A method for realizing mineral resource detection by using spatial distribution rules of metallogenic metal elements

Abstract

The application discloses a method for realizing mineral resource detection by using spatial distribution rules of metallogenic metal elements, which utilizes the data accumulated in existing mineral exploration and mining, firstly finds an index metal relative quantization parameter index, and then establishes a geological sample model of the index metal relative quantization parameter three-dimensional spatial distribution rule by a computer; drilling construction is conducted on a prospecting target area obtained through early geological survey, a geological model of the index metal relative quantization parameter three-dimensional spatial distribution rule of the prospecting target area is established by using data obtained through the drilling construction, and the model is used as a key prospecting target area, and then a pit exploration project is implemented to expose a vein; in the mineral resource detection method, the drilling project and the pit exploration project are implemented in steps, and the pit exploration project with higher cost is implemented on the basis of the data accumulated in the previous mineral exploration and mining, so that the prospecting cost is greatly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ore prospecting, in particular to a method for realizing mineral resource exploration by using spatial distribution rules of ore-forming metal elements. BACKGROUND

[0002] With the sustained and rapid growth of China's economy, the resource reserves of more than half of the 45 main minerals in the country are consumed at a rate greater than the growth rate. In particular, a number of old mines in the eastern and central regions have exhausted their resources and become crisis mines. It is an extremely urgent task to explore new resources in the surrounding or deep parts of old mines with market demand and resource potential, which has economic and social benefits.

[0003] From the perspective of exploration investment and new discoveries, the current situation and trends of global deep mineral exploration are analyzed. In recent years, the world's mineral exploration and mining trend has shown that the exploration and mining depth is increasing. Deep prospecting in known mining areas and search for concealed mines in unknown mining areas have attracted widespread attention. The role of ore-forming theory in guiding deep prospecting work is increasingly prominent. However, due to the limitations of existing ore-forming theory, the spatial distribution rules of many mineral resources occurring in the deep part of known mining areas are still lack of mature and reliable theoretical guidance. Therefore, drilling and pit exploration methods are usually used for exploration, resulting in low success rate and high cost.

[0004] Therefore, how to use the data accumulated from existing shallow prospecting and mining to summarize its rules for prospecting in the deep part or adjacent area of known mining areas has very important practical significance. However, due to the large scale of data accumulated from existing shallow prospecting and mining, and the complexity and non-obviousness of the rules contained therein, it is obviously a very difficult task to summarize the rules of accumulated data completely by manual work. However, with the rapid development of computer information technology, especially the rapid development of artificial intelligence, it has a very solid technical foundation to use computer to assist in deep mining of accumulated data from existing prospecting and mining and summarize the hidden rules. However, there is still no much experience to learn from in this direction in China. SUMMARY

[0005] In order to overcome the deficiencies in the prior art, the present application discloses a method for realizing mineral resource detection by using spatial distribution rules of metallogenic metal elements, which uses the data accumulated in existing mineral exploration and mining, first finds index metal relative quantization parameter indexes, and then establishes a geological sample model of the three-dimensional spatial distribution rules of the index metal relative quantization parameters by using a computer; drilling construction is performed on the prospecting target area obtained through previous geological investigation, a geological model of the three-dimensional spatial distribution rules of the index metal relative quantization parameters of the prospecting target area is established by using the data obtained through the drilling construction and by using the same parameters as those of the geological sample model, the spatial region indicated by the model is taken as a key prospecting target area, and then pit exploration engineering is implemented to expose the ore vein.

[0006] In order to realize the object of the present application, the present application adopts the following technical solution: a method for realizing mineral resource detection by using spatial distribution rules of metallogenic metal elements, based on the data accumulated in existing mineral exploration and mining, the content relationship between a certain metal element and other elements at different spatial points in an existing mining area is relatively quantized to obtain a plurality of relative quantization parameters Mi of the certain metal element; the plurality of relative quantization parameters Mi of the certain metal element are input into a computer with the coordinates of the three-dimensional spatial points to obtain a geological model of the three-dimensional spatial distribution rules of the relative quantization parameters Mi of the certain metal element; then the driving strength attribute of the line is assigned by using the ratio of the absolute value of the difference between the relative quantization parameters Mi of the two end points of the line to the spatial distance L of the line, and the driving vector attribute of the line is assigned in the direction in which the relative quantization parameters Mi of the two end points increase; then the spatial model of the explored or mined ore vein is placed into the established geological model of the three-dimensional spatial distribution rules of the relative quantization parameters Mi of the certain metal element; the line with the minimum driving strength attribute is deleted in sequence, and if the driving vector attributes of the remaining lines of a set proportion point to the closed region of the spatial model of the explored or mined ore vein, the metal corresponding to the relative quantization parameter Mi is the index metal related to the explored or mined ore vein, and the corresponding relative quantization parameter is the index metal relative quantization parameter MS.

