AU2020103098A4 - Ore prospecting prediction method of nonlinear discrete speculation model - Google Patents
Ore prospecting prediction method of nonlinear discrete speculation model Download PDFInfo
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Abstract
The present invention discloses an ore prospecting prediction method of a nonlinear discrete
speculation model, which determines an optimal combination number and an optimal shrinkage
distance of ore-controlling factors to solve the problems that the combination with many
ore-controlling factors is small in area coverage, the combination with few ore-controlling
factors is large in area coverage, too small shrinkage distance value causes the small number and
scattering distribution of finally delineated favorable mineralization areas, and too large
shrinkage distance causes the wide range of the delineated favorable mineralization area, losing
the ore prospecting prediction significance, thereby overcoming the information asymmetry of
mine areas and limitations of continuous interpolation models under the collective action of the
optimal ore-controlling factor combination number and the optimal shrinkage distance,
implementing the delineation of the favorable mineralization areas under the double constraints
of the optimal combination and the optimal shrinkage distance of the ore-controlling factors, and
achieving an ore prospecting prediction purpose.
Drawings of Description
Mineraizatin Minral GnesisOf the reah
tiesrh
IOre-controlling Or rspc eoia model Grid division Mdln
factormdl
Optimal combination number of ore
controlling factor
Ore prospecting
prediction method of
Bnysis othe nonlinear discrete
B fatal combination of speculation model
ore-controlling
factors
Optimals hrinktage- distance oif favorable minieralization units
Favorbleiealzto Shrinkage
area delineation
Fig. 1
Distance Number of block Volume of
interval units larger than blocks l-are
the distance than the
distance
y2,=a x+b2
-lo n.o- n.
d2 2n2 x Va
d3 S-va R= 0.9--
d4 nfx nxVa
dm na n.xvo
dm+1 fla r.-ntxvo
.m.,~44-,4-Log(D)
Fig. 2
1
Description
Drawings of Description
Mineraizatin Minral GnesisOf the reah tiesrh
Or rspc IOre-controlling eoia model Grid division Mdln factormdl
Optimal combination number of ore controlling factor Ore prospecting prediction method of Bnysis othe nonlinear discrete B fatal combination of speculation model ore-controlling factors
Optimalshrinktage- distance oif favorable minieralization units
Favorbleiealzto Shrinkage area delineation
Fig. 1
Distance Number of block Volume of interval units larger than blocks l-are the distance than the distance
y2,=a x+b2 -lo n.o- n.
d2 2n2 x Va
d3S-va R=0.9- d4 nfx nxVa
dm na n.xvo dm+1 fla r.-ntxvo .m.,~44-,4-Log(D)
Fig. 2
Description
Technical Field
The present invention belongs to the technical field of mineral exploration, and particularly relates to an ore prospecting prediction method of a nonlinear discrete speculation model.
Background
In the research of mineralization prediction, ore-controlling factors related to the mineralization are important factors that control the output and occurrence of ore bodies. An acting position of the ore-controlling factors (such as fault zones, folds, gravity anomalies and magnetic anomalies) has a guiding effect on the positioning and prediction of deep blind ore bodies, and is an important reference for delineating favorable mineralization areas in the research of the mineralization prediction. Areas with good ore-controlling conditions have a high probability for the output of ore bodies, and the areas with poor ore-controlling conditions have a low probability for the output of the ore bodies. For a known type of ore deposit, the formation position of the ore body is the result of the combined action of different ore-controlling factors. By analyzing a geological background of the research area and the genesis of the ore deposit, an ore prospecting model of the research area is established to determine the type of ore-controlling factors of the specific ore deposit. In a non-linear discrete speculation model, a three-dimensional geological entity model of different ore-controlling factors is established based on the existing data (what data is available and what model will be established) of the research area; block units are classified on the basis of the three-dimensional entity model; attributes are established for each block unit (the attribute value is 0 or 1, wherein 0 indicates that the block unit is not a certain ore-controlling factor, and 1 indicates that the block unit is a certain ore-controlling factor); and the number of ore-controlling variables (i.e., the combination number of ore-controlling variables) contained in each block unit is counted. Without considering the importance of the ore-controlling factors, the block unit containing many ore-controlling variables is considered as a position with good ore-forming conditions and with high possibility in discovering the ore body; and the block unit containing few ore-controlling variables is considered as the position with poor ore-forming conditions and
