Research On Application Mode Of Copper Deposit Geostatistical Model Construction

Kriging estimation techniques, from the design phase for the mining of ore block grade estimate accurately. The problem was first proposed by the South African mining engineer DG Krige. It made that according to inside and outside of the ore block to be estimated in different blocks of ore samples, the average grade of ore optimal fitting with the estimated. French mathematician Matheron (1963) gave Krige's theory a further systematic research, so that it found the formation of geostatistics theory. After several decades of research and development, geostatistics has been gradually formed a relatively complete set of theories and methods of work used in mining, geology, geophysics, geochemistry, hydrogeology, environmental geology, meteorology and agriculture and other fields. At present the international mining industry has build Geostatistical Kriging estimation method as the standard method of solid mineral resources estimation.Geostatistics, the introduction of China's mining industry and research for almost 30 years, but it was reported that currently the estimation method submitted by geostatistics solid mineral geological survey/exploration resource reserves are only a dozen copies of, it can be said that geostatistical resource/reserves estimation and evaluation methods have not yet widely application in China geology and mineral exploration and mining industry. But with the global mining development, applying geostatistical evaluation methods in geological mineral resources estimation has become a trend. The current foreign mining market only confirm the mineral resource/reserve reports that evaluated by kriging estimation method estimates, so that if do not use geostatistics method, geological exploration results and reserves of assets can not be recognized by international peers, which for our international mining cooperation is very negative. However, geostatistics reserves estimation method in geological exploration and mining industry has been unable to obtain a wide range of applications, the main reasons are as following:(1) The traditional method is still the primary means of estimating reserves in China geology mineral resources, geostatistics should be general accepted by geology mineral exploration researchers also need a process; The geological block segment, profiling method, etc. (brief "traditional method") reserves estimation method of solid mineral resources are still primary methords for reserves estimation. Traditional methods due to strong operational, the application simple and easy calculation, after nearly 50 years of application has formed a relatively completive system of practices and theories. Furthermore, traditional methods in pre-investigation, survey and other exploration and other extent of the lower stage has its unique advantages. Therefore, geostatistics as a new resource/reserves estimation and evaluation methods be generally accepted by geology and mineral resources need to be further process.(2) Have not form a complete set of geostatistical mineral reserve estimation applications. A very important reason why geostatistics of geology mineral reserves is so difficult to popularized in China's geology mineral reserves estimation is that:①geology researchers are very unsure about the Kriging method choice;②The confirms of parameters in model construction are really doubt. Have no standard refence criteria;③The selection of parameters are inexperienced to follow; the estimation results made by a different parameter setting, valuation methods or constraints boundary set are often have a big difference. Although there are many scholars from various angles to discuss the factors that affect accuracy in the application of geostatistics. Discussed from both a theoretical support for data distribution and sample the impact on the valuation accuracy. Journel and Pitard detailed study the reasons that creates the nugget values in the operation and its the theoretical basis. Andre (1996) discuss the nugget on dual kriging estimation accuracy.However, there's still has no a completly set of application preject for geostatistical mineral reserve estimation. Particularly in original data pretreatment to makesure the estimation precision, how to choose a appropriative kriging evaluation method by grade distribution and deposit type, how to obtain theoretics variance by construct analysis are still need to further discuss.(3) Have not a detailed research on application mode of kriging estimation methods in a specify types of deposits; Although the methods and theory of geostatistics has a great breakthrough, there is not a common set of geological deposits statistical modeling guidance methods in the geology mineral exploration and absent a certain category-specific statistical model of the geological deposits to analysis and research; Due to certain specific types of deposits, how to choose a suitable kriging method to analysis variogram, spatial grade distribution model to obtain ore reserves and resources, different valuation methods and model accuracy of the relationship between the formation of deposits for a certain type of deposit types and mineralization Geostatistical between the factors relational model for such problems are yet to be