NL2030476B1 - Prospecting Method Based on Geological Information and Device, Electronic Equipment and Storage Medium Thereof - Google Patents

Prospecting Method Based on Geological Information and Device, Electronic Equipment and Storage Medium Thereof Download PDF

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NL2030476B1
NL2030476B1 NL2030476A NL2030476A NL2030476B1 NL 2030476 B1 NL2030476 B1 NL 2030476B1 NL 2030476 A NL2030476 A NL 2030476A NL 2030476 A NL2030476 A NL 2030476A NL 2030476 B1 NL2030476 B1 NL 2030476B1
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ore
block unit
value
block
area
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Wang Gongwen
Zhang Zhiqiang
Li Ruixi
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Univ China Geosciences Beijing
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    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
    • E21B2200/20—Computer models or simulations, e.g. for reservoirs under production, drill bits

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Abstract

Disclosed is a nonlinear quantitative prospecting method based on geoscience information 5 and device, electronic equipment and storage medium, which includes: collecting geological data in a research area through field geological survey, and verifying the collected geological data; three-dimensional solid models of various geological bodies and exploration data are constructed by three-dimensional geological modelling technology, and the solid models are gridded into three-dimensional grid data by discrete interpolation method, and the attribute values are 10 uniformly stored in a three-dimensional grid data structure; Traverse all layers of 3D grid data according to the cursor of layers; the prior probability Ppriorand the prior advantage O,… of the study area are calculated; according to the posterior probability values of each block unit in the study area, the prediction results in the study area are generated. In this invention, fractal and multifractal methods are applied to determine the abnormal lower limit of some prospecting 15 indicators and classify the metallogenic prospect areas, so that the distribution of mineral deposits in the study area is more accurate.

Description

Prospecting Method Based on Geological Information and Device, Electronic Equipment and Storage Medium Thereof
TECHNICAL FIELD The invention relates to intelligent prospecting technology, in particular to a nonlinear quantitative prospecting method based on geoscience information and device, electronic equipment and computer storage medium thereof, and to method for detecting ore and mining thereof based on the said technology.
BACKGROUND In recent years, the prospecting, prediction and evaluation of mineral survey and exploration tend to be applied by computer, especially the technology based on Geographic Information System (GIS) has always been an important technical means for quantitative geoscience and geoscience information researchers at home and abroad. The extraction and integration of multivariate metallogenic geoscience information (geology, geophysics, geochemistry and remote sensing) based on 2-dimension (2D) or 3-dimension (3D) GIS has become a technical method system for mineral resources evaluation, and it is still a mineral resource at home and abroad. The system is universal for the evaluation of medium-scale and small-scale 2D regional resources, but there are still many shortcomings in the evaluation of large-scale 3D mineral resources over 1 : 50000 scale. Especially, the system is obviously lacking in the positioning prediction and metal resource estimation of deep concealed ore, mainly because of the lack of necessary 3D spatial analysis technology and the extraction and integration method of ore-forming anomaly information due to the restriction of 3D visualization of geological bodies. At present, 3D deep prospecting is a flashpoint and a difficult topic in domestic and international geoscience research, while there is almost no relevant technology for reference.
SUMMARY The invention provides a nonlinear quantitative prospecting method based on geological information and device, in particular a method for detecting ore occurrence and mining thereof comprising nonlinear quantitative prospecting of the occurrence of ore based on geological information, electronic equipment and a storage medium. The first objective of the invention is to provide a nonlinear quantitative prospecting method based on geological information, in particular a method for detecting ore occurrence and mining thereof comprising nonlinear quantitative prospecting of the occurrence of ore based on geological information comprising following steps. (1) Collecting geological data in the study area through field geological survey, verifying the collected geological data, building three-dimensional solid models of various geological bodies and detection data through three-dimensional geological modelling technology, gridding the solid models into three-dimensional grid data through discrete interpolation method, and uniformly storing attribute values in a three-dimensional grid data structure; the geological data includes at least one of the following: stratigraphic association relationship, stratigraphic sequence, lithologic stratification characteristics, unconformity contact and fault contact relationship between strata, stratum information of rock mass shape and occurrence, etc.; When reading 3D grid data, it is necessary to traverse all block units in all layers of 3D grid data according to the order of index from small to large, and record the currently accessed index position in each layer through the cursor; when the index of the block unit where the cursor is located in the layer is consistent with the current search index, pile the index of the block unit into the stack; if the index value of the block unit where the cursor is located in the layer is greater than the index value currently searched, the layer is skipped to keep the cursor position of the layer unchanged, Assuming that there are T block units in the study area, each block unit has at most one known ore occurrence unit, among which there are D block units with known ore occurrences, then the prior probability Pir and the prior advantage Or of the study area are obtained as follows: Pprior = D (1)
