A three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies

Abstract

The present invention belongs to the technical field of uranium geology, and specifically relates to a three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies, which is suitable for the deep and peripheral mineralization prediction research of sandstone-type uranium deposits in a three-dimensional environment. The present invention includes the following steps: step 1, data collection and collation; step 2, data preprocessing; step 3, construction of a three-dimensional model of the ore deposit geological body and prediction factors; step 4, determination of the three-dimensional mineralization prediction calculation principle; step 5, calculation of the weight value of the ore deposit prediction factor; step 6, comparative analysis of the posterior probability value; step 7, delineation of the mineralization prediction target area. The present invention provides a theoretical basis and technical support for determining the prospecting target and prospecting direction, and strives to achieve new breakthroughs in prospecting.

Description

Technical Field

[0001] The invention belongs to the technical field of uranium geology, and specifically relates to a three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies, which is suitable for deep and peripheral mineralization prediction research of sandstone-type uranium deposits in a three-dimensional environment. Background Art

[0002] The basic principle of ore deposit formation is the uneven distribution of ore-forming elements in the earth (or crust), so deep ore prospecting can also be transformed into geochemical problems. Geochemical exploration, as an important ore prospecting method and a means of obtaining direct ore prospecting information, can directly identify the regional distribution pattern and local concentration center of ore-forming materials, indicating the possible location of ore deposits.

[0003] Most of the sandstone-type uranium deposits belong to exogenous post-hydrogenous uranium deposits. According to the relevant theories of hydrogenous uranium deposits, the formation mechanism of sandstone-type uranium deposits can be summarized as follows: long-term stable uranium-containing oxygen-containing water forms good infiltration and migration in the aquifer, and through the oxidation-reduction reaction with the reduced components in the permeable layer, on the one hand, the oxidation zone continues to advance toward the interior of the basin, and on the other hand, it promotes the continuous activation, migration, and enrichment of uranium, and finally accumulates near suitable geochemical barriers to form roll-shaped, layer-like or lens-shaped uranium ore bodies. Therefore, in actual prospecting work, the abnormal distribution area of ​​groundwater uranium is often used as one of the core signs of geochemical prospecting methods.

[0004] Since the 1990s, my country has listed in-situ leaching sandstone uranium deposits as the main direction of prospecting, and has discovered a number of large and super-large sandstone uranium deposits in the Mesozoic and Cenozoic basins in the north. By the end of 2015, sandstone uranium deposits accounted for about 15% of the total number of proven uranium deposits, and the resource volume ranked first among the four types of uranium deposits. Due to its large reserves, easy development, and economical mining costs, it has become the most important uranium exploration target type in my country.

[0005] However, with the increasing difficulty of geological prospecting and the decreasing effectiveness of prospecting, prospecting and exploration work has entered the stage of "deep exploration and blind exploration". How to more effectively prospect has become one of the focal issues of common concern at home and abroad. With the substantial improvement of computer graphics analysis and processing technology, three-dimensional visualization technology and geological big data computing capabilities, three-dimensional mineralization prediction research based on computers and software platforms has become a highlight in the field of mineral resource prediction in recent years.

[0006] As one of the most commonly used models for mineral resource mineralization prediction, the evidence weight model is widely used by domestic and foreign scholars in multivariate information integration and spatial information decision support systems, and it also has a wide range of applications in my country.

[0007] It is under the above background that the present invention is an attempt at mineralization prediction based on the theoretical knowledge of uranium mineralization by combining traditional geochemical prospecting methods with three-dimensional mineralization prediction technology. Summary of the invention

[0008] The purpose of the present invention is to provide a three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies, to provide a theoretical basis and technical support for determining prospecting targets and directions, and to strive to achieve new prospecting breakthroughs.

[0009] The technical solution adopted by the present invention is:

[0010] A three-dimensional prospecting and prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies comprises the following steps: step 1, data collection and organization; step 2, data preprocessing; step 3, construction of a three-dimensional model of a ore deposit geological body and prediction factors; step 4, determination of a three-dimensional mineralization prediction calculation principle; step 5, calculation of weight values ​​of ore deposit prediction factors; step 6, comparative analysis of posterior probability values; and step 7, delineation of a target area for mineralization prediction.

[0011] In step 1, the data on the mobile phone include ore drilling data, sedimentary phase belt map, geochemical data, exploration line profile map, and ore geological map.

[0012] In step 2, the preprocessing is to create a drilling database, which includes a positioning table, a lithology table, sand body particle size, sand body thickness, sand body color, ore body burial depth, and groundwater uranium anomaly sample analysis table.