[0007] Further, the drilling engineering is carried out on the exploration target area obtained by the previous geological survey, basic element analysis is carried out on the drilling core sampling, and a plurality of index metal relative quantization parameters MSi are calculated; the plurality of index metal relative quantization parameters MSi are input into the computer as the coordinates of the three-dimensional space points, and a three-dimensional space distribution rule geological model of the index metal relative quantization parameters MSi is obtained; then, the absolute value of the difference between the index metal relative quantization parameters MSi of the two end points of the line and the space distance L of the line is assigned to the driving strength attribute of the line, and the direction in which the index metal relative quantization parameters MSi of the two end points of the line increase is assigned to the driving vector attribute of the line; the line with the minimum driving strength attribute is sequentially deleted, and finally the space region pointed by the driving vector attribute of the remaining line in a set proportion is taken as the key exploration target area for the pit exploration engineering, and the ore vein is exposed.

[0008] Preferably, a mineral resource artificial intelligence analysis model is established; the index metal relative quantization parameters MS calculated by using the data accumulated in the existing mineral exploration and mining are used to establish a space distribution model of a plurality of existing mining areas based on the index metal relative quantization parameters MS and the spatial distribution model of the known ore veins, a learning sample library is established based on the plurality of space distribution models, and the mineral resource artificial intelligence analysis model is trained; the drilling engineering is carried out on the exploration target area obtained by the previous geological survey, basic element analysis is carried out on the drilling core sampling, and a plurality of index metal relative quantization parameters MSi are calculated; the plurality of index metal relative quantization parameters MSi are input into the computer as the coordinates of the three-dimensional space points, and a three-dimensional space distribution rule geological model of the index metal relative quantization parameters MSi is obtained; the model is input into the computer, the key exploration target area is delineated by the mineral resource artificial intelligence analysis model, the pit exploration engineering is carried out on the key exploration target area, and the ore vein is exposed.

[0009] Further, the three-dimensional space distribution rule geological model of the index metal relative quantization parameters MSi after the ore vein is exposed is modified, and the modified three-dimensional space distribution rule geological model of the index metal relative quantization parameters MSi is put into the learning sample library of the mineral resource artificial intelligence analysis model.

[0010] Further, the calculation formula of the metal element relative quantization parameter M is:

[0011] Mi=(Ma / (Mb+Mc+……))²,

[0012] wherein Ma, Mb, Mc, … are metal element grade indexes, and the subscript i is a space point serial number;

[0013] The driving strength calculation formula is:

[0014] β=|Mi-Mi´| / L,

[0015] Wherein, β is the driving strength, Mi, Mi' are the relative quantification parameter values of metal elements of two different spatial points, and L is the straight line distance of the two different spatial points of Mi and Mi'.

[0016] Further, the drilling core sampling interval is 0.5-1.0m, and the maximum sampling interval is not more than 1.5m.

[0017] Further, the basic element analysis includes Ag, Pb, Zn, Au, Cu, S and Fe; for the ore vein with significant gold mineralization, As (As2O3) should be supplemented; and other trace elements (such as Se, Co, etc.) can also be analyzed if necessary.

[0018] By using the technical scheme, the present application has the following beneficial effects: the accumulated data in the existing mineral exploration and mining are used to firstly find the index metal relative quantification parameter index, and then a geological sample model of the three-dimensional spatial distribution rule of the index metal relative quantification parameter is established by using a computer; drilling construction is performed on the exploration target area obtained by the previous geological survey, and a geological model of the three-dimensional spatial distribution rule of the index metal relative quantification parameter of the exploration target area is established by using the data obtained by the drilling construction, the space area indicated by the model is used as the key exploration target area, and then pit exploration engineering is implemented to expose the ore vein; in the mineral resource exploration method of the present application, the drilling engineering and the pit exploration engineering are implemented step by step, and the pit exploration engineering with higher cost is implemented more purposefully under the guidance of the accumulated data rule in the previous mineral exploration and mining, so that the exploration success rate is greatly improved, and the exploration cost is effectively controlled. DETAILED DESCRIPTION

[0019] The present application can be explained in detail by the following examples, and the purpose of the present application is to protect all technical improvements within the scope of the present application.