T Prior Oprior = IP Prior (2) P(B/D), P(B;|D), P(B,|D) and P(B,|D) are calculated respectively, where P{B|D) represents the probability of existence of ore-controlling variable Biwhen there is a mineral deposit site in a block unit, and the expression is as follow: P(BID) = Le P(B,ID) = pain) =D pip) =I 3) P(B/D)represents the ratio of the ore-controlling variable B;and the number of block units existing at the mineral deposit site to the total number of block units containing the mineral deposit site; N denotes the intersection operation, N denotes the number of block units, D denotes non-ore block units, and B; denotes the i-th block unit of the i-th ore-controlling variable; if T represents the total block unit in the study area, then the weight of B, = T — B; ore- controlling variable B;can be expressed as follow: Wr =n en P(B;|D) Wi =1In VID (4) P(B,|D)
In formula (4), W;* represents the weight value of the area where the ore-controlling variable B;exists, while W; represents the weight value of the area where the ore-controlling variable B;does not exist, and the weight value of the area where the data is missing is set to 0. The correlation degree C; = W;t — W;™ between ore-controlling variable B;and ore mineral deposit site, with C; value greater than zero, indicates that the existence of ore-controlling variable B;is beneficial to mineralization, and the larger C; value, the more favourable it is to mineralization. On the contrary, if the C; value is less than zero, it means that the existence of ore-controlling variable B;is unfavourable to mineralization, and the smaller the C; value, the more unfavourable it is to mineralization. A zero C; value means that the ore-controlling variable Brhas nothing to do with mineralization. The variance of weights can be calculated according to formula (5): 1 1 WIT NBA) N{B nD) 1 1 = SGD) NE nD) © For each ore-controlling variable, W;* and W,™ are used to replace the 1 and O values of the multivariate linear model, respectively, and the partial regression coefficient 8; of the ore- controlling variable is calculated according to the following formula (6): n In(DIB;B3 .. By) = > Bli +a’ (6) i=1 The value of x; is W;t or W;; and then obtain the partial regression coefficient 2; of each ore-controlling variable, which is used as the correction coefficient of ore-controlling variable weight to get the weighted evidence weight: wit =p wi Wo = BW (7) The posterior advantage of any grid element in the study area can be expressed as follows: Opost = Oprior * eZ Wi(i= 1,2, ..,N) (8) The logarithm of both sides of equation in formula (8) can be obtained as follows:
N In Opost (DIBÉBE .. BE) = In Oprior + > WEG =1.2,....N) (9) i=1 The posterior probability of any block unit is: Opost Prost = T= 0, (10) According to the posterior probability values of each block unit in the study area, the prediction results in the study area are generated.
(2) According to the posterior probability values and prediction results of each block unit in the study area, the study area is divided into separation background value areas and metallogenic prospect areas, and the metallogenic prospect areas are further divided to obtain multi-level metallogenic prospect areas; the fractal model used in the classification of metallogenic prospect areas is shown in the following formula (11): NG) =CrP (11) in which r is the characteristic scale, which represents the posterior probability; C > 0, is scale coefficient, d > 0, is fractal dimension; N(r) represents the cumulative number of block elements whose posterior probability is less than the characteristic scale r; take the logarithm of equation (11) as: InN(r) =lInC —Dlnr (12) According to formula (12), logN(r) and logr have a linear relationship, and the fractal dimension is the absolute value of the slope of the double logarithmic fitting line. The ordinary least square method is used to fit the straight line segment by segment, and the background value area and metallogenic prospect area are determined based on different slope values of the fitted straight line. The boundary values corresponding to different line segments are used as critical values to distinguish the background value area and metallogenic prospect area, or as critical values to distinguish different levels of metallogenic prospect areas.
(3) Dividing the storage space into a plurality of grids, each grid is called a block unit, storing each geological data in each block unit, and assigning attribute values of geological data stored in the block unit according to each block unit; among them, the point entities are represented by a block element; line entities are represented by adjacent block units connected in a string in a certain direction; surface entities are represented by block sets of adjacent block units with the same attributes; solid entities are represented by a block set with a block unit.
(4) An electronic equipment, is characterized by comprising a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to be able to execute the steps of the nonlinear quantitative prospecting method based on geo-information according to any one of claims 1-3 when calling the executable instructions in the memory.
(5) A computer-readable storage medium on which computer instructions are stored, is characterized in that the instructions, when executed by a processor, can operate the steps of the nonlinear quantitative prospecting method based on geoscience information according to any one of claims 1-3.
BRIEF DESCRIPTION OF THE FIGURES FIG. 1 is a schematic flow diagram of the nonlinear quantitative prospecting method based on geoscience information of the present invention; FIG. 2 is a structural diagram of the nonlinear quantitative prospecting device based on geo-information of the present invention.
