the dividing point of abnormity, the prospecting target has been divided the level of A,BandC. Level A units are relatively advantageous to the mineralization condition, are the obvious prospecting criteria and have the potential of seeking the large-scale polymetallic deposits; Level B units are the better prospecting criteria and have the potential of seeking the medium polymetallic deposits; Level C units are the poor prospecting criteria and have the potential of seeking small polymetallic deposits.8.In order to confirm the validity in the model of the spatial information metallogenic prognosis,the paper imports the BP artificial neural network to evaluate the mineralization beneficial area, The model inversion result discovers the studied effect of the neural network is good, the output value and the expectation value satisfies the evaluation requirement, which indicated it is feasible using the BP neural network to evaluate the mineralization beneficial area, thus also explained the computed mineralization beneficial degree is correct in the model of the spatial information metallogenic prognosis, the division of the prospecting units is reasonable.