Method for predicting mineralization of ion adsorption type medium and heavy rare earth ore

Abstract

The invention provides an ion adsorption type medium and heavy rare earth ore mineralization prediction method which comprises the following steps: (1) screening ion adsorption medium and heavy rare earth mineralization prediction indexes according to mineralization theory understanding; (2) calculating the weight of each prediction index by using an analytic hierarchy process; (3) on the basis of a group decision-making method, calculating an expert weight coefficient, and solving a comprehensive index weight; (4) constructing a membership function of each prediction index by adopting a fuzzy mathematics method, and carrying out membership calculation; and (5) integrating the weight and the membership degree of each prediction index, fitting and constructing an ion adsorption type medium and heavy rare earth mineralization prediction model, and performing mineralization prediction. According to the method, expert cognition is effectively simulated, errors caused by subjective judgment can be remarkably reduced, the reliability of a metallogenic prediction result is improved, compared with a traditional method, the success rate of finding ion adsorption type medium and heavy rare earth ore is greatly increased, and then the prospecting exploration efficiency can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of mineralization prediction, and in particular to a method for predicting the mineralization of ion-adsorption type medium and heavy rare earth ores. Background Art

[0002] Rare earth elements are a general term for 17 elements including lanthanides, yttrium and scandium. Due to their unique electronic structure and excellent physical and chemical properties, they are widely used in new materials such as polishing, permanent magnets and luminescence. According to the differences in physical and chemical properties and separation technology between rare earth elements, rare earth elements can be divided into three categories: light, medium and heavy. Among them, lanthanum, cerium, praseodymium and neodymium are light rare earth elements, samarium, europium and gadolinium are medium rare earth elements, and terbium, dysprosium, holmium, erbium, thulium, ytterbium, lutetium and yttrium are heavy rare earth elements.

[0003] Compared with light rare earth elements, medium and heavy rare earth elements are less abundant in the earth's crust, and their reserves account for only 1% of the world's rare earth reserves. At present, more than 90% of the world's medium and heavy rare earth resources come from ion-adsorption rare earth deposits in South China, so they have great strategic and economic value. Therefore, it is urgent to carry out mineralization prediction work on ion-adsorption medium and heavy rare earth deposits.

[0004] At present, the prediction of ion adsorption type mineralization is often based on the semi-quantitative mineralization prediction based on the spatial correlation between data and ore deposits, emphasizing the importance of multivariate data for mineralization prediction, lacking the full integration of expert knowledge and geological data. This leads to problems such as unsatisfactory prediction results and poor prediction accuracy. Therefore, the present invention proposes an ion adsorption type medium and heavy rare earth mineralization prediction method based on group decision fuzzy hierarchical analysis method to solve the above problems. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for predicting the mineralization of ion-adsorption type medium and heavy rare earth ores, which can well simulate the geological knowledge of experts, reduce or eliminate the errors caused by subjective judgment, significantly improve the reliability of mineralization prediction results, and thus improve the efficiency of searching for ion-adsorption type medium and heavy rare earth ores.

[0006] To achieve the above object, the present invention provides the following scheme: a method for predicting the mineralization of medium and heavy rare earth ores of ion adsorption type, comprising the following steps:

[0007] According to the knowledge of mineralization theory, the prediction index of ion adsorption type medium and heavy rare earth mineralization is screened out;

[0008] The weights of the various mineralization prediction indicators are calculated using the analytic hierarchy process;

[0009] Based on the mineralization prediction index and the calculated weight, the expert weight coefficient is calculated by using the group decision method, and the comprehensive index weight is calculated based on the hierarchical analysis method of group decision;

[0010] Using fuzzy mathematics method to construct the membership function of each mineralization prediction index and calculate it to obtain the membership;

[0011] A mineralization prediction model is constructed by combining the comprehensive index weight and the membership degree, and mineralization prediction is performed using the mineralization prediction model.

[0012] Optionally, the mineralization prediction indicators include structure, geochemistry, temperature, rainfall, elevation and slope.