DESCRIPTION OF THE INVENTION The essence of the technical scheme of the present invention will be explained in detail with reference to the accompanying drawings.
5 FIG. 1 is a schematic flow diagram of the nonlinear quantitative prospecting method based on geo-information of the present invention. As shown in FIG. 1, the nonlinear quantitative prospecting method based on geo-information of the embodiment of the present invention includes the following processing steps: (1) Step 101 Collect geological data in the study area through field geological survey, and check the collected geological data. The geological data includes at least one of the following: stratigraphic association relationship, stratigraphic sequence, lithologic stratification characteristics, unconformity contact and fault contact relationship between strata, and stratigraphic information of rock mass shape and occurrence in the study area. In the embodiment of the invention, geological survey can obtain the geological data by means of exploration, scanning, satellite remote sensing detection and the like. (2) Step 102 The geological data is stored as a three-dimensional grid, which is stored in the form of block units. Each grid is a block unit, each geological data is stored in each block unit, and attribute values of geological data stored in the block unit are given according to each block unit; among them, the point entities are represented by a block element; line entities are represented by adjacent block units connected in a string in a certain direction; surface are represented by block sets of adjacent block units with the same attributes; solid entities are represented by a block set with a block unit. Each block unit 3182 7Thas a value, which is used to express the described item, such as category, height, magnitude, etc. (3) Step 103 Traverse all layers of the 3D grid according to the cursor of the layer. Through the processing of 3D grid, every search can extract all the block units with this index in each layer, and all the data can be read by only once traverse. At the same time, the process of traversing all through direct stream reading and writing of files, without reading files into memory, and thus reducing the resource occupation of computing and processing and improving the processing efficiency. (4) Step 104 Calculate the prior probability Poor and the prior advantage Oprior of the study area. According to the embodiment of the invention, the logistic regression coefficient is used as the weight factor of the weighted evidence weight to obtain the weighted weight and calculate the posterior probability. By using logistic regression coefficient as weight factor, unbiased estimation of posterior probability can be obtained, thus overcoming the influence of the independence of ore-controlling variables on prediction results.
Assuming that there are 7 block units in the study area, each block unit has at most one known ore occurrence unit, among which there are D block units with known ore occurrences, then the prior probability Pprior and the prior advantage Op Of the study area are obtained as follows: Psion = 7 (1 Prior Oprior = TE Bor (2) P(B/D), P(B;|D), P(B,|D) and P(B,|D) are calculated respectively, where P(B|D) represents the probaBility of existence of ore-controlling variable B;when there is a mineral deposit site in a block unit, and the expression is: PID) 2D PD) = PID) =D PED) = 22 © P(B/D) represents the ratio of the ore-controlling variable B;and the number of block units existing at the mineral deposit site to the total number of block units containing the mineral deposit site; N denotes the intersection operation, N denotes the number of block units, D denotes the block units of non-ore bodies, and B;denotes the block units of the i-th ore- controlling variable.
If T denotes the total block units in the study area, then the weight of B, = T — B;ore-controlling variable B;can be expressed as follows: W* =1In ja Pp (B.D) P(B,|D Ww, =In hn (4) In formula (4), W; represents the weight value of the area where the ore-controlling variable B; exists, while W; represents the weight value of the area where the ore-controlling variable B;does not exist, and the weight value of the area where the data is missing is set to 0. The correlation degree C; = W;* — W; between ore-controlling variable B;and ore mineral deposit site, with C; value greater than zero, indicates that the existence of ore-controlling variable B;is beneficial to mineralization, and the larger C; value, the more favourable it is to mineralization.
On the contrary, if the C; value is less than zero, it means that the existence of ore-controlling variable B;is unfavourable to mineralization, and the smaller the C; value, the more unfavourable it is to mineralization.
A zero C; value means that the ore-controlling variable B;has nothing to do with mineralization.