Description
with low possibility in discovering the ore body. After an optimal combination number of the ore-controlling variables is determined, the block units meeting the requirements of the optimal combination number of the ore-controlling variables are subjected to the block shrinkage at an appropriate distance, so that the block units that are originally discretely distributed in a certain space are aggregated into a larger block, and finally the aggregated block that is shrunk at the appropriate distance is selected as a favorable mineralization area delineated by the nonlinear discrete speculation model. For the choice of a shrinkage distance value, if the distance is too small, the finally delineated favorable mineralization areas is large in quantity and widely distributed, and if the distance is too large, the delineated favorable mineralization area is wide, losing the research significance of the ore prospecting prediction. In actual research, the area with the most favorable mineralization conditions in the entire three-dimensional research area is smaller than the entire research area, that is, a ratio of the number of the block units containing the maximal combination number of ore-controlling factors in the total number of the block units of the entire research area is small. If the favorable mineralization area is only delineated by the block units containing more ore-controlling factors, the final delineated range is small and the discrete degree is high, which cannot meet the requirements for the ore prospecting prediction accuracy. The difficulty to be solved by the present invention is to determine the optimal combination number and the optimal shrinkage distance of the ore-controlling factors through the fractal analysis between D (distance) and V (volume) so as to ensure that the delineated favorable mineralization area under the double constraints of the optimal combination number and optimal shrinkage distance of the ore-controlling factors meets the practice requirements of ore exploration.
Summary
For the above problems in the background, the present invention aims at providing an ore prospecting prediction method of a nonlinear discrete speculation model, which determines an optimal combination number and an optimal shrinkage distance of ore-controlling factors, thereby completing the delineation of a favorable mineralization area under the collective action of the optimal combination number and the optimal shrinkage distance of the ore-controlling factors, and achieving an ore prospecting prediction purpose. Therefore, the present invention adopts the following technical solution: the ore prospecting prediction method of the nonlinear discrete speculation model comprises the following steps:
Description
Step I: determining an optimal combination number of ore-controlling factors (1) block calculation: on the basis of analyzing the geological background and mineralization characteristics of a research area, summarizing a prospecting model of the research area, determining the type of ore-controlling factors, establishing a local three-dimensional geological entity model according to the existing geological data of the research area, and establishing a three-dimensional block model on the basis of grid division; selecting any block unit in the block model as a target block, creating a search ellipsoid by taking the center position of the target block as a spherical center, and initializing a radius value of the search ellipsoid by referring to the range of the research area. After the radius is set, counting a distance value from the block unit (a characteristic block) to the target block under any combination number of the ore-controlling factors respectively in the range of the search ellipsoid, and calculating a minimal distance value; (2) Distance-volume fractal fitting: taking the calculated minimal distance value from different target blocks to the characteristic blocks and a combination number of ore-controlling variables as independent variables, and sorting all block units under the same combination number of ore-controlling variables according to the corresponding minimal distance value; for the result sorted according to the distance, counting the number (N) of the block units contained beyond a distance value; because the size (volume) of the divided smallest block unit is a fixed value (Vo) when the research area is subjected to the grid division, the volume (V) of the block larger than a certain range is calculated according to the number and division size of the block unit. According to different combination conditions of the ore-controlling factors, analyzing a relationship between the distance and the volume of all blocks larger than the distance according to the formula 1 and formula 2 to fit self-similarity in various factor combinations and between factors.