[0013] Optionally, the weights of the various mineralization prediction indicators are calculated using the analytic hierarchy process, including:

[0014] A judgment matrix is ​​constructed using different prediction indicators and relative importance values ​​between the prediction indicators, and the judgment matrix is ​​assigned values ​​using the Saaty relative importance rank table, and the consistency ratio of the judgment matrix is ​​checked; wherein, when the consistency ratio is less than 0.1, the judgment matrix is ​​considered to be consistent and the actual weight is accepted;

[0015] The relative importance values ​​in the judgment matrix are calculated, and the relative importance values ​​are normalized to obtain the initial weights of the prediction indicators; the formula of the initial weights is:

[0016]

[0017] Among them, W i is the initial weight, a ij Represents the prediction index a i Relative to the predictor a j The relative importance value of n is the number of prediction indicators.

[0018] Optionally, based on the mineralization prediction index and the calculated weight, the expert weight coefficient is calculated using a group decision method, and the comprehensive index weight is calculated based on the hierarchical analysis method of group decision, including:

[0019] A plurality of experts make weight judgments on a plurality of the prediction indicators to obtain an expert weight average value of each prediction indicator, calculate the standard deviation of the expert weight average value, and then use the standard deviation to calculate the expert weight coefficient; the formula of the expert weight coefficient is:

[0020]

[0021] Among them, w 权 is the expert weight coefficient, is the average expert weight for each prediction indicator, W ij It represents the weight judgment of the i-th expert on the j-th prediction indicator, m is the number of experts, and n is the number of prediction indicators;

[0022] The expert weight coefficient is multiplied by the initial weight to obtain the comprehensive index weight under group decision making. The formula of the comprehensive index weight is:

[0023]

[0024] Optionally, the membership function of each of the mineralization prediction indicators is constructed and calculated using fuzzy mathematics to obtain the membership, including: determining the data threshold and membership function of the prediction indicator according to the data characteristics and geological significance of the prediction indicator, and constructing a membership matrix according to the data threshold and the membership function; wherein the membership function is a Sigmoidal function, and the formula of the Sigmoidal function is:

[0025]

[0026] Optionally, combining the comprehensive indicator weight and the degree of membership to construct a mineralization prediction model, and using the mineralization prediction model to perform mineralization prediction, includes:

[0027] The comprehensive index weight and the membership degree are multiplied to obtain a mineralization prediction model, and the mineralization intensity is calculated using the mineralization prediction model. The formula for the mineralization intensity is:

[0028]

[0029] Among them, E is the mineralization intensity, W is the weight vector, and G is the membership matrix of the reclassification;

[0030] Based on the mineralization intensity, on the ArcGIS and GeoDAS platforms, the concentration-area fractal model was used to determine different inflection point values ​​and mineralization intensity thresholds, and then the mineralization prediction prospect map was divided to obtain high mineralization intensity areas, medium mineralization intensity areas and low mineralization intensity areas, completing the delineation of different levels of prediction areas for ion adsorption-type medium and heavy rare earth mineralization.

[0031] The mineralization intensity values ​​and the areas corresponding to the mineralization intensity values ​​in the whole area were counted, and a scatter plot of the mineralization intensity values ​​and the corresponding areas in the whole area was drawn using the least squares formula and Excel. The lower limits corresponding to high, medium and low mineralization intensities were determined using the classification dimension. The least squares formula is:

[0032]

[0033] Among them, n i is the dividing point, D1 and D2 are the slopes of the corresponding intervals.

[0034] The present invention discloses the following technical effects by providing an ion adsorption type medium and heavy rare earth ore mineralization prediction method:

[0035] 1. The fuzzy hierarchical analysis method adopted in the present invention is a method that is mainly knowledge-driven and also takes data-driven into consideration. It is a fuzzy extension of the traditional hierarchical analysis method. By introducing expert weight coefficients, it simulates expert cognition well and reduces or eliminates errors caused by subjective judgment.