The variance of weights can be calculated according to formula (5):
WIT NBA) N{B nD) 1 1 a2(W; = NE np) TNE AD) (5) For each ore-controlling variable, W‚* and W; are used to replace the 1 and 0 values of the multivariate linear model, respectively, and the partial regression coefficient 8; of the ore- controlling variable is calculated according to the following formula (6): n In(D|B;B; … Bi) = > Bix +a’ (6) i=1 The value of x; is W;* or W;”; and then obtain the partial regression coefficient 8; of each ore-controlling variable, which is used as the correction coefficient of ore-controlling variable weight to get the weighted evidence weight: wit = pw Wi = BW (7) The posterior advantage of any grid element in the study area can be expressed as follows: Opast = Oprior * eZ (i = 1,2, .., N) (8) The logarithm of both sides of equation in formula (8) can be obtained as follows:
N In Opost (DIBÉBÉ .. BY) = In Oprior + > WE(Gi=12,....,N) (9) i=1 The posterior probability of any block unit is: Opost Prost = T= Opo (10) According to the posterior probability values of each block unit in the study area, the prediction results in the study area are generated. (5) step 105 According to the posterior probability values and prediction results of each block unit in the study area, the study area is divided into separation background value areas and metallogenic prospect areas, and the metallogenic prospect areas are further divided to obtain multi-level metallogenic prospect areas; among them, the fractal model used in the classification of metallogenic prospect areas is shown in the following formula (11): N({r)=cCcrP (11) in which r is the characteristic scale, which represents the posterior probability; C > 0, is scale coefficient, d > Q, is fractal dimension; N(r) represents the cumulative number of block elements whose posterior probability is less than the characteristic scale r; take the logarithm of equation (11) as: InN(r) =lnC —-Dlnr (12)
According to formula (12), logN(r) and logr have a linear relationship, and the fractal dimension is the absolute value of the slope of the double logarithmic fitting line.
The ordinary least square method is used to fit the straight line segment by segment, and the background value area and metallogenic prospect area are determined based on different slope values of the fitted straight line.
The boundary values corresponding to different line segments are used as critical values to distinguish the background value area and metallogenic prospect area, or as critical values to distinguish different levels of metallogenic prospect areas.
The second objective of the invention is to provide a nonlinear quantitative prospecting device based on geo-information, and FIG. 2 is a schematic structural diagram of the device.
As shown in FIG. 2, the nonlinear quantitative prospecting device based on geo-information of the embodiment of the present invention includes: a collecting unit 20 is configured to collect geological data in the study area through field geological investigation and verify the collected geological data; a storage unit 21 is configured to store the geological data in the form of a three- dimensional grid, and assign corresponding attribute values to the three-dimensional grid data; a searching unit 22 is configured to traversal all block units in all layers of the 3D grid according to the order of index from small to large, and record the currently accessed index position in each layer through the cursor; when the index of the block unit where the cursor is located in the layer is consistent with the current search index, pile the index of the block unit into the processing stack; if the index value of the block unit where the cursor is located in the layer is greater than the index value currently searched, the layer is skipped to keep the cursor position of the layer unchanged, a calculation unit 23 is configured to the followings: assume that there are T block units in the study area, and at most one known ore occurrence unit appears in each block unit, wherein, if there are D block units containing known ore occurrences, the prior probability Prior and the prior advantage Op of the study area are obtained as follows: Pyrior = z (1) Prior Oprior = TE Por (2) P(B/D}), P(B;|D), P(B,|D) and P(B,|D) are calculated respectively, where P(B|D) represents the probability of existence of ore-controlling variable B;when there is a mineral deposit site in a block unit, and the expression is as follow: pain) 2D ip) NED pain) =D pip) =I 3) P(B/D)represents the ratio of the ore-controlling variable B;and the number of block units existing at the mineral deposit site to the total number of block units containing the mineral deposit site; N denotes the intersection operation, N denotes the number of block units, D denotes non-ore block units, and B; denotes the i-th block unit of the i-th ore-controlling variable; if T represents the total block unit in the study area, then the weight of B, = T — B; ore- controlling variable B;can be expressed as follow: W‚ =1n a 4 P(BID) Wi =ln EE (4) P(B,|D)
In formula (4), W;* represents the weight value of the area where the ore-controlling variable B;exists, while W‚ represents the weight value of the area where the ore-controlling variable B;does not exist, and the weight value of the area where the data is missing is set to 0. The correlation degree C; = W;t — W; between ore-controlling variable B;and ore mineral deposit site, with C; value greater than zero, indicates that the existence of ore-controlling variable B;is beneficial to mineralization, and the larger C; value, the more favourable it is to mineralization.
On the contrary, if the C; value is less than zero, it means that the existence of ore-controlling variable B;is unfavourable to mineralization, and the smaller the C; value, the more unfavourable it is to mineralization.
A zero C; value means that the ore-controlling variable
B;has nothing to do with mineralization.
The variance of weights can be calculated according to formula (5):
1 1 TW = nD) NB nD) 1 1 WI = BaD) NED) ©
For each ore-controlling variable, W‚* and W; are used to replace the 1 and 0 values of the multivariate linear model, respectively, and the partial regression coefficient B; of the ore- controlling variable is calculated according to the following formula (6):
n In(D|B;B; B) = > Bli +a’ ©) i=1
The value of x; is W;* or W,™; and then obtain the partial regression coefficient 2; of each ore-controlling variable, which is used as the correction coefficient of ore-controlling variable weight to get the weighted evidence weight:
wit = fi wr Wi = Bw, (7)
The posterior advantage of any grid element in the study area can be expressed as follows:
Opost = Oprtor * eZ WE = 1,2,...,N) (8)
The logarithm of both sides of equation in formula (8) can be obtained as follows:
N In Opose (DIBEBE … B) = In Oprior + > WE(i=1,2, NN) (9) i=1 The posterior probability of any block unit is: Ppost = TE (10) According to the posterior probability values of each block unit in the study area, the prediction results in the study area are generated.