V kxD d (formula 1)
In the formula, D is distance, V is volume of the block larger than the distance D, d is a number of fractal dimensions, and k is a proportional constant. Taking the logarithm of formula 1 to obtain formula 2:
1n V = hiK + d In D (formula 2)
Projecting data to a double-log coordinate system, and fitting the function y=a*x+b
Description
according to the minimum interclass variance, so that the number of fractal dimensions of the piecewise fitting is d=a. (3) Analyzing combinations of ore-controlling factors: According to the D-V fractal result, on the premise that the D-V satisfies the fractal distribution characteristics under the different combination numbers of the ore-controlling factors, according to the distribution range of the characteristic blocks in the research area, setting a maximal search distance (L) as the radius of a search sphere; based on this value, equally dividing the maximal search distance value L into n parts; and respectively establishing spheres with the radius r of L/n, 2L/n, 3L/n, 4L/n and the like, wherein the sphere with the minimal radius length is called a basic sphere, and volume is Vo; on this basis, establishing the sphere varied at an equal step (L/n) which is called the search sphere with the volume of Vi; respectively counting the number N of characteristic blocks containing the specific combination number of ore-controlling factors between the adjacent radii, and a volume increment of the adjacent search spheres; and taking the number of characteristic blocks containing a specific number of ore-controlling factors distributed in the unit volume as the ore-controlling density (formula 3) under the specific combination number of the ore-controlling factors, and taking a ratio of the ore-controlling density of different search ellipsoids to the ore-controlling density of the basic ellipsoid (formula 4) as the relative ore-controlling density (formula 5) AN v-V - -formula (3)
formula (4)
/ P AV V V formula (5) According to a calculation result of the relative ore-controlling density under different search scales, drawing a statistics curve of the relative ore-controlling density and search scales under different combination numbers of ore-controlling variables; Step II: determining an optimal shrinkage distance After the optimal combination number of ore-controlling factors is determined through the above research, all block units satisfying the optimal combination number are called favorable mineralization units in the prospecting prediction research; and in the prospecting prediction method of the nonlinear discrete speculation model, the optimal shrinkage distance of the favorable mineralization unit is determined through sliding windows of different scales.
Description
After the favorable mineralization unit is re-classified at the search windows of different scales, the block unit meeting the requirement of the optimal combination number of ore-controlling factors is called a favorable mineralization window. The relationship between the scale of different sliding search windows and the number of final favorable mineralization windows is counted; and on this basis, a sliding search scale corresponding to a peak value of the number of the favorable mineralization windows is selected as the optimal shrinkage distance value of the favorable mineralization unit under the optimal combination number of the ore-controlling factors; On the basis of determining the optimal combination number and optimal shrinkage distance of the ore-controlling variables, a favorable mineralization area delineated according to the ore prospecting prediction method of the nonlinear discrete speculation model is obtained. As a supplement and improvement to the above technical solution, the present invention also includes the following technical features: In the step I (1), calculating the minimal distance value is to calculate a minimal value of the distance from all characteristic blocks containing 1 ore-controlling factor in the range of the ellipsoid to the target block respectively, a minimal value of the distance from all characteristic blocks containing 2 ore-controlling factors to the target block, and a minimal value of the distance from all characteristic blocks containing 3 ore-controlling factors to the target block, and so on. In the step I (3), in the block model, when the ore-controlling condition is loose (the combination number of the ore-controlling variables is relatively small), a lot of block units in the entire research area meet the requirement of the combination number of the ore-controlling variables, a lot of block units satisfy the preconditions and are closely distributed, and the distance between the block units is relatively small, so that with the increase of the search radius, the relative ore-controlling density is reduced; and when the condition is strict (the combination number of the ore-controlling variables is large), for the large combination number of the specific ore-controlling variables, only a few block units in the entire research area meet the requirement of the combination number of the ore-controlling variables, few block units satisfy the precondition and are discretely distributed, and the distance between the block units is relatively large, so that with the increase of the search radius, the relative ore-controlling density increases; after the search scale increases to a certain scale, all block units satisfying the precondition are basically contained; and if the search scale continuously increases, the relative ore-controlling density decreases.
Description
The present invention can achieve the following beneficial effects: by determining the optimal combination number and the optimal shrinkage distance of ore-controlling factors, the following problems can be solved that the combination with many ore-controlling factors is small in area coverage, the combination with few ore-controlling factors is large in area coverage, the too small shrinkage distance causes the small number and scattering distribution of finally delineated favorable mineralization areas, and the too large shrinkage distance causes the wide range of the delineated favorable mineralization areas, losing the ore prediction significance, thereby overcoming the information asymmetry of mine areas and limitations of continuous interpolation models under the collective action of the optimal ore-controlling factor combination number and the optimal shrinkage distance, implementing the delineation of the mineralization favorable areas under the double constraints of the optimal combination and the optimal shrinkage distance of the ore-controlling factors, and achieving an ore prospecting prediction purpose.