[0013] The step 3 specifically includes the following steps:

[0014] Step 3.1, construct a three-dimensional entity model, classify and extract the strata, lithology, structure and mineralization information of the mining area, and establish a three-dimensional entity model of the ore deposit geological body, oxidized sand body, fault, ore body, braided river channel and the abnormally increased uranium area in the water;

[0015] Step 3.2, establish a prospecting prediction model, extract mineralization factor information, and based on the existing knowledge of mineralization theory of ore deposits, use the mineralization factors of the lower segment of the Yaojia Formation of the Lower Cretaceous, interlayer oxidation zone, structure, braided river subfacies, and abnormally elevated uranium in water as mineralization prediction factors in the study area.

[0016] The step 4 specifically includes: weight value calculation, membership degree μ jt Calculation, fuzzy weight value calculation, posterior probability calculation.

[0017] The weight value calculation is as follows: the research object is divided into T three-dimensional cells, and the mineralization information and mineralization information in each cell are counted. In the mineralization information layer, there are D ore-bearing elements and D ore-free elements. In the mineralization information layer, there are B cells with mineralization prediction factors, and there are B cells without mineralization factors.

[0018] Each mineralization prediction factor has two weight values, positive and negative, denoted as W + and W - , the difference between the two is C = W + -W - , C represents the correlation between the prediction factor and the mineral deposit. If C>0, it means that the factor is conducive to mineralization, otherwise it is not conducive to mineralization.

[0019] The positive and negative weight values ​​are:

[0020]

[0021] In the above formula,

[0022]

[0023] The membership degree μ jt calculate:

[0024] If there are n evidence factors in the study area, first j The attribute values ​​of (j=1,2,3,…,n) are divided into m j interval class, let A j1 and And defined as follows:

[0025] A j1 = {x|μ jt (x) = 1}, A j2 = {x|μ jt (x)=0} (3)

[0026] (j=1,2,3,…,n; t=1,2,3,…,m j )

[0027] but

[0028] (j=1,2,3,…,n; t=1,2,3,…,m j ).

[0029] The fuzzy weight value calculation:

[0030] Evidence Factor A j The tth interval The fuzzy weight value of is:

[0031]

[0032] (j=1,2,3,…,n; t=1,2,3,…,m j ).

[0033] The posterior probability is calculated as:

[0034] For n evidence factors, the logarithmic representation of the probability ratio of any unit k in the study area to be a mineral-bearing unit,

[0035]

[0036] (j=1,2,3,…,n; t=1,2,3,…,m j ; k=1,2,3,…,T)

[0037] Where O(D) is the prior quality ratio of D, that is:

[0038]

[0039] The posterior probability P of cell k is,

[0040] In step 5, according to the established prospecting prediction model, the favorable mineralization element interval is added as an evidence layer to the fuzzy evidence weight layer, and the prior probability of each evidence layer and the positive and negative correlation and weight value of each evidence layer are calculated under the two conditions of abnormally increased uranium content and abnormally increased value without uranium.

[0041] In step 6, the posterior probability value distribution diagrams for the two cases of containing uranium anomaly and not containing uranium anomaly are calculated in the three-dimensional software respectively.

[0042] In step 7, the known ore body model is superimposed on the geological body model, the number of ore-bearing blocks in the geological body is counted, and a favorable mineralization area is delineated in combination with the spatial distribution.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) The present invention provides a three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies. In a three-dimensional visualization environment, according to the mineralization theory and mineralization characteristics of uranium deposits, the most typical uranium mineralization geochemical parameters in the study area are selected, and mathematical geology and computer analysis techniques are used to integrate basic geological data, geological information interpretation, sandstone-type uranium mineralization theory, mathematical geological calculation methods and other information to predict the mineralization of the deep and periphery of the known ore body, so as to achieve a more intuitive, clear and accurate display of the prospecting target area, and provide a theoretical basis for the deep and periphery prospecting direction;

[0045] (2) The present invention provides a three-dimensional prospecting and prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies. On the basis of existing mineralization theories and mineralization characteristics research, the present invention analyzes the abnormal uranium content values ​​in a large number of groundwater samples in the region, takes the abnormal area with U ≥ 8.06 μg / L as a special prediction factor, and calculates the distribution of posterior probability values ​​in three-dimensional mineralization prediction in two cases: containing uranium anomalies and not containing uranium anomalies. Through comparative analysis, it is found that when the uranium anomaly prediction factor is involved in mineralization prediction, the favorable mineralization range can be better constrained and the distribution of favorable mineralization belts can be more accurately reflected. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Flow chart of three-dimensional mineralization prediction technology solution. DETAILED DESCRIPTION

[0047] 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.

[0048] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0049] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] like Figure 1 As shown, the present invention provides a three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies, comprising the following steps:

[0051] Step 1: Data collection and organization. Classify and organize the useful information of the XX ore deposit drilling data, sedimentary phase belt map, geochemical data, exploration line profile map, ore deposit geological map and other data according to the needs of subsequent work.