further studied.The geostatistical resource reserves assessment method has been unable to be widely applied in China, mainly because there is no formation of a complete set of applications for the geology mneral resources researchers to reference when estimating reserves. Therefore, this article supported by China Geological Survey Development Research Center "Solid mineral resource/ resverse estimation subsystem module development from 2006 to 2008," national crisis prospecting mining resources project management office:"New technologies and new methods" subject:" GIS system development on crisis in mine exploration project report on the outcome of the preparation 2008 ""the improve subject on crisis of mining exploration project in reporting the outcome by GIS system 2009 ", etc combined with geostatistics theory, deposit modeling theory and three-dimensional geological modeling techniques used the actual copper deposit geology survey data, to research geostatistical model construction process on porphyry-type (code-named BYCu), skarn-type (code-named XKCu), marine volcanic-type (code-named CJCu) three different copper deposit model bed. From the data base construction process to discuss the geostatistical method mineral resources estimates in detail. Mainly foucs on how to select the appropriate regional variable by data distribution and ore genesis, how to construct adpetable theory variance fuction by experimental variogram fuction and how to choose the right Kriging valuation method of ore-body estimation by data characteristics, and finally summarizes an applicable geological modeling construction method on porphyry-type, skarn type, marine volcanic copper deposits and their application mode. Obtained results and knowledge are as follows:(1) This research conbined with the actual business processes of mineral exploration, based on existing deposit modeling techniques, gave a more sophisticated mineral deposit modeling technology programs for the digital geology survey processing. And Apply it to three exploration data of copper deposit the data to build the original database and the latter part of business processing. The specific process is as follows: Based on the exploration and catalog standard bilding geology mineral resources information database→Automatic delineate orebodies base on Industry index→profile the boundary line of geological body and ore body in the exploration section maps→build three-dimensional solid body of orebody and geological model→to create block model with the orebody solid model restrict→based on Geostatistics and other statistics to complete the regionalized variable theory, analysis, model fitting and block assignment;(2) Before geostatistics deposit modeling construct, the data pre-processing and sample conbined are very important. In the data preprocessing stage, the choice of regionalized variables, special high-grade processing and different types of sample handling need to pay enough attention. Based on the three different mineralized mining belt grade straight statistics, obtained basic characteristics of three experimental data:(a) BYCu mining areas:the main ore mineralization in the central phase, occur in the granodiorite porphyry and quartz diorite porphyry; (b) CJCu mine: The grade straight-square of I massive sulphide copper-zinc orebody distribution exists an obvious mixture distribution characteristics. The Cu ore grade within the range from 2% to 6% has secondary enrichment of the phenomenon; (c) XKCu mining: the three main mineralized zone showed a similar grade of straight-square distribution, and therefore modeling, geostatistics when dealing with them as a whole set.(3) sample distance, ultra-high grade, the proportion effect, and data with mixed distribution are the main factors that impact the stability of variogram in copper deposit data main. As the sampling distance grow and the smooth variation of the function has a gradual increase, but it also conceals the micro-grade ore body variation information. The the value of geostatistical variation can be a refence to judge the smoothness of the discrete function curve. The ultra-high grade is the most important factor to make experimental variogram steady declining. It's important to handle the ultra-grade before experimental variogram calculation, usually the ultra-grade can be replaced or delete by an setting-max grade. the proportion effect can also impact the experimental variogram to a fluctuate, raising the nugget, sill and estimate variance of which the most common effects of direct proportion.XKCu mining data are classified within four types of data as inner-contact zone mineralization, contact mineralization, wall rock mineralization and similar-layered copper pyrite, And than calculated the experimental variogram by azimuth angle 10°and dip 35°.It denotes that inner-contact zone mineralization, contact mineralization, wall rock mineralization have an obvious effects of direct proportion. Therefore process the logarithmic transformation to mining data to obtain a more stable experimental variogram curve so that it's conducive to the later model fitting.