Description of Drawings
Fig. 1 is a flow chart of an ore prospecting prediction method of the present invention. Fig. 2 is a schematic diagram of calculation and fitting of D (distance)-V (volume of a block larger than the distance D). Fig. 3 is a schematic diagram of a fractal analysis result of a D-V model under different combinations of ore-controlling factors of an ore deposit. Fig. 4 is a schematic diagram of analysis of an equal-interval search sphere. Fig. 5 is a schematic diagram of a statistics curve of relative ore-controlling density and search scales under different combination numbers of ore-controlling variables of an ore deposit. Fig. 6 is a schematic diagram of search of a sliding window. Fig. 7 is a schematic diagram of an optimal shrinkage distance corresponding to an optimal combination number of ore-controlling factors of the ore deposit. Fig. 8 is a schematic diagram of a favorable mineralization area of the ore deposit delineated by a method of the present invention and a know ore body.
Detailed Description
Specife embodiments of the present invention will be detailed in combination with the drawings.
Description
The main flow of an ore prospecting prediction method of a nonlinear discrete speculation model in the present invention is shown in Fig. 1. Determination of an optimal combination number of ore-controlling factors (1) Block calculation On the basis of analyzing a geological background and mineralization characteristics of a research area, an ore prospecting model of the research area is summarized, and the type of ore-controlling factors is determined. According to the existing data of the research area, a three-dimensional geological entity model (based on any available data) is established, and on the basis of the grid division, a three-dimensional block model of the research area is established. Any block unit in the block model is selected as a target block; a search ellipsoid is established by taking the center position of the target block as the spherical center; and a radius value of the search ellipsoid is initialized according to the range of the research area. After the radius is set, in the range of the search ellipsoid, a value of the distance from any characteristic block under different combination numbers of the ore-controlling factors to the target block is counted respectively, and a minimal value of the distance is calculated (that is, the minimal value of the distance from all characteristic blocks containing 1 ore-controlling factor in the range of the ellipsoid to the target block is calculated; the minimal value of the distance from all characteristic blocks containing 2 ore-controlling factors to the target block is calculated; the minimal value of the distance from all characteristic blocks containing 3 ore-controlling factors to the target block is calculated, and so on; and the minimal value of the distance from all characteristic blocks containing n ore-controlling factors to the target block is calculated). If there is no characteristic block satisfying the combination number of the ore-controlling factors in the current search range, the minimal distance is calculated at -1. According to this concept, other block units are successively selected as the target block to calculate the distance, and the calculation results are shown in Table 1. Table 1 Statistics of the minimal distance from the target block to the characteristic block
Description
Target block unit Cmbiatio numbr of vanable
Mmax Mmax-1 Mmax-2 M2 Mi No. Cete coordmate m M-1 m-2 2 1
1 X1, Y1 Zi d(n,1) d(m-1,1) d(m-2,1) d(2,1) d(l,1)
2 X2 Y2, Z2 d(m,2) d(n-1,1) d(m-2,2) d(2,2) d(1,2)
3 X3 Y3, Z3 d(m,3) d(m-1,1) d(m-2,3) d(2,3) d(1,3)
n-1 Xn-1 Yn-1, Zn-1 d(mpn-1) d(m-1,n-1) d(m-2,n-1) d(2,n-1) d(1n-1)
n XI, Yn, Zn d(m) d(m-1,n) d(m-2,n) d(2,n) d(14)
Note: Mmax (m) is a maximal value of the number of ore-controlling variables contained in the block unit; Ml (1) is the minimal value of the number of the ore-controlling variables contained in the block unit; d(m,n) is the minimal distance from the block unit numbered as n and taken as the target block to the characteristic block containing m ore-controlling factor combinations. (2) D(distance))-V(volume) fractal fitting According to the minimal distance value from different target blocks to the characteristic block calculated in Table 1, by taking the combination number of the ore-controlling variables as independent variables, all block units under the same ore-controlling variable combination number are sorted according to the corresponding minimal distance value (i.e. column sorting for Table 1). For the result sorted according to the distance, the number (N) of the block units contained beyond a certain distance value is counted. Because the size (volume) of the divided smallest block unit is a fixed value (Vo) when the research area is subjected to the grid division, the volume (V) of the blocks larger than a certain range is calculated according to the number and division size of the block units, as shown in Fig. 2-left. The ore prospecting prediction method of the nonlinear speculation model is a method based on a nonlinear theory. It is necessary for a research object to satisfy the fractal distribution characteristics. A main characteristic of the fractal distribution is that there is a power-function relationship between the number of the objects larger than a scale and the size of the objects. In the statistics distribution of geological phenomena, the power function is not the only type of fractal distribution, but it is the only type of distribution without characteristic scale. Therefore, according to different combination conditions of the ore-controlling factors, a relationship