[0036] 2. Compared with traditional methods, the present invention can effectively indicate favorable mineralization areas and determine the intensity of mineralization, reducing the uncertainty brought by traditional methods, effectively narrowing the prediction range, and greatly improving the success rate of searching for ion adsorption-type medium and heavy rare earth ores.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the process flow of the fuzzy hierarchical analysis method provided by an embodiment of the present invention;

[0041] Figure 3 The mineralization intensity CA fractal graph and histogram provided by the embodiment of the present invention;

[0042] Figure 4 A prospect prediction map of ion-adsorption medium and heavy rare earth minerals in southern Jiangxi based on fuzzy analytic hierarchy process is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1-4 As shown, the present invention provides a method for predicting the mineralization of ion-adsorbed medium and heavy rare earth ores. The present invention takes the southern Jiangxi region as an example. The southern Jiangxi region is one of the important ion-adsorbed medium and heavy rare earth resource bases in my country. It has a large number of ion-adsorbed rare earth resources, and is rich in high-value-added medium and heavy rare earth elements such as terbium, dysprosium and lutetium. It is known as the "Rare Earth Kingdom". The present invention includes the following steps:

[0046] 1. Based on the theoretical knowledge of mineralization, the prediction indicators of ion adsorption type medium and heavy rare earth mineralization are screened and obtained. The mineralization prediction indicators include structure, geochemistry, temperature, rainfall, elevation and slope; among them, structure and geochemistry are endogenous geological effects, and temperature, rainfall, elevation and slope are exogenous geological effects.

[0047] 1.1 The present invention uses ArcGIS software as a platform for data integration and analysis, and integrates comprehensive geological information such as geology, climate and mineral resources in the study area as the original information for mineralization prediction. Among them:

[0048] Geological database: The geological database used this time is the 1:200,000 geological map of Ganzhou City, including information on strata, structures, and igneous rocks;

[0049] Mineral deposit database: A total of 95 ion adsorption type rare earth deposits (points) were collected in the study area, including longitude and latitude, reserves and distribution type information. Among them, there are 10 large (super large) deposits, 49 medium-sized deposits and 36 small deposits (points), including 65 light rare earth deposits and 30 medium and heavy rare earth deposits;

[0050] Geochemical data: The data used in this study are 1:200,000 stream sediment geochemical data. The sample data comes from the 1:200,000 scale stream sediment geochemical scanning program of the China Geological Survey.

[0051] Topographic data: The topographic data comes from the geospatial data cloud, mainly based on ASTER GDEM2 data, with a spatial resolution of 30m;

[0052] Climate data: mainly temperature and rainfall data, which come from the Earth Resources Data Cloud Platform and are all annual average data.

[0053] 1.2 Specific explanation of the prediction indicators using the southern part of Jiangxi as an example:

[0054] 1) The control of structure on ion-adsorption rare earth minerals

[0055] The study area is located at the intersection of the northern part of the eastern section of the Nanling East-West Complex Tectonic Belt and the western edge of the Wuyi Uplift Fold Belt. The overall regional structure is dominated by east-west and north-northeast structures, superimposed by northeast, northwest, and nearly north-south structures. These tectonic belts have experienced multiple periods of repeated activities, forming extensive and diverse complex structures. Ion adsorption-type medium and heavy rare earth deposits are related to fault structures. For example, the Zudong super-large heavy rare earth deposit has extensively developed fault structures, and the surrounding rocks on both sides of these faults have mostly undergone alkali metasomatism. Alkali metasomatism not only makes heavy rare earths relatively enriched, but also forms a large number of minerals such as feldspar and mica. The clay minerals formed after the weathering of these minerals adsorb heavy rare earths, thereby forming alkali metasomatized rocks rich in heavy rare earths.

[0056] This study constructed a buffer zone based on regional faults. The spatial correlation between each rare earth deposit in the study area and the regional large structure was statistically analyzed. The study found that the relationship between the deposit and the structure is relatively close in spatial position. Among the 95 ion adsorption type rare earth deposits, the deposits within 5km of the regional fault accounted for more than 60% of the total.

