A method and system for quickly delineating tin ore prospecting target areas

Abstract

The present invention provides a method and system for quickly delineating prospecting target areas for tin ore, which relates to the field of earth information science and technology. First, the existing tin ore is set as the target area, and samples are evenly taken within each target area, the physical properties of the samples are recorded, and a comprehensive index of the samples is generated. Then, the geochemical data of the target area is obtained, the relative abundance values of the elements in the samples are calculated, and the relative abundance values of the elements and the comprehensive index are input into the trained spatial distribution model to generate the spatial autocorrelation index of each sample. The target areas with high tin ore potential are screened according to the sorting of the spatial autocorrelation indexes of the samples. This method significantly improves the identification efficiency of tin ore potential areas and provides a reliable basis for the sustainable development of mineral resources.

Description

Technical Field

[0001] The present invention relates to the field of earth information science and technology, and specifically provides a method and system for quickly delineating tin ore prospecting target areas. Background Art

[0002] In the field of mineral exploration, as an important metal resource, the detection and evaluation of tin ore have always been the focus of geological research. Traditional tin ore exploration methods mainly rely on geological surveys and empirical judgments, usually by analyzing known ore deposits and evaluating the geological characteristics of surrounding areas. Although these methods have achieved certain results in history, due to their subjectivity and limitations, they often cannot fully reveal the true situation of potential mining areas.

[0003] Traditional exploration means, such as empirical discrimination and limited geological surveys, may lead to an incomplete evaluation of potential mining areas. At the same time, many methods lack in-depth analysis of spatial distribution characteristics and cannot effectively utilize geographic information systems and data mining technologies, thus limiting the accurate identification of tin ore potential. In addition, traditional methods are less efficient in data collection and analysis and are difficult to obtain high-quality exploration results in a short time. Therefore, it is particularly important to develop a systematic, fast and efficient method for delineating tin ore target areas, which can provide strong technical support for the sustainable development of mineral resources.

[0004] The existing technologies have the following deficiencies:

[0005] With the development of technology, modern exploration technologies have gradually introduced advanced means such as geochemical analysis and geographic information systems. Through detailed analysis of soil and rock samples, researchers can obtain more accurate element distributions and rich geological information. However, the existing technologies still face some challenges, such as low data processing efficiency, uneven sample collection, one-sided analysis of the physical properties of soil and rock samples, and insufficient analysis of spatial distribution characteristics. These factors limit the rapid identification and evaluation of potential tin ore target areas.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for quickly delineating tin ore prospecting target areas to solve the problems raised in the above background art.

[0008] A method for quickly delineating tin ore prospecting target areas, the specific steps include:

[0009] Step 1: Set the existing tin mine as the target area, evenly calibrate N sampling points in each target area, dig the surface soil and rocks of the sampling points as samples, and record the geographical location, rock type, ore deposit type and geomorphic data of the samples;

[0010] Step 2: Analyze the topographic data of the sample, generate the terrain complexity index, and combine the rock type and deposit type of the sample to obtain the comprehensive index of the sample;

[0011] Step 3: Obtaining geochemical data of samples in the target area by laser ablation-inductively coupled plasma mass spectrometry technology, wherein the geochemical data of samples in the target area include the concentrations of tin, copper, lead, and zinc elements, and calculating the relative abundance of each element;

[0012] Step 4: Calculate the relative abundance mean and standard deviation of all samples, standardize the relative abundance of each element, integrate the standardized data into a data set, and divide it into a training set and a validation set;

[0013] Step 5: Analyze the spatial distribution based on the geographic information system, build a spatial distribution model, input the relative abundance of each element in the training set, combine the comprehensive indicators of the sample, obtain the spatial autocorrelation index of each element as a label, and verify the spatial distribution model;

[0014] Step 6: Select the area to be tested based on the geomorphic and topographic data of the target area, obtain samples from the area to be tested and collect relevant data to input into the spatial distribution model, sort the predicted spatial autocorrelation indicators, and screen the target area with high tin mining potential.

[0015] Furthermore, an area where tin ore distribution is known is selected, and the boundary of the target area is drawn using geographic information system software. Systematic sampling is performed using a grid sampling method, and surface soil and rocks at a depth of 0-10 cm at each sampling point are excavated as samples. 50 grams of samples are collected, and the position of the Nth sample is calibrated as N(x, y), where x is the latitude of the position, and y is the longitude of the position. A rock type index is determined based on the rock type, and a deposit type index is determined based on the mineral type. The rock types include granite, metamorphic rock, volcanic rock, quartzite and sedimentary rock. The deposit types include hydrothermal deposits, magmatic deposits, metamorphic deposits, sedimentary deposits, volcanic rock deposits and placer deposits. The geomorphological data include contour lines and terrain line data of the area. The specific data of the contour lines are the density of the contour lines, the height of the highest point and the height of the lowest point corresponding to the contour lines, and the specific data of the terrain lines are the density of the terrain lines, the height of the highest point and the height of the lowest point corresponding to the terrain lines.