A determining unit 24 is configured to generate the prediction results in the study area according to the posterior probability values of each block unit in the study area.
On the basis of the nonlinear quantitative prospecting device based on geo-information shown in FIG. 2, the nonlinear quantitative prospecting device based on geo-information according to the embodiment of the present invention further includes: A dividing unit (not shown in FIG. 2), which is configured to divide the study area into separation background value area and metallogenic prospect area according to the posterior probability values and prediction results of each block unit in the study area, and further divide the metallogenic prospect area to obtain multi-level metallogenic prospect area; among them, the fractal model used in the classification of metallogenic prospect areas is shown in the following formula (11): NG) =CrP (11) in which r is the characteristic scale, here it means the posterior probability, C > 0, is the proportional coefficient, d > 0, is the fractal dimension, and N{r} means the cumulative number of block units whose posterior probability is less than the characteristic scale r; take the logarithm of equation (11) as: InN(r) =lnC —-Dlnr (12) According to formula (12), logN(r) and logr have a linear relationship, and the fractal dimension is the absolute value of the slope of the double logarithmic fitting line. The least square method is used to fit the straight line segment by segment, and the background value area and metallogenic prospect area are determined based on different slope values of the fitted straight line. The boundary values corresponding to different line segments are used as critical values to distinguish the background value area and metallogenic prospect area, or as critical values to distinguish different levels of metallogenic prospect areas.
In the embodiment of the present disclosure, the specific way of each unit in the nonlinear quantitative prospecting device based on geo-information shown in FIG. 2 has been described in detail in the embodiment related to this method, and will not be explained in detail here.
The third objective of the invention is to provide an electronic equipment, which comprises a processor and a memory for storing the executable instructions of the processor, wherein the processor is configured to be able to execute the steps of the nonlinear quantitative prospecting method based on geo-information of the previous embodiment when calling the executable instructions in the memory.
The fourth objective of the present invention is to provide a computer-readable storage medium with computer instructions stored thereon, which is characterized in that the instructions, when executed by a processor, realize the steps of the nonlinear quantitative prospecting method based on geo-information of the previous embodiment.
In this embodiment, the non-transitory computer readable storage medium can be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
In addition, the features and benefits of the present invention are described with reference to exemplary embodiments. Accordingly, the present invention should definitely not be limited to these exemplary embodiments which illustrate some possible combinations of non-limiting features, which can exist alone or in other combinations of features.
Those of ordinary skill in the art will easily think of other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. This invention is intended to cover any variation, use or adaptation of the present invention, which follows the general principle of the present invention and includes the common knowledge or customary technical means in the technical field that the present invention has not disclosed. The description and examples are to be regarded as exemplary only, and the true scope and spirit of the invention should be determined by the claims.

Claims (5)

CONCLUSIESCONCLUSIONS 1. Een werkwijze voor het vinden en delven van erts omvattende het niet-lineair kwantitatief vaststellen van verwachtingen van het voorkomen van erts op basis van geologische informatie omvattende van de volgende stappen: — het verzamelen van geologische gegevens in het studiegebied door middel van geologisch veldonderzoek, het verifiëren van de verzamelde geologische gegevens, het opzetten van driedimensionale vaste modellen van verschillende geologische lichamen en opsporingsgegevens door middel van driedimensionale geologische modelleringstechnologie, het rasteren van de vaste modellen in driedimensionale rastergegevens door middel van discrete interpolatiemethode, en het uniform opslaan van attribuutwaarden in een driedimensionale rastergegevensstructuur, waarbij de geologische gegevens ten minste een van de volgende omvatten: stratigrafische associatierelatie, stratigrafische sequentie, lithologische stratificatiekenmerken, unconformiteitsscontact en breukcontactrelatie tussen strata, stratuminformatie van gesteentemassavorm en voorkomen, enz. ; — bij het lezen van 3D-rastergegevens het noodzakelijkerwijs doorlopen van alle blokeenheden in alle lagen van 