[0057] 2) Control of magmatic rocks on ion-adsorption rare earth minerals

[0058] Magmatic rocks have a significant control effect on ion adsorption type deposits. The types of ion adsorption type rare earth deposits are largely inherited from the distribution characteristics of the rare earth in the parent rock. Compared with the light rare earth mineralization parent rock, the ΣHREY content in the mineralization parent rock of medium and heavy rare earth deposits accounts for 55% to 85%, while ΣLREE is only about 15%. The parent rock rare earth distribution pattern shows an obvious seagull type, and the europium anomaly is obvious. In particular, there are significant differences in the content of Y in the parent rock. The Y content of the parent rock of medium and heavy rare earths is generally 80×10-6 to 200×10-6, while that of light rare earths is 15×10-6 to 50×10-6. The Y / La ratio in the water sediments around the ion adsorption type rare earth deposits was statistically analyzed, and it was found that the Y / La ratio of the water sediments around the rare earth ores of different distribution types was significantly different. The average Y / La ratio of medium and heavy rare earth ores was 1.3, while the average Y / La ratio of light rare earth deposits was about 0.68, which was nearly 2 times higher.

[0059] 3) The control effect of temperature and rainfall on ion-adsorption rare earth minerals

[0060] As a typical deposit with the characteristics of "endogenous and exogenous" mineralization, exogenous weathering is crucial to the formation and preservation of ion-adsorption rare earth deposits. During the weathering process, the minerals in the parent rock will decompose and release their own elements. At the same time, the clay minerals formed by weathering are important carriers of rare earth elements in ion-adsorption rare earth deposits.

[0061] Chemical weathering, which releases rare earth elements and forms adsorption carriers for rare earth elements, is significantly controlled by climate, especially rainfall. Studies on basalt weathering on Hainan Island show that under extreme rainfall conditions, the rare earth element content of the weathering crust is nearly 10 times higher than that of the parent rock. At the same time, air temperature also significantly controls the formation of the weathering crust. For every 10°C increase in temperature, the rate of chemical weathering increases by about 2 to 4 times.

[0062] The paleoclimate conditions in this area are conducive to the formation of weathering crust. During the paleoclimate period of the middle and late Pleistocene, the southern Jiangxi region had a warm and rainy subtropical climate, which was conducive to the formation of weathering crust. In the Holocene stage, especially in modern times, the climate belongs to the subtropical monsoon climate zone, with an annual rainfall of 1000-2000mm and an annual average temperature of about 20℃. The study area had a tropical-subtropical climate for a certain period of geological history, which was conducive to the formation of thick weathering crust. The temperature and rainfall of ion adsorption-type rare earth deposits in the area were counted. It was found that most rare earth deposits are distributed in areas with temperatures between 17 and 20℃ and rainfall of about 1600-2000mm.

[0063] 4) Control of elevation and slope on ion-adsorption rare earth ores

[0064] Topography has a significant impact on the erosion and preservation of ion-adsorption rare earth deposits. It is generally believed that ion-adsorption deposits are mainly distributed in hilly areas with an altitude of less than 550m and a mountain slope of less than 30°.

[0065] Elevation refers to the distance from a ground point along a plumb line to the geoid. It is the most basic attribute of topography and can directly reflect the state of the terrain. As the elevation increases, the temperature drops and the rainfall also changes. Therefore, elevation has an important influence on the development of weathering crust.

[0066] Slope is the angle between the tangent plane at a point on the surface and the horizontal plane. It is used to describe the degree of inclination of the ground in the vertical direction. It is affected by factors such as the hardness of the rock and the coarseness of the particles, forming slopes of different steepness. The greater the slope angle of the terrain, the faster the runoff velocity and the stronger the erosion of the weathering crust. In mid-latitude regions, the erosion rate caused by terrain undulations can reach up to 400m / Ma. At the same time, slope is also a key factor reflecting soil erosion. If the slope is too large, it is not conducive to the accumulation of weathering crust, which in turn affects rare earth mineralization. The elevation and slope distribution ranges of ion adsorption rare earth deposits in the study area were statistically analyzed, and it was found that most rare earth deposits are distributed between 200 and 300m in elevation and around 0° to 17° in slope.