[0016] Furthermore, the logic for obtaining the comprehensive indicators of the samples is as follows:

[0017] The formula for obtaining the contour density is as follows:

[0018]

[0019] where D i is the contour density of the area where the i-th sample is located, M i is the number of contour lines in the area where the i-th sample is located, and L i is the perimeter of the area where the i-th sample is located;

[0020] The formula for obtaining the topographic line density is as follows:

[0021]

[0022] where F i represents the topographic line density of the area where the i-th sample is located, LF i is the total length of the topographic lines in the area where the i-th sample is located, and A i is the area of the area where the i-th sample is located;

[0023] The formula for obtaining the topographic complexity index is as follows:

[0024]

[0025] where S i is the topographic complexity index of the area where the i-th sample is located, is the height of the highest point of the topographic lines in the area where the i-th sample is located, is the height of the lowest point of the topographic lines in the area where the i-th sample is located, is the height of the highest point of the contour lines in the area where the i-th sample is located, is the height of the lowest point of the contour lines in the area where the i-th sample is located;

[0026] The formula for obtaining the comprehensive index of the sample is as follows:

[0027] Q i = ω1 * R q i + ω2 * O q i + ω3 * S i

[0028] where Q i is the comprehensive index of the i-th sample, R q i is the rock type index of the i-th sample, O q i is the ore deposit type index of the i-th sample, S iis the terrain complexity index of the i-th sample, ω1, ω2, and ω3 are weight coefficients, ω2 > ω1 > ω3 and ω1 + ω2 + ω3 = 1.

[0029] Furthermore, the logic for calculating the relative abundance of each element is as follows:

[0030] Using a laser ablation instrument, the surface of the sample is ablated by a laser beam to generate an aerosol. The aerosol is introduced into an inductively coupled plasma through a conduit, ionized at high temperature, and the concentration of the element is calculated by measuring the mass-to-charge ratio of different ions and according to the specific peak intensity; the standard curve method is used for quantitative analysis of the element concentration. By measuring a standard sample with a known concentration, the relationship between the concentration and the corresponding mass spectrometry signal intensity is established. The specific formula based on the standard curve is:

[0031] Y = δX + b

[0032] where Y is the mass spectrometry signal intensity, X is the known concentration, δ is the slope, representing the signal change caused by per unit concentration change, and b is the intercept, representing the signal at a concentration of 0;

[0033] The specific formula for obtaining the relative abundance of each element is:

[0034]

[0035] where the tin element, copper element, lead element, and zinc element in the sample are labeled as 1, 2, 3, 4 respectively, and the concentration of the a-th element in the i-th sample is calibrated as a ∈ [1, 4], is the relative abundance of the a-th element in the i-th sample.

[0036] Furthermore, the specific logic for normalizing the relative abundances of each element is as follows:

[0037] The formula for obtaining the mean value of the relative abundances of each element is:

[0038]

[0039] where is the mean value of the relative abundances of the elements in the i-th sample, n is the number of elements, which is 4 here;

[0040] The formula for obtaining the standard deviation of the relative abundance is:

[0041]

[0042] where σ i is the standard deviation of the relative abundance of the i-th sample, n is the number of elements, which is 4 here;

[0043] The formula based on which the relative abundances of each element are standardized is as follows:

[0044]

[0045] wherein, is the value after standardizing the relative abundance of element a in the i-th sample.

[0046] Furthermore, the formula based on which the spatial autocorrelation index of each element is obtained is as follows:

[0047]

[0048] wherein, I i is the spatial autocorrelation index of the i-th sample, N is the number of samples, W is the total weight, η1 is the spatial weight of the relative abundance, η2 is the spatial weight of the comprehensive index, and η1 > η2 > 0.

[0049] Furthermore, the logic based on which the target areas with high potential for tin ore are screened is as follows:

[0050] By comparing with the contour density, contour height difference, terrain line density, and terrain line height difference of the target area, if the total difference is lower than the preset similarity threshold, then this area is set as the area to be measured. If the total difference is higher than the preset similarity threshold, it is determined that this area is not an area to be measured;

[0051] Collect samples from the area to be measured, divide them into N portions, calculate the comprehensive index of the samples to be measured and the relative abundance values of the elements, and input them into the spatial distribution model. Sort the predicted spatial autocorrelation indices, and mark the areas where the samples corresponding to the top three sorted spatial autocorrelation indices are located as the target areas with high potential for tin ore.

[0052] The present invention further provides a system for quickly delineating tin ore prospecting target areas. The system for quickly delineating tin ore prospecting target areas is used to implement the method for quickly delineating tin ore prospecting target areas as described above, and includes:

[0053] A sample collection module, which is used to set the existing tin ore as the target area, uniformly mark N sampling points within each target area, excavate the surface soil and rocks of the sampling points as samples, and record the geographical location, rock type, ore deposit type, and geomorphic terrain data of the samples;

[0054] A geomorphic feature analysis module, which is used to analyze the geomorphic terrain data of the samples, generate a terrain complexity index, and combine the rock type and ore deposit type of the samples to obtain the comprehensive index of the samples;

[0055] A geochemical data acquisition module is used to acquire geochemical data of samples in the target area by laser ablation-inductively coupled plasma mass spectrometry technology, wherein the geochemical data of samples in the target area include the concentrations of tin, copper, lead and zinc elements, and calculate the relative abundance of each element;

[0056] The data preprocessing module is used to calculate the relative abundance mean and standard deviation of all samples, standardize the relative abundance of each element, integrate the standardized data into a data set, and divide it into a training set and a validation set;

[0057] The spatial distribution model building module is used to analyze spatial distribution based on the geographic information system, build a spatial distribution model, input the relative abundance of each element in the training set, combine the comprehensive indicators of the sample, obtain the spatial autocorrelation index of each element as a label, and verify the spatial distribution model;

[0058] The potential target area screening module is used to screen out the area to be tested based on the geomorphic and topographic data of the target area, obtain samples from the area to be tested and collect relevant data to input into the spatial distribution model, sort the predicted spatial autocorrelation indicators, and screen the target area with high tin ore potential.

[0059] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0060] This method significantly improves the efficiency and accuracy of tin ore target area delineation through a systematic sample collection and analysis process. Geochemical data, structural geomorphology and topographic index, rock type and other comprehensive indicators obtained by laser ablation-inductively coupled plasma mass spectrometry can more