[0020] A method for mineral resource detection by using spatial distribution rule of metallogenic metal elements, based on the data accumulated in existing mineral exploration and mining, the content relationship between a metal element and other elements at different spatial points in an existing mining area is relatively quantified to obtain a plurality of relative quantization parameters Mi of the metal element, the calculation formula of which is Mi= (Ma / (Mb+Mc+…))², wherein Ma, Mb, Mc, … are metal element grade indexes, and the subscript i is a spatial point serial number; the plurality of relative quantization parameters Mi of the metal element are input into a computer with the coordinates of the three-dimensional spatial points to obtain a three-dimensional spatial distribution rule geological model of the relative quantization parameters Mi of the metal element; then, the absolute value of the difference between the relative quantization parameters Mi of the two endpoints of each line is divided by the spatial distance L of the line to assign a driving strength attribute β to the line, and the direction in which the relative quantization parameters Mi of the two endpoints increase is assigned a driving vector attribute to the line, the calculation formula of which is: β=|Mi-Mi´| / L, wherein β is the driving strength, Mi and Mi´ are the relative quantization parameter values of the metal element of the two different spatial points, and L is the straight-line distance between the two different spatial points Mi and Mi´; then, the proven or mined vein spatial model is placed into the three-dimensional spatial distribution rule geological model of the relative quantization parameters Mi of the metal element; the line with the minimum driving strength attribute is deleted in sequence, and if the driving vector attributes of the remaining lines of a set proportion point to the closed area of the proven or mined vein space, the metal corresponding to the relative quantization parameter Mi of the metal element is the index metal related to the proven or mined vein space, and the corresponding relative quantization parameter is the index metal relative quantization parameter MS;

[0021] The idea of looking for the relative quantization parameter MS of the index metal to establish the three-dimensional spatial distribution regularity geological model comes from the practical experience of the company's mineral exploration and mining: taking a certain mining area of the company as an example, the mining area is mainly composed of silver, lead and gold elements, the ore body shape is mainly stratoid and lenticular, followed by kidney-shaped and irregular; the ore body pinch-out along the strike is obvious, and the inclination is gentle, but the overall vein occurrence is steep; the boundary between the ore body and the surrounding rock is generally clear, which can be preliminarily defined by the naked eye; the mineralization is mainly galena, sphalerite, pyrite and chalcopyrite; the stratum sequence, vein scale, shape, occurrence and the distribution of lithology and lithofacies related to mineralization, the nature and occurrence of main structures, ore-controlling structural factors and the structural conditions of mineralization enrichment have certain correlation; but there is no obvious regularity between the basic element content, proportion and spatial position relationship of Ag, Pb, Zn, Au, Cu, S and Fe in the surrounding rock of the ore spot and the rock mass, so the data of Ag, Pb, Zn, Au, Cu, S and Fe in the surrounding rock of the ore spot and the rock mass accumulated in the existing mineral exploration and mining cannot provide reliable reference for the direct use of known mining area deep or adjacent area prospecting work, and the delineation of target exploration target area still depends on traditional technical means and methods, and then combined with drilling and pit exploration to accurately expose, so the exploration work has considerable blindness, which is the main reason for the low success rate and high cost of exploration; in the analysis of the accumulated data in the existing mineral exploration and mining, the exploration technology personnel believe that there should be some degree of relationship between the basic element content in the surrounding rock of the ore spot and the rock mass, but when quantizing, the data difference is extremely small, and the spatial distribution cannot show obvious regularity, so the accumulated data in the existing mineral exploration and mining cannot be directly used; but if the content of a certain metal element and its closely associated elements is relatively processed, the content quantization relationship between a certain metal element and its closely associated elements is significantly amplified, and after comparing the distance between the amplified content quantization relationship and the spatial position of multiple stages of structure, the spatial position also shows certain regularity change (but not absolute relationship); therefore, the method of constructing three-dimensional spatial distribution regularity geological model with metal element relative quantization parameter value is proposed, and the driving strength and driving vector (direction) attributes are assigned to the connection between any two points in the three-dimensional spatial distribution regularity geological model to facilitate the observation of the spatial variation of the metal element relative quantization parameter; when constructing the three-dimensional spatial distribution regularity geological model, the MICROMINE mining professional software is used;After deleting the connection with the minimum driving strength attribute in sequence (after deleting the connection with the minimum driving strength attribute each time, it is necessary to observe whether the driving vector attribute of the remaining connection appears regularity direction), when the driving vector attribute of the remaining connection has 60-70% proportion presenting regularity direction, it indicates that the content of a certain metal element and its closely associated elements is effectively relative to the content of the metal element, and the metal element is defined as the index metal; taking metal silver as an example, the relative quantization parameter calculation formula of the metal silver is Agsi= (Ag / (Pb+Zn))²; the driving strength calculation formula is β=|Agsi-Agsi´| / L; wherein Ag, Pb and Zn are in units of grams / ton, L is in units of meters, and Ags is dimensionless; it is additionally pointed out that the relative quantization parameter calculation formula of the metal silver is not unique, and there may be other relative quantization parameter calculation formulas with better calculation effect; the same situation also exists for other metals; it is further pointed out that: for the exploration of a certain metal, the index metal may not be the metal itself;