[0067] 2. Use the analytic hierarchy process to calculate the weights of each of the mineralization prediction indicators. Specifically include:

[0068] 2.1 A judgment matrix is ​​constructed using different prediction indicators and the relative importance values ​​between the prediction indicators, and the judgment matrix is ​​assigned values ​​using the Saaty relative importance grade table, and the consistency ratio of the judgment matrix is ​​checked; wherein, when the consistency ratio is <0.1, the judgment matrix is ​​considered to be consistent and the actual weight is accepted.

[0069] 1) The formula of judgment matrix A is:

[0070]

[0071] Taking expert A as an example, the specific calculation results are shown in Table 1 and Table 2.

[0072] Table 1 Weights of the first-level indicator target layer - Expert A

[0073] First level indicator Geochemistry structure Topography climate Weight Geochemistry 1.00 6.00 3.00 7.00 0.58 structure 0.17 1.00 0.50 1.00 0.10 Topography 0.33 2.00 1.00 8.00 0.26 climate 0.14 1.00 0.13 1.00 0.06

[0074] Table 2 Comprehensive weights of the secondary indicator target layer - Expert A

[0075]

[0076]

[0077] 2) The formula for the consistency ratio is:

[0078]

[0079] Among them, s i is the sum of the columns in the judgment matrix, λ max is the maximum characteristic root, CR is the consistency ratio, CI is the general index, and RI is the random consistency index (as shown in Table 3).

[0080] Table 3. Consistency index RI corresponding to different matrix orders

[0081] n 1 2 3 4 5 6 7 8 9 10 RI 0.00 0.00 0.58 0.90 1.12 1.24 0.00 0.00 0.58 0.90

[0082] 3) The scale of the hierarchical tree uses the Saaty relative importance scale (as shown in Table 4) to assign values ​​to the judgment matrix. The larger the scale, the more important the former is than the latter. For example, a scale of 1 means that both are equally important, and a scale of 9 means that the former is extremely important relative to the latter.

[0083] Table 4 Saaty relative importance level (1 to 9 scale)

[0084] Scale meaning 1 Equally important 3 Slightly important 5 Clearly important 7 Strongly important 9 Extremely important 2，4，6，8 The median of the above two adjacent judgments reciprocal If the scale of A compared to B is 3, then B is 1 / 3 compared to A.

[0085] 2.2 Calculate the relative importance values ​​in the judgment matrix, normalize the relative importance values, and obtain the initial weights of each prediction indicator; the formula for the initial weights is:

[0086]

[0087] Among them, W i is the initial weight, a ij Represents the prediction index a i Relative to the predictor a j The relative importance value of n is the number of prediction indicators.

[0088] 3. Based on the mineralization prediction index and the calculated weight, the expert weight coefficient is calculated using the group decision method, and the comprehensive index weight is calculated based on the hierarchical analysis method of group decision. Specifically including:

[0089] 3.1 Multiple experts make weight judgments on multiple prediction indicators to obtain the expert weight average of each prediction indicator, calculate the standard deviation of the expert weight average, and then use the standard deviation to calculate the expert weight coefficient; the formula of the expert weight coefficient is:

[0090]

[0091] Among them, w 权 is the expert weight coefficient, is the average expert weight for each prediction indicator, W ij It represents the weight judgment of the i-th expert on the j-th prediction indicator, m is the number of experts, and n is the number of prediction indicators.

[0092] 3.2 Multiply the expert weight coefficient and the initial weight to obtain the comprehensive index weight under group decision-making. The formula of the comprehensive index weight is:

[0093]

[0094] like Figure 2 As shown, the hierarchical structure of the group decision-making hierarchical analysis method is constructed, and the hierarchical structure of the group decision-making hierarchical analysis method is a hierarchical tree, which includes four first-level prediction indicators consisting of geochemistry, structure, topography and climate, and six second-level prediction indicators consisting of Y / La ratio, structural buffer zone, slope, elevation, rainfall and intensity.