Description
between D (distance) and V (volume of all blocks larger than the distance) is analyzed according to formula 1 and formula 2 to fit the self-similarity in various factor combinations and between the factors (as shown in Fig. 2-right).
V kxD d (formula 1) In the formula, D is distance, V is volume of the block larger than the distance D, d is a number of fractal dimensions, and k is a proportional constant. The logarithm of formula 1 is taken to obtain formula 2
1n V =ln K + d In D (formula 2)
Data is projected to a double-log coordinate system, and a function y=a*x+b is fitted according to the minimum interclass variance, so that the number of fractal dimensions of the
piecewise fitting is d=a
(3) Analysis on combinations of ore-controlling factors
According to the D-V fractal result, on the premise that both D (distance) and V (volume of all blocks larger than the distance) satisfy the fractal distribution characteristics (Fig. 3) under different combination numbers of ore-controlling factors, a maximal search distance (L) is set as a radius of a search sphere according to the distribution range of the characteristic blocks in the research area; and based on this value, the maximal searching distance value L is equally divided into n parts, and the spheres with radius r of L/n, 2L/n, 3L/n, 4L/n and the like are established respectively. The sphere with the minimal radius length is called the basic sphere, and the volume is Vo. On this basis, the established sphere varied at an equal step (L/n) is called a search sphere, and he volume is Vi (i is a multiple of the step, as shown in Fig. 4), and the number N of the characteristic blocks containing a specific combination number of ore-controlling variables between adjacent radii and a volume increment AV between adjacent search spheres are counted respectively. The number of the characteristic blocks with a specific number of ore-controlling factors distributed in a unit volume is used as the ore-controlling density under the specific combination number of the ore-controlling factors (formula 3), and a ratio of the ore-controlling density of different search ellipsoids to the ore-controlling density (formula 4) of the basic ellipsoid is used as the relative ore-controlling density (formula 5).
A Ni - Ni_, (formula 3)
Description
No PO = V
(formula 4)
p ANIN NM - N 1 N0 /PFo AV V V,-V_1 V) (formula 5)
According to a calculation result of the relative ore-controlling density under different search scales, a statistics curve of the relative ore-controlling density and search scales under different combination numbers of ore-controlling variables is drawn (as shown in Fig. 5). When the ore-controlling condition is loose (the combination number of the ore-controlling variables is relatively small), a lot of block units in the entire research area meet the requirement of the combination number of the ore-controlling variables, a lot of block units satisfy the preconditions and are closely distributed, and the distance between the block units is relatively small, so that with the increase of the search radius, the relative ore-controlling density is reduced; and when the condition is strict (the combination number of the ore-controlling variables is large), for the specific large combination number of the ore-controlling variables, only a few block units in the entire research area meet the requirement of the combination number of the ore-controlling variables, few block units satisfy the precondition and are distributed discretely, and the distance between the block units is relatively large, so that with the increase of the searching radius, the relative ore-controlling density increases; after the search scale increases to a certain scale, all block units satisfying the precondition are basically contained; and if the search scale continuously increases, the ore-controlling density decreases. By taking Fig. 5 as an example, when the ore-controlling variable combination number is 11, 12 and 13, the curve of the relative ore-controlling density rises first and then falls with the increase of the search distance. When the ore-controlling variable combination number is less than 11, the curve of the relative ore-controlling density falls with the increase of the search distance. Therefore, the ore prospecting prediction method of the nonlinear discrete speculation model considers the optimal combination of the ore-controlling variables when the combination number of the ore-controlling variables is 11.