3D-rastergegevens volgens de volgorde van index van klein naar groot, en het vastleggen van de momenteel gezochte indexpositie in elke laag via de cursor; waarbij wanneer de index van de blokeenheid waar de cursor zich in de laag bevindt overeenkomt met de huidige zoekindex, de index van de blokeenheid op de stapel wordt gelegd; wanneer de indexwaarde van de blokeenheid waar de cursor zich in de laag bevindt groter is dan de momenteel gezochte indexwaarde, de laag wordt overgeslagen om de cursorpositie van de laag ongewijzigd te laten; — hetaannemen dat er T blokeenheden in het onderzoeksgebied zijn, dat elke blokeenheid ten hoogste één bekende ertsvoorkomenseenheid heeft, waaronder er D blokeenheden zijn met bekende ertsvoorkomens, waarbij de voorafgaande waarschijnlijkheid Por en het voorafgaande voordeel Opo van het studiegebied als volgt worden verkregen: Porior = : (1) Oprior = er (2) — het berekenen van P(B||D), P(B;|D), P(B,|D) and P(B,|D), waarbij P(B|D) staat voor de waarschijnlijkheid van het bestaan van de ertsbeheersingsvariabele B;wanneer er een vindplaats van een mineraalafzetting in een blokeenheid is, als volgt uitgedrukt:1. An ore finding and mining method comprising the non-linear quantitative assessment of ore occurrence expectations based on geological information comprising the following steps: — collecting geological data in the study area through geological field survey , verifying the collected geological data, establishing three-dimensional solid models of various geologic bodies and prospecting data through three-dimensional geologic modeling technology, rasterizing the solid models into three-dimensional raster data through discrete interpolation method, and uniformly storing attribute values in a three-dimensional grid data structure, where the geologic data includes at least one of the following: stratigraphic association relationship, stratigraphic sequence, lithological stratification features, nonconformity contact, and fracture contact relationship between str ata, stratum information of rock mass form and occurrence, etc.; — when reading 3D raster data, necessarily traversing all block units in all layers of 3D raster data in order of index from smallest to largest, and recording the currently searched index position in each layer via the cursor; where if the index of the block unit where the cursor is in the layer matches the current search index, the index of the block unit is placed on the stack; when the index value of the block unit where the cursor is located in the layer is greater than the currently searched index value, the layer is skipped to leave the cursor position of the layer unchanged; — assuming there are T block units in the study area, that each block unit has at most one known ore occurrence unit, among which there are D block units with known ore occurrences, obtaining the preceding probability Por and the preceding advantage Opo of the study area as follows: Porior = : (1) Oprior = er (2) — calculating P(B||D), P(B;|D), P(B,|D) and P(B,|D), where P(B |D) denotes the probability of the existence of Ore Control Variable B;when there is a mineral deposit occurrence in a block unit, expressed as follows: pin) "OD ip) NED pain) =D ip) =D & waarbij P(B|D) staat voor de verhouding tussen de ertsbeheersingsvariabele B;en het aantal blokeenheden dat op de plaats van de minerale afzetting bestaat, en het totale aantal blokeenheden dat de plaats van de minerale afzetting bevat; N staat voor de intersectiebewerking, N staat voor het aantal blokeenheden, D staat voor niet- ertsblokeenheden, en B; staat voor de it blokeenheid van de it ertsbeheersingsvariabele indien T staat voor de totale blokeenheid in het studiegebied, dan kan het gewicht van B, = T — B, ertsbeheersende variabele B; als volgt worden uitgedrukt: P(B;|D P(B,|D W‚ = a (4) waarbij in formule (4) W;t staat voor de gewichtswaarde van het gebied waar de ertsbeheersingsvariabele B; bestaat, waarbij W; staat voor de gewichtswaarde van het gebied waar de ertsbeheersingsvariabele B; niet bestaat, en de gewichtswaarde van het gebied waar de gegevens ontbreken op 0 wordt gesteld; waarbij de correlatiegraad C; = W‚* —W/ tussen de ertsbeheersingsvariabele B; en het ertsmineraalafzettingsgebied, waarbij de Crwaarde groter is dan nul, geeft aan dat het bestaan van de ertsbeheersingsvariabele B; gunstig is voor de mineralisatie, en hoe groter de C-waarde, hoe gunstiger het is voor de mineralisatie; als de C-waarde daarentegen kleiner is dan nul, dit betekent dat het bestaan van de ertsbeheersingsvariabele B; ongunstig is voor de mineralisering, waarbij hoe kleiner de C-waarde, hoe ongunstiger deze voor de mineralisering is; waarbij een Crwaarde van nul betekent dat de ertsbeheersingsvariabele B; niets met de mineralisering te maken heeft; — waarbij de variantie van de gewichten kan berekend worden volgens formule (5): 1 1 WIT NBA) ! NB; nD) 1 1 TW) EAD) TNE AD) © waarbij voor elke ertsbeheersingsvariabele W;‚* en W; worden gebruikt ter vervanging van respectievelijk de 1- en de O-waarden van het multivariate lineaire model, en de partiële regressiecoéfficiént 8; van de