[0095] With a i and a j Represents different prediction indicators, a ij Indicates a i Relative to a j The relative importance value of aij Construct the judgment matrix A, introduce the concept of standard deviation, and use the standard deviation σ i The size of the weight is used to measure the degree of deviation from the average level. The greater the expert deviation level, the greater the standard deviation σ ii The larger the expert weight coefficient w is, the i The smaller the value, the higher the deviation. The deviation degree and weight parameters of the 29 experts are shown in Table 5.

[0096] Table 5 Group decision-making expert weight coefficient and deviation degree

[0097] expert Expert weight coefficient Degree of deviation expert Expert weight coefficient Degree of deviation Expert 1 0.05 0.11 Expert 16 0.02 0.23 Expert 2 0.03 0.21 Expert 17 0.03 0.16 Expert 3 0.04 0.14 Expert 18 0.02 0.25 Expert 4 0.03 0.17 Expert 19 0.04 0.14 Expert 5 0.03 0.16 Expert 20 0.03 0.18 Expert 6 0.05 0.11 Expert 21 0.04 0.15 Expert 7 0.05 0.12 Expert 22 0.02 0.32 Expert 8 0.06 0.09 Expert 23 0.02 0.24 Expert 9 0.04 0.13 Expert 24 0.01 0.62 Expert 10 0.04 0.14 Expert 25 0.05 0.12 Expert 11 0.03 0.17 Expert 26 0.05 0.12 Expert 12 0.04 0.14 Expert 27 0.03 0.16 Expert 13 0.03 0.16 Expert 28 0.03 0.19 Expert 14 0.02 0.25 Expert 29 0.03 0.16 Expert 15 0.04 0.14

[0098] 4. Use fuzzy mathematics to construct the membership function of each mineralization prediction index and calculate it to obtain the membership.

[0099] The data threshold and membership function of the prediction index are determined according to the data characteristics and geological significance of the prediction index, and a membership matrix is ​​constructed according to the data threshold and the membership function; wherein the membership function is a Sigmoidal function, and the formula of the Sigmoidal function is:

[0100]

[0101] Among them, x is the specific value of the grid function, and the Max and Min memberships are the values ​​corresponding to the most favorable (membership is 1) and the most unfavorable (membership is 0) values ​​for mineralization, respectively.

[0102] The values ​​of weights and membership of each evidence layer are shown in Table 6.

[0103]

[0104] Since ion adsorption type medium and heavy rare earth deposits are only found in granite weathering crust. Therefore, the present invention takes the granite in southern Jiangxi as an example, and uses the granite in southern Jiangxi as the basis for prediction unit grid division, eliminating other interference factors and improving the accuracy of mineralization prediction. The six evidence layers are gridded with 1000*1000m based on the granite geological body, and the whole area is automatically divided into 10 categories according to the grid size, and then the membership function is used for reclassification, and the membership is assigned to each prediction indicator of each grid unit, and the membership matrix G is constructed. ij is the membership degree corresponding to the j index in the i-th grid. The formula of the membership matrix G is:

[0105]

[0106] 5. Combine the comprehensive index weights and the membership degree to construct a mineralization prediction model (for prediction elements and results, refer to Table 7), and use the mineralization prediction model to perform mineralization prediction.

[0107] Table 7 Comprehensive information prediction of ion-adsorption medium and heavy rare earth ores in southern Jiangxi

[0108]

[0109]

[0110] The prediction process specifically includes:

[0111] 5.1 The comprehensive index weight and the membership degree are multiplied to obtain a mineralization prediction model, and the mineralization intensity is calculated using the mineralization prediction model. The formula for the mineralization intensity is:

[0112]

[0113] Among them, E is the mineralization intensity, W is the weight vector, and G is the reclassified membership matrix.

[0114] 5.2 As Figure 3-4 As shown, based on the mineralization intensity, on the ArcGIS and GeoDAS platforms, the concentration-area fractal model is used to determine different inflection point values ​​and mineralization intensity thresholds, and then the mineralization prediction prospect map is divided to obtain high mineralization intensity areas, medium mineralization intensity areas and low mineralization intensity areas, completing the delineation of the predicted area for ion adsorption-type medium and heavy rare earth mineralization.