Determination of an optimal shrinkage distance
After the optimal combination number of ore-controlling factors is determined through the above research, block units satisfying the optimal combination number are called favorable mineralization units in the prospecting prediction research. In the prospecting prediction method
in
Description
of the nonlinear discrete speculation model, the optimal shrinkage distance of the favorable mineralization unit is determined through sliding windows of different scales. The windows of different scales (generally the integer times of the size of the block unit) are set to perform the sliding search for the entire research area. During the sliding search, the combination of the ore-controlling factors in the sliding window is correspondingly varied. The entire sliding search process is similar to the repeated interpolation and re-classification of the ore-controlling variable combination number of the entire research area (as shown in Fig. 6). The favorable mineralization units are re-classified at the search windows of different scales; the block units meeting the requirement of the optimal combination number of ore-controlling factors are called favorable mineralization windows; and the relationship between the scale of different sliding search windows and the number of final favorable mineralization windows is counted, as shown in Fig. 7. On this basis, a sliding search scale corresponding to a peak value of the number of the favorable mineralization windows is selected as an optimal shrinkage distance value of the favorable mineralization unit under the optimal combination number of the ore-controlling factors. On the basis of determining the optimal combination number and the optimal shrinkage distance of the ore-controlling variables, a favorable mineralization area delineated according to the prospecting prediction method of the nonlinear discrete speculation model is obtained. The basic principles and main features and advantages of the present invention are shown and described above. Those skilled in the art shall understand that the present invention is not limited by the above embodiments. The illustration in the above embodiments and description only explain the principles of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements can be made to the present invention and shall fall within the protection scope of the present invention. The protection scope claimed by the present invention is defined by the appended claims and equivalents.
Claims (3)
- Claims 1. An ore prospecting prediction method of a nonlinear discrete speculation model, comprising the following steps: step I: determining an optimal combination number of ore-controlling factors (1) block calculation: on the basis of analyzing the geological background and mineralization characteristics of a research area, summarizing a prospecting model of the research area, determining the type of ore-controlling factors, establishing a local three-dimensional geological entity model according to the existing geological data of the research area, and establishing a three-dimensional block model on the basis of grid division; selecting any block unit in the block model as a target block, creating a search ellipsoid by taking the center position of the target block as a spherical center, and initializing a radius value of the search ellipsoid according to the range of the research area. after the radius is set, counting a distance value from the block unit (a characteristic block) to the target block under any combination number of the ore-controlling factors respectively in the range of the search ellipsoid, and calculating a minimal distance value; (2) distance (D)-volume (V) fractal fitting: taking the calculated minimal distance value from different target blocks to the characteristic blocks and a combination number of ore-controlling variables as independent variables, and sorting all block units under the same combination number of ore-controlling variables according to the corresponding minimal distance value; for the result sorted according to the distance, counting the number (N) of the block units contained beyond a distance value; because the size (volume) of the divided smallest block unit is a fixed value (Vo) when the research area is subjected to the grid division, the volume (V) of the block larger than a certain range is calculated according to the number and division size of the block unit; according to different combination conditions of the ore-controlling factors, analyzing a relationship between the distance and the volume of all blocks larger than the distance according to the formula 1 and formula 2 to fit self-similarity in various factor combinations and between factors.V~kxD4 (formula 1)in the formula, D is distance, V is volume of the block larger than the distance D, d is a number of fractal dimensions, and k is a proportional constant; taking the logarithm of formula 1 to obtain formula 2:In V = hi K + d n D (formula 2)Claimsprojecting data to a double-log coordinate system, and fitting the function y=a*x+b according to the minimum interclass variance, so that the number of fractal dimensions of the piecewise fitting is d=a; (3) analyzing combinations of ore-controlling factors: according to the D-V fractal result, on the premise