[0052] Step 2: Data preprocessing

[0053] Create a drilling database. The database mainly includes positioning tables, lithology tables, sand body grain size, sand body thickness, sand body color, ore body burial depth, groundwater uranium anomaly sample analysis tables, etc., laying the foundation for subsequent model creation and data analysis and calculation.

[0054] Step 3: Construct a 3D model of the deposit geological body and predictive factors

[0055] Step 3.1: Take the XX uranium deposit as an example to build a three-dimensional solid model. Classify and extract the strata, lithology, structure, mineralization and other information of the XX mining area, and build a three-dimensional solid model of the deposit geological body, oxidized sand body, fault, ore body, braided river channel and the abnormally increased uranium in the water.

[0056] Step 3.2: Establish a prospecting prediction model and extract mineralization factor information. According to the existing mineralization theory of XX deposit, the lower segment of Yaojia Formation in Lower Cretaceous (K2y 1 ), interlayer oxidation zone (yellow oxidized sand body), structure, braided river subfacies, and areas with abnormally elevated uranium in water are used as mineralization prediction factors in the study area (Table 1).

[0057] Table 1 XX ore deposit prospecting prediction model

[0058]

[0059] Step 4: Determine the calculation principle of 3D mineralization prediction

[0060] (1) Weight value calculation

[0061] The research object is divided into T three-dimensional cells, and the mineralization information and mineralization information in each cell are counted. In the mineralization information layer, there are D ore-bearing elements and D ore-free elements. In the mineralization information layer, there are B cells with mineralization prediction factors, and there are B cells without mineralization factors.

[0062] Each mineralization prediction factor has two weight values, positive and negative, denoted as W + and W - , the difference between the two is C = W + -W - C represents the correlation between the prediction factor and the ore (point) deposit. If C>0, it means that the factor is conducive to mineralization, otherwise it is not conducive to mineralization.

[0063]

[0064] In the above formula,

[0065]

[0066] (2) Membership degree (μ jt )calculate

[0067] If there are n evidence factors in the study area, first j The attribute values ​​of (j=1,2,3,…,n) are divided into m j interval class, let A j1 and And defined as follows:

[0068] A j1 = {x|μ jt (x) = 1}, A j2 = {x|μ jt (x)=0} (3)

[0069] (j=1,2,3,…,n; t=1,2,3,…,m j )

[0070] but

[0071] (j=1,2,3,…,n; t=1,2,3,…,m j )

[0072] (3) Fuzzy weight value calculation

[0073] Evidence Factor A j The tth interval The fuzzy weight value of is:

[0074]

[0075] (j=1,2,3,…,n; t=1,2,3,…,m j )

[0076] (4) Calculation of posterior probability

[0077] For n evidence factors, the logarithm of the probability ratio of any unit k in the study area to be a mineral-bearing unit is expressed as:

[0078]

[0079] (j=1,2,3,…,n; t=1,2,3,…,m j ; k=1,2,3,…,T)

[0080] Where O(D) is the prior quality ratio of D, that is:

[0081]

[0082] The posterior probability P of cell k is:

[0083] Step 5: Calculation of weight values ​​of XX mineral deposit prediction factors

[0084] According to the established prospecting prediction model, the favorable mineralization element interval is added as the evidence layer to the fuzzy evidence weight layer, and the prior probability of each evidence layer and the positive and negative correlation and weight value of each evidence layer are calculated under the two conditions of abnormally increased uranium content and abnormally increased uranium content (Table 2 and Table 3).

[0085] Step 6: Comparative analysis of posterior probability values

[0086] The posterior probability value is the probability of each favorable mineralization element appearing simultaneously in a cell, reflecting the relative prospecting probability of each unit block. The higher the probability value, the higher the probability of block mineralization.

[0087] According to the above calculation principle, the distribution diagrams of the posterior probability values ​​in the two cases of uranium anomaly and uranium-free anomaly were calculated in the three-dimensional software. By comparison, it can be seen that after superimposing the influencing factors of groundwater uranium anomaly, the distribution area of ​​the posterior probability values ​​that are conducive to mineralization is more concentrated.

[0088] Table 2 Evidence weights of various ore-controlling factors under the condition of abnormally high uranium value in groundwater

[0089]

[0090]

[0091] Table 3 Evidence weights of various ore-controlling factors under the condition of no abnormal increase of uranium in groundwater

[0092] Evidence Items W+ S(W+) W- S(W-) C K2y1 Strata 0.554285 0.058708 -0.039 0.01793 0.593287 Braided channel subfacies 2.058071 0.028274 -0.42406 0.021715 2.482131 Slope belt 0.907458 0.032027 -0.21631 0.020317 1.123772 Alteration (yellow sand body) 1.344785 0.090435 -0.02747 0.017466 1.372253 (Light) grey medium fine sandstone 1.384503 0.027421 -0.39892 0.022039 1.783425 fracture 0.531839 0.045044 -0.06774 0.018545 0.599576

[0093] Step 7: Delineation of mineralization prediction target areas

[0094] The known ore body model is superimposed on the geological body model, and the number of ore-bearing blocks in the geological body is counted. According to calculations, 60% of the known ore bodies fall in cubic blocks with a posterior probability value ≥ 0.65. Combined with their spatial distribution, a favorable mineralization area is delineated.