[0115] The methods for defining prediction areas of different levels are as follows:

[0116] The mineralization intensity value (nValue) and the corresponding area (nArea) of the whole area are counted, and a scatter plot of ln(nArea) and ln(nValue) is drawn using Excel, and the least squares formula is used:

[0117]

[0118] Where: n i is the dividing point; D1 and D2 are the slopes of the corresponding intervals, i.e., fractal dimensions. D1, D2, D3… are multiple fractal dimensions, and the lower limit values ​​corresponding to high, medium and low mineralization intensities are determined using the fractal dimensions.

[0119] like Figure 4As shown in the figure, taking the southern part of Jiangxi as an example, the study has delineated 5 A-level prospective areas and 3 B-level prospective areas. The A-level prospective areas are mainly concentrated in the southern and central parts of the study area, and the B-level prospective areas are concentrated in the northern part of the study area. Because ion-adsorbed medium and heavy rare earths are key strategic minerals in the country, this article only lists the approximate scope of the prospective areas. Take part of the A2 prospective area as an example. This prospective area has high mineralization intensity and a large area. The favorable area is well matched with the distribution range of known ore deposits (points). There are a large number of high-potential areas, and they are distributed in a planar shape. The middle and southern parts of the rock mass have good mineralization potential and great prospecting prospects. The recent exploration work of the author's unit also shows that there are large and above ion-adsorbed medium and heavy rare earth mines in this area.

[0120] Analyzing and integrating multivariate geological information is a key step to improve the mineralization rate of mineral exploration. This paper analyzes and integrates multivariate geological data such as geology, climate and geochemistry in southern Jiangxi, constructs an ion adsorption type medium and heavy rare earth mineralization prediction model based on fuzzy hierarchical analysis method, and performs mineralization prediction:

[0121] (1) The Y / La geochemical ratio of stream sediments can effectively distinguish light rare earth deposits from medium and heavy rare earth deposits. The average Y / La ratio of light rare earth deposits is similar to that of southern Jiangxi, with an average value of 0.7; while the average Y / La ratio of medium and heavy rare earth deposits is 1.3, nearly twice the average Y / La ratio of light rare earth deposits. The Y / La ratio may be a potential geochemical exploration indicator for finding medium and heavy rare earth deposits with ion adsorption.

[0122] (2) By using the analytic hierarchy process and introducing expert weight parameters, the subjectivity of weight selection can be reduced, making the weight distribution more consistent with the opinions of the majority of experts. There are six forecasting indicators, and their importance from large to small is Y / La ratio, slope, elevation, rainfall, temperature and structure.

[0123] (3) The prediction results show that the distribution pattern of ion-adsorbed medium and heavy rare earth resources in southern Jiangxi is "strong in the south, good in the middle, and weak in the east and west". Combined with the large and super-large deposits discovered in southern Jiangxi in recent years, it shows that there is still great potential for the search for ion-adsorbed medium and heavy rare earth ores in southern Jiangxi.

[0124] Therefore, the present invention provides a method for predicting the mineralization of ion-adsorption-type medium and heavy rare earth ores, which can well simulate the geological knowledge of experts, reduce or eliminate the errors caused by subjective judgment, and significantly improve the reliability of mineralization prediction results, improve the mineralization rate of mineral exploration, and thus improve the efficiency of searching for ion-adsorption-type medium and heavy rare earth ores.

[0125] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0126] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting the mineralization of medium and heavy rare earth ores of ion adsorption type, characterized in that: The following steps are involved: According to the knowledge of mineralization theory, the prediction index of ion adsorption type medium and heavy rare earth mineralization is screened out; The weights of the various mineralization prediction indicators are calculated using the analytic hierarchy process; Based on the mineralization prediction index and the calculated weight, the expert weight coefficient is calculated by using the group decision method, and the comprehensive index weight is calculated based on the hierarchical analysis method of group decision; Using fuzzy mathematics method to construct the membership function of each mineralization prediction index and calculate it to obtain the membership; A mineralization prediction model is constructed by combining the comprehensive index weight and the membership degree, and mineralization prediction is performed using the mineralization prediction model.