[0022] Still taking metal silver as an example, drill engineering is carried out on the exploration target area obtained by the previous geological investigation, basic element analysis is carried out on the drill core sampling, and a plurality of metal silver relative quantization parameters Agsi are calculated; the plurality of metal silver relative quantization parameters Agsi are input into the MICROMINE mining professional software as the coordinates of three-dimensional space points, and a three-dimensional space distribution geological model of the metal silver relative quantization parameter Agsi is obtained; then all the space points are connected, and then the driving strength β attribute and the driving vector attribute are assigned to all the connections, the connection with the minimum driving strength attribute is deleted in sequence, and finally when the driving vector attribute of the remaining connection has 60-70% proportion pointing to a certain area in the three-dimensional space, the operation of deleting the connection with the minimum driving strength attribute ends, and the space area pointed by the driving vector attribute of 60-70% connections is the key exploration target area, and pit exploration engineering is carried out on the key exploration target area, and the ore vein is exposed; this method greatly improves the success rate of pit exploration operation in the key exploration target area, and effectively controls the exploration cost;

[0023] In actual drilling operation, the drilling core sampling interval is 0.5-1.0m, and the maximum sampling interval is not more than 1.5m; the basic element analysis includes Ag, Pb, Zn, Au, Cu, S and Fe; for the ore vein with significant gold mineralization, As (As2O3) should be supplemented; if necessary, other trace elements (such as Se, Co, etc.) can also be analyzed.

[0024] The foregoing method for realizing mineral resource detection by using the spatial distribution rule of metallogenic metal elements is a method with full manual participation by using the data accumulated in existing mineral exploration and mining and MICROMINE mining professional software. In the foregoing method, the overall efficiency is relatively low due to large calculation workload and low automation degree, and therefore the finally obtained index metal relative quantification parameter three-dimensional spatial distribution geological model is not the optimal result. Therefore, the method can be improved by means of existing artificial intelligence technology. The specific improvement method is that the method in the early stage is still the method with manual participation, the index metal relative quantification parameter MS calculated by using the data accumulated in existing mineral exploration and mining is used to establish the spatial distribution model of the index metal relative quantification parameter MS and the spatial distribution model of the known ore veins in some existing mining areas, a learning sample library is established based on the spatial distribution model, a mineral resource artificial intelligence analysis model is trained by means of the Baidu PaddlePaddle artificial intelligence platform, drilling engineering is carried out on the exploration target area obtained by the geological survey in the early stage, basic element analysis is carried out on the drilling core sampling, and a plurality of index metal relative quantification parameters MSi are calculated. The plurality of index metal relative quantification parameters MSi are input into a computer in the form of the coordinates of three-dimensional spatial points to obtain the three-dimensional spatial distribution rule geological model of the index metal relative quantification parameters MSi. The three-dimensional spatial distribution rule geological model of the index metal relative quantification parameters MSi is analyzed by means of the mineral resource artificial intelligence to automatically delineate a key exploration target area, pit exploration engineering is carried out on the key exploration target area, and an ore vein is exposed. The three-dimensional spatial distribution rule geological model of the index metal relative quantification parameters MSi after the exposure of the ore vein is corrected, and the corrected three-dimensional spatial distribution rule geological model of the index metal relative quantification parameters MSi is put into the learning sample library of the mineral resource artificial intelligence analysis model.

[0025] The part not described in detail in the present application is prior art.