[0095] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0096] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies, characterized by: The method comprises the following steps: step (1), data collection and collation; step (2), data preprocessing; step (3), construction of a three-dimensional model of the ore deposit geological body and prediction factors; step (4), determination of the three-dimensional mineralization prediction calculation principle; step (5), calculation of the weight value of the mineral deposit prediction factor; step (6), comparative analysis of the posterior probability value; step (7), delineation of the mineralization prediction target area; In step (1), the data on the mobile phone include ore drilling data, sedimentary facies maps, geochemical data, exploration line profiles, and ore geological maps; In the step (2), the preprocessing is to create a drilling database, which includes a positioning table, a lithology table, a sand body particle size, a sand body thickness, a sand body color, a ore body burial depth, and a groundwater uranium anomaly sample analysis table; The step (3) specifically includes the following steps: Step (3.1), construct a three-dimensional solid model, classify and extract the strata, lithology, structure, and mineralization information of the mining area, and establish a three-dimensional solid model of the ore deposit geological body, oxidized sand body, fault, ore body, braided river channel, and the area with abnormally increased uranium in the water; Step (3.2), establish a prospecting prediction model, extract the information of metallogenic elements, and according to the existing knowledge of metallogenic theory of ore deposits, take the metallogenic elements of the lower segment of the Yaojia Formation of the Lower Cretaceous, the interlayer oxidation zone, the structure, the braided river channel subfacies, and the abnormally elevated uranium in water as the metallogenic prediction factors in the study area; The step (4) specifically includes: weight value calculation, membership degree μ jt Calculation, fuzzy weight value calculation, posterior probability calculation; The weight value calculation is as follows: the research object is divided into T three-dimensional cells, and the mineralization information and mineralization information in each cell are counted. In the mineralization information layer, there are D ore-bearing elements and D ore-free elements. In the mineralization information layer, there are B cells with mineralization prediction factors, and there are B cells without mineralization factors. Each mineralization prediction factor has two weight values, positive and negative, denoted as W + and W - , the difference between the two is C = W + -W - , C represents the correlation between the prediction factor and the mineral deposit. If C>0, it means that the factor is conducive to mineralization, otherwise it is not conducive to mineralization; The positive and negative weight values ​​are: In the above formula, The membership degree μ jt calculate: If there are n evidence factors in the study area, first j The attribute values ​​of (j=1,2,3,…,n) are divided into m j interval class, let A j1 and A j2 ( A j1 ∩A j2 ), and defined as follows: A j1 ={x∣μ jt (x)=1},A j2 ={x∣μ jt (x)=0} (3) (j=1,2,3,…,n;t=1,2,3,…,m j ) but (j=1,2,3,…,n;t=1,2,3,…,m j )。 2. The three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies according to claim 1 is characterized by: The fuzzy weight value calculation: Evidence Factor A j The tth interval The fuzzy weight value of is: (j=1,2,3,…,n;t=1,2,3,…,m j )。 3. The three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies according to claim 1 is characterized by: The posterior probability is calculated as: For n evidence factors, the logarithmic representation of the probability ratio of any unit k in the study area to be a mineral-bearing unit, (j=1,2,3,…,n;t=1,2,3,…,m j ;k=1,2,3,…,T) Where O(D) is the prior quality ratio of D, that is: The posterior probability P of cell k is, 4. The three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies according to claim 1 is characterized in that: In the step (5), according to the established prospecting prediction model, the favorable mineralization element interval is added as an evidence layer to the fuzzy evidence weight layer, and the prior probability of each evidence layer and the positive and negative correlation and weight value of each evidence layer are calculated under the two conditions of abnormally increased uranium content and abnormally increased uranium content.

5. The three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomaly according to claim 3 is characterized by: In the step (6), the posterior probability value distribution diagrams for the two cases of containing uranium anomaly and not containing uranium anomaly are calculated in the three-dimensional software respectively.

6. The three-dimensional prospecting prediction method for sandstone-type uranium deposits based on groundwater uranium anomalies according to claim 5 is characterized by: In the step (7), the known ore body model is superimposed on the geological body model, the number of ore-bearing blocks in the geological body is counted, and a favorable mineralization area is delineated in combination with the spatial distribution.

Images