2. The method for predicting the formation of ion-adsorption medium and heavy rare earth ores according to claim 1, characterized in that: The mineralization prediction indicators include structure, geochemistry, temperature, rainfall, elevation and slope.

3. The method for predicting the formation of ion-adsorption medium and heavy rare earth ores according to claim 2, characterized in that: The weights of various mineralization prediction indicators are calculated using the hierarchical analysis method, including: A judgment matrix is ​​constructed using different prediction indicators and relative importance values ​​between the prediction indicators, and the judgment matrix is ​​assigned values ​​using the Saaty relative importance rank table, and the consistency ratio of the judgment matrix is ​​checked; wherein, when the consistency ratio is less than 0.1, the judgment matrix is ​​considered to be consistent and the actual weight is accepted; The relative importance values ​​in the judgment matrix are calculated, and the relative importance values ​​are normalized to obtain the initial weights of the prediction indicators; the formula of the initial weights is: Among them, W i is the initial weight, a ij Represents the prediction index a i Relative to the predictor a j The relative importance value of n is the number of prediction indicators.

4. The ion adsorption type medium and heavy rare earth ore mineralization prediction method according to claim 3, characterized in that: Based on the mineralization prediction index and the calculated weight, the expert weight coefficient is calculated using the group decision method, and the comprehensive index weight is calculated based on the hierarchical analysis method of group decision, including: A plurality of experts make weight judgments on a plurality of the prediction indicators to obtain an expert weight average value of each prediction indicator, calculate the standard deviation of the expert weight average value, and then use the standard deviation to calculate the expert weight coefficient; the formula of the expert weight coefficient is: Among them, w 权 is the expert weight coefficient, is the average expert weight for each prediction indicator, W ij It represents the weight judgment of the i-th expert on the j-th prediction indicator, m is the number of experts, and n is the number of prediction indicators; The expert weight coefficient is multiplied by the initial weight to obtain the comprehensive index weight under group decision making. The formula of the comprehensive index weight is:

5. The method for predicting the formation of ion-adsorption medium and heavy rare earth ores according to claim 4, characterized in that: The membership function of each of the mineralization prediction indicators is constructed and calculated by using fuzzy mathematics to obtain the membership, including: determining the data threshold and the membership function of the prediction indicator according to the data characteristics and geological significance of the prediction indicator, and constructing a membership matrix according to the data threshold and the membership function; wherein the membership function is a Sigmoidal function, and the formula of the Sigmoidal function is: Among them, x is a specific value, Max and Min memberships are the most favorable and most unfavorable values ​​for mineralization, respectively, and μ(x) is the membership. A membership matrix is ​​constructed through multiple memberships.

6. The method for predicting the formation of ion-adsorption medium and heavy rare earth ores according to claim 5, characterized in that: Combining the comprehensive index weight and the membership degree, constructing a mineralization prediction model, and using the mineralization prediction model to perform mineralization prediction, including: The comprehensive index weight and the membership degree are multiplied to obtain a mineralization prediction model, and the mineralization intensity is calculated using the mineralization prediction model. The formula for the mineralization intensity is: Among them, E is the mineralization intensity, W is the weight vector, and G is the membership matrix of the reclassification; Based on the mineralization intensity, on ArcGIS and GeoDAS platforms, the concentration-area fractal model is used to determine different inflection point values ​​and mineralization intensity thresholds, and then the mineralization prediction prospect map is divided to obtain high mineralization intensity areas, medium mineralization intensity areas and low mineralization intensity areas, completing the delineation of different levels of prediction areas for ion adsorption-type medium and heavy rare earth mineralization; The mineralization intensity values ​​and the areas corresponding to the mineralization intensity values ​​in the whole area were counted, and a scatter plot of the mineralization intensity values ​​and the corresponding areas in the whole area was drawn using the least squares formula and Excel. The lower limits corresponding to high, medium and low mineralization intensities were determined using the classification dimension. The least squares formula is: Among them, n i is the dividing point, D1 and D2 are the slopes of the corresponding intervals.

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