Claims

1. A method for mineral resource exploration using the spatial distribution patterns of ore-forming metal elements, characterized by: Based on the data accumulated in the existing mineral exploration and mining, the content relationship between a certain metal element and other elements at different spatial points in the existing mining area is relatively quantified to obtain a plurality of relative quantization parameters Mi of the certain metal element; the plurality of relative quantization parameters Mi of the certain metal element are input into a computer with the coordinates of the three-dimensional spatial points to obtain a three-dimensional spatial distribution regularity geological model of the relative quantization parameters Mi of the certain metal element; then, the absolute value of the difference between the relative quantization parameters Mi of the two endpoints of each line is divided by the spatial distance L of the line to assign a driving strength attribute to the line, and the direction in which the relative quantization parameters Mi of the two endpoints of the line increase is assigned a driving vector attribute to the line; then, the spatial closed area of the proven or mined vein is placed in the three-dimensional spatial distribution regularity geological model of the relative quantization parameters Mi of the certain metal element; the line with the smallest driving strength attribute is deleted in sequence, and if the driving vector attributes of the remaining lines of a certain proportion point to the spatial closed area of the proven or mined vein, it indicates that the relative quantization parameter Mi of the metal element is effective, and is defined as an index metal related to the proven or mined vein, and the corresponding relative quantization parameter is an index metal relative quantization parameter MS; The calculation formula of the metal element relative quantization parameter M is: Mi=(Ma / (Mb+Mc+……))², wherein Ma, Mb, Mc, … are metal element grade indexes, and the subscript i is a spatial point serial number; The driving strength calculation formula is: β=|Mi-Mi´| / L, wherein β is the driving strength, Mi and Mi' are the relative quantization parameter values of the metal element at two different spatial points, and L is the straight-line distance between the two different spatial points of Mi and Mi'.

2. The method for detecting mineral resources by using the spatial distribution of ore-forming metal elements according to claim 1, characterized in that: The drilling engineering is carried out on the prospecting target area obtained by the previous geological survey, the basic element analysis is carried out on the drilling core sampling, and a plurality of index metal relative quantization parameters MSi are calculated; the plurality of index metal relative quantization parameters MSi are input into a computer with the coordinates of the three-dimensional spatial points to obtain a three-dimensional spatial distribution regularity geological model of the index metal relative quantization parameters MSi; then, the absolute value of the difference between the index metal relative quantization parameters MSi of the two endpoints of each line is divided by the spatial distance L of the line to assign a driving strength attribute to the line, and the direction in which the index metal relative quantization parameters MSi of the two endpoints of the line increase is assigned a driving vector attribute to the line; The line with the smallest driving strength attribute is deleted in sequence, and finally the spatial area pointed by the driving vector attributes of the remaining lines of a certain proportion is taken as the key prospecting target area for the pit exploration engineering, and the vein is exposed.

3. The method for detecting mineral resources by using the spatial distribution of ore-forming metal elements according to claim 2, characterized in that: Establishing a mineral resource artificial intelligence analysis model; using the index metal relative quantification parameter MS calculated from the data accumulated in the existing mineral exploration and mining to establish a correlation model of the three-dimensional spatial distribution of the existing several mining areas and the known spatial distribution of the ore veins, and using the correlation model of the three-dimensional spatial distribution of the several ore veins to establish a learning sample library to train the mineral resource artificial intelligence analysis model; drilling engineering is carried out on the exploration target area obtained by the previous geological survey, basic element analysis is carried out on the drilling core sampling, and the index metal relative quantification parameter MSi is calculated; the index metal relative quantification parameter MSi is input into the computer as the coordinates of the three-dimensional spatial point to obtain the three-dimensional spatial distribution rule geological model of the index metal relative quantification parameter MSi, the model is input into the mineral resource artificial intelligence analysis model, and the key exploration target area is delineated by the mineral resource artificial intelligence analysis model, pit exploration engineering is carried out on the key exploration target area, and the ore veins are exposed.

4. The method for detecting mineral resources by using the spatial distribution of ore-forming metal elements according to claim 3, characterized in that: The three-dimensional spatial distribution rule geological model of the index metal relative quantification parameter MSi after the ore veins are exposed is corrected, and the corrected three-dimensional spatial distribution rule geological model of the index metal relative quantification parameter MSi is put into the learning sample library of the mineral resource artificial intelligence analysis model.

5. The method for detecting mineral resources by using the spatial distribution of ore-forming metal elements according to claim 3, characterized in that: The drilling core sampling interval is 0.5-1.0 m.

6. The method for detecting mineral resources by using the spatial distribution of ore-forming metal elements according to claim 3, characterized in that: The basic element analysis includes Ag, Pb, Zn, Au, Cu, S and Fe.