(4) Established three experimental variogram mining carried out geological interpretation and analysis, and on this basis, the use of theoretical models fit them. Access a few key understanding: (a) BYCu mine orebody grade mineralization centers relative to a characteristics of geometric anisotropy with nugget value of 0.032, sill of 0.246. Principal axis direction, which is the direction of mineralized phase change in contact zone of ore body, within a lag distance of 180m manifest a continuative mineralization, and then become a degree of cavitation effect is due to conclude with alternation between stone layers and the ore body. As the ore-forming rock back by a dome-shaped structure control, resulting in nearly horizontal ore body (sub-axis direction) of the variability increased somewhat less stable than the other two directions. The direction of the vertical axis represents the direction of the ore-bearing intrusive rock ore grade variability, variation function in the lag distance of 280m at the beginning mutations, so inferred that the average thickness of ore body in this direction is 280m; (b) CJCu mine Id grade mineralized zone showed changes in the characteristics of band anisotropy, the vertical axis of the base station is higher than the spindle and vertical axis, and also have the different variable-range. Ore body in the direction of incline has a more obvious tendency of the mineral phase sub-zone and thus leading to mutations function in the direction away from the lagged effect of holes became apparent, after 50m. At the same time due to the presence of mixed distribution, leading to mutations function of distance from the hysteresis curve after 100m upturned situation. Id mineralized zone in the direction of orientation and the direction it has isotropic characteristics. In the ore body thickness direction. Fitting the model of local base station values 3.442, higher than the spindle and sub-axis value of the base station. Fitting curve according to variable-range inferred average thickness of ore bodies in the 85m or so; (c) XKCu three mineralized mining data is presented to geometric anisotropy. The infection along the direction of the main shape development vein copper ore body or lens on carbonate rock and the acidic porphyry contact zone, According to variable-range axis inferred ore body, the average length of about 232m. At the same time along the carbonate rock tendencies (sub-axis) and direction (vertical axis) direction of the mineralized ore body continuity is better, have the same degree of variation;(5) when it valuate by kriging, the search ellipsoid on the settings have a certain impact to the accuracy of valuation. Search ellipsoid is also a valuation to determine the radius of the main parameters of the sample selection, the search ellipsoid with the variation of the radius of the general function of the tripartite setting of the variable-range line up. However, when fitting model with the experimental variogram fitting degree is not high, it is recommended as far as possible theoretical models and experimental variation of the curve fit the first few points, or similar, while narrow your search to ensure the search ellipsoid radius within the scope of variability of the sample grade consistent with the fitting model. At the same time need to be considered when searching in the sample space for parties up ore variability, particularly within the first few lags from the variation of the length of the data. If the data from the last few lag variability within a large band anisotropy was suggested by setting the tolerance extends only to search theory model of the tripartite upward valuation of the samples; the other hand, you can use the eight round way sample search.(6) Taking XKCu data as exsample, the author gave a series of comparative experiments to compare the estimation precision under different data variance, upper limit of grade and estimation methods. When the data that is not subject to the normal distribution does not obey log-normal distribution in variance, grade variance can be the upper limit of the method of valuation is based on choice. When the variance of the data to be estimated is greater than 1.0, if the data is greater than the upper limit of quality data on the mean +9 times the standard deviation, it is recommended that application of the Indicator Kriging; if the data quality +9 times the upper limit of less than the mean standard deviation, then the the proposal of using Ordinary Kriging; when the variance of the data to be estimated between 0.5-1.0, it is recommended the use of Ordinary Kriging; when the variance of the data to be estimated is less than 0.5, if the grade is less than the mean+5 times the upper limit of the standard deviation, it is recommended not remove the original data on the ultra-high grade with directing calculation, with the number of Ordinary Kriging was basically the same as the valuation of accuracy; if grade is greater than the mean+5 times the upper limit of the standard deviation, it is recommended to conduct special high-grade treatment; to the use of lognormal kriging valuation, if the data variance to be estimated is greater than 0.5, it is recommended to be assessed on the quality of data limitations is set to +2 times the standard deviation about the mean valuation; Applied above conclusions to BYCu and CJCu data, generally verified the universality of those conclusions in the copper deposit modeling and geostatistics reserves estimates.