ertsbeheersingsvariabele wordt berekend aan de hand van de volgende formule (6):pin) "OD ip) NED pain) =D ip) =D & where P(B|D) is the ratio between the ore control variable B; and the number of block units existing at the mineral deposit site, and the total number block units containing the mineral deposit site; N represents the intersection operation, N represents the number of block units, D represents non-ore block units, and B; represents the it block unit of the ore control variable if T represents the total block unit in the study area, then the weight of B, = T — B, ore controlling variable B; can be expressed as follows: P(B;|D P(B,|D W‚ = a (4) where in formula (4) W;t denotes the weight value of the area where the ore control variable B; exists, where W; denotes the weight value of the area where the ore control variable B; does not exist, and the weight value of the area where the data is missing is set to 0; where the degree of correlation C; = W‚* —W/ between ore management rsing variable B; and the ore mineral deposit area, where the Cr value is greater than zero, indicates the existence of the ore control variable B; is favorable for mineralization, and the greater the C-value, the more favorable it is for mineralization; if, on the other hand, the value of C is less than zero, it means that the existence of the ore control variable B; is unfavorable for mineralization, whereby the smaller the C-value, the less favorable it is for mineralization; where a Cr value of zero means that the ore control variable B; has nothing to do with the mineralization; — where the variance of the weights can be calculated according to formula (5): 1 1 WHITE NBA) ! NB; nD) 1 1 TW) EAD) TNE AD) © where for each ore control variable W;,* and W; are used to replace the 1 and 0 values of the multivariate linear model, and the partial regression coefficient 8, respectively; of the ore control variable is calculated using the following formula (6): n In(D|BIB; B) = > Bix, +a’ (6) i=1 waarbij de waarde van x; gelijk is aan W,* or W;”; waarbij vervolgens de gedeeltelijke regressiecoéfficiént 8; van elke ertsbeheersingsvariabele wordt verkregen, die wordt gebruikt als de correctiecoëfficiënt van het ertsbeheersingsvariabele gewicht om het gewogen bewijsgewicht te verkrijgen: wit = Bi wit Wi = BW (7) — waarbij het posterior voordeel van elk rasterelement in het studiegebied als volgt kan worden uitgedrukt: Opost = Oprior * Zi wit (i=12,..,N) (8) waarbij het logaritme van beide zijden van de vergelijking in formule (8) als volgt kan worden verkregen:n In(D|BIB; B) = > Bix, +a' (6) i=1 where the value of x; equals W,* or W;”; where then the partial regression coefficient δ; of each ore control variable is obtained, which is used as the correction coefficient of the ore control variable weight to obtain the weighted weight of evidence: white = Bi white Wi = BW (7) — where the posterior advantage of each grid element in the study area can be expressed as follows: Oppost = Oprior * Zi white (i=12,..,N) (8) where the logarithm of both sides of the equation in formula (8) can be obtained as follows: N In Opost (D|BEBY .. BY) = In Oprior + > WrG=12,....,N) (9) i=1 — de posterior waarschijnlijkheid van elke blokeenheid is: Opost Prost ZT Ge (10) — het genereren van de voorspellingsresultaten van het onderzoeksgebied overeenkomstig de posterior waarschijnlijkheidswaarden van elke blokeenheid in het onderzoeksgebied waarmee het ertsvoorkomen wordt vastgesteld; en — het delven van de erts alwaar de aanwezigheid ervan is vastgesteld.N In Oppost (D|BEBY .. BY) = In Oprior + > WrG=12,....,N) (9) i=1 — the posterior probability of each block unit is: Opost Prost ZT Ge (10) — generating the prediction results of the survey area according to the posterior probability values of each block unit in the survey area identifying the ore occurrence; and — mining the ore where its presence has been identified. 2. De werkwijze volgens conclusie 1, die voorts omvat: het overeenkomstig de posterior waarschijnljkheidswaarden en voorspellingsresultaten van elke blokeenheid in het studiegebied onderverdelen van het onderzoeksgebied in scheidingsgebieden met achtergrondwaarde en metallogene verwachtingsgebieden, waarbij de metallogene verwachtingsgebieden verder worden onderverdeeld ter verkrijging van metallogene verwachtingsgebieden op meerdere niveaus; waarbij het fractale model dat wordt gebruikt bij de classificatie van metallogene verwachtingsgebieden wordt weergegeven in de volgende formule (11): N(r) =Cr? (11) waarbij r de karakteristieke schaal is, die de posterior waarschijnlijkheid weergeeft; C > 0 is de schaalcoëfficiënt, d > 0 is de fractale dimensie; N(r) staat voor het cumulatieve aantal blokelementen waarvan de posterior waarschijnlijkheid kleiner is dan de karakteristieke schaal r; waarbij het logarithme van vergelijking 11 wordt genomen als: InN(r)=InC—-Dlinr (12) volgens formule (12), waarbij logN(r} en logr een lineair verband hebben, en de fractale dimensie de absolute waarde van de helling van de dubbele logaritmische passingslijn is; waarbij de gewone kleinste kwadraatmethode wordt gebruikt