that the D-V satisfies the fractal distribution characteristics under the different combination numbers of the ore-controlling factors, according to the distribution range of the characteristic blocks in the research area, setting a maximal search distance (L) as the radius of a search sphere; based on this value, equally dividing the maximal search distance value L into n parts, and respectively establishing spheres with the radius r of L/n, 2L/n, 3L/n, 4L/n and the like, wherein the sphere with the minimal radius length is called a basic sphere, and volume is Vo; on this basis, establishing the sphere varied at an equal step (L/n) which is called the search sphere with the volume of Vi; respectively counting the number N of characteristic blocks containing the specific combination number of ore-controlling factors between the adjacent radii, and a volume increment of the adjacent search spheres; and taking the number of characteristic blocks containing a specific number of ore-controlling factors distributed in the unit volume as the ore-controlling density (formula 3) under the specific combination number of the ore-controlling factors, and taking a ratio of the ore-controlling density of different search ellipsoids to the ore-controlling density of the basic ellipsoid (formula 4) as the relative ore-controlling density (formula 5), formula (3)PO = N 0 = formula (4)P = p/ =N /N, (N,-N,) INO / Po~ AVT ~V-V_ 4 ) SAformula V, )(5)according to a calculation result of the relative ore-controlling density under different search scales, drawing a statistics curve of the relative ore-controlling density and search scales under different combination numbers of ore-controlling variables; step II: determining an optimal shrinkage distance after the optimal combination number of ore-controlling factors is determined through the above research, all block units satisfying the optimal combination number are called favorable mineralization units in the prospecting prediction research; for the favorable mineralization units after the constraint of the optimal combination number of the ore-controlling factors, in theClaimsprospecting prediction method of the nonlinear discrete speculation model, the optimal shrinkage distance of the favorable mineralization unit is determined through sliding windows of different scales; when the entire research area is subjected to sliding search by windows of different scales, the combination of ore-controlling factors contained in the sliding windows is correspondingly changed; at the search of the windows of different scales, the block unit meeting the requirement of the optimal combination number of ore-controlling factors is called a favorable mineralization window; the relationship between the scale of different sliding search windows and the number of final favorable mineralization windows is counted; and on this basis, a sliding search scale corresponding to a peak value of the number of the favorable mineralization windows is selected as the optimal shrinkage distance value of the favorable mineralization unit under the optimal combination number of the ore-controlling factors; on the basis of determining the optimal combination number and optimal shrinkage distance of the ore-controlling variables, a favorable mineralization area delineated according to the ore prospecting prediction method of the nonlinear discrete speculation model is obtained.
- 2. The ore prospecting prediction method of the nonlinear discrete speculation model according to claim 1, wherein in the step I (1), calculating the minimal distance value is to calculate a minimal value of the distance from all characteristic blocks containing 1 ore-controlling factor in the range of the ellipsoid to the target block respectively, a minimal value of the distance from all characteristic blocks containing 2 ore-controlling factors to the target block, and a minimal value of the distance from all characteristic blocks containing 3 ore-controlling factors to the target block, and so on.
- 3. The ore prospecting prediction method of the nonlinear discrete speculation model according to claim 1, wherein in the step I (3), in the block model, when the ore-controlling condition is loose, a lot of block units in the entire research area meet the requirement of the combination number of the ore-controlling factors, a lot of block units satisfy the preconditions and are closely distributed, and the distance between the block units is relatively small, so that with the increase of the search radius, the relative ore-controlling density is reduced; and when the condition is strict, for the large combination number of the specific ore-controlling factors, only a few block units in the entire research area meet the requirement of the combination number of the ore-controlling factors, few block units satisfy the precondition and are discretely distributed, and the distance between the block units is relatively large, so that with the increase of the search radius, the relative ore-controlling density increases; after the search scale increasesClaimsto a certain scale, all block units satisfying the precondition are basically contained; and if the search scale continuously increases, the relative ore-controlling density decreases.
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