om de rechte lijn segment per segment te passen, en het achtergrondwaardegebied en het metallogene verwachtingsgebieden worden bepaald op basis van verschillende hellingswaarden van de rechte passingslijn; waarbij de grenswaarden die overeenstemmen met verschillende lijnsegmenten worden gebruikt als kritische waarden om het achtergrondwaardegebied en het metallogene verwachtingsgebied van elkaar te onderscheiden, of als kritische waarden om verschillende niveaus van metallogene verwachtingsgebieden van elkaar te onderscheiden.The method of claim 1, further comprising: according to the posterior probability values and prediction results of each block unit in the study area, dividing the study area into separation areas of background value and metallogenic expectation areas, wherein the metallogenic expectation areas are further subdivided to obtain metallogenic expectation areas by multiple levels; where the fractal model used in the classification of metallogenic expectation regions is represented in the following formula (11): N(r) =Cr? (11) where r is the characteristic scale, representing the posterior probability; C > 0 is the scale coefficient, d > 0 is the fractal dimension; N(r) denotes the cumulative number of block elements whose posterior probability is less than the characteristic scale r; where the logarithm of equation 11 is taken as: InN(r)=InC—-Dlinr (12) according to formula (12), where logN(r} and logr have a linear relationship, and the fractal dimension is the absolute value of the slope of the double logarithmic fit line; where the ordinary least squares method is used to fit the straight line segment by segment, and the background value range and the metallogenic expectation ranges are determined from different slope values of the straight fit line; where the cutoff values correspond to different line segments are used as critical values to distinguish the background value region from the metallogenic expectation region, or as critical values to distinguish different levels of metallogenic expectation regions. 3. De werkwijze volgens conclusie 1 waarbij het opslaan van de geologische gegevens in de vorm van een driedimensionale rastereenheid omvat: het verdelen van de opslagruimte in een aantal rasters, waarbij elk raster een blokeenheid wordt genoemd, het opslaan van elke geologisch gegeven in elke blokeenheid, en het toekennen van attribuutwaarden van geologische gegevens die in de blokeenheid zijn opgeslagen overeenkomstig elke blokeenheid; waarbij onder hen de puntentiteiten worden vertegenwoordigd door een blokelement; waarbij lijnentiteiten worden vertegenwoordigd door aangrenzende blokeenheden die in een reeks in een bepaalde richting zijn verbonden; waarbij oppervlakte- entiteiten worden vertegenwoordigd door blokreeksen van aangrenzende blokeenheden met dezelfde attributen; en waarbij vaste entiteiten worden vertegenwoordigd door een blokreeks met een blokeenheid.The method of claim 1 wherein storing the geological data in the form of a three-dimensional grid unit comprises: dividing the storage space into a plurality of grids, each grid being called a block unit, storing each geological data in each block unit , and assigning attribute values of geological data stored in the block unit according to each block unit; where among them the point entities are represented by a block element; where line entities are represented by adjacent block units connected in a sequence in a particular direction; where surface entities are represented by block sequences of adjacent block units with the same attributes; and wherein fixed entities are represented by a block sequence with a block unit. 4. Elektronische apparatuur, die een processor en een geheugen voor het opslaan van uitvoerbare instructies van de processor omvat, waarbij de processor is geconfigureerd om de stappen van de niet-lineaire kwantitatieve prospectiemethode op basis van geo-informatie zoals gedefinieerd in willekeurig welke van conclusies 1 - 3 te kunnen uitvoeren wanneer de uitvoerbare instructies in het geheugen worden opgeroepen.An electronic equipment comprising a processor and a memory for storing executable instructions from the processor, the processor configured to perform the steps of the non-linear geo-information based quantitative prospecting method as defined in any one of claims 1 - 3 to be able to execute when the executable instructions are called into memory. 5. Een computer-leesbaar opslagmedium waarop computerinstructies zijn opgeslagen, waarbij de instructies, wanneer zij door een bewerker worden uitgevoerd, de stappen van het niet- lineair kwantitatief vaststellen van verwachtingen van het voorkomen van erts op basis van geologische informatie zoals gedefinieerd in willekeurig welke van conclusies 1 - 3 kunnen uitvoeren.5. A computer-readable storage medium on which computer instructions are stored, the instructions, when executed by a processor, specifying the steps of non-linear quantitative determination of ore occurrence expectations based on geological information as defined in any of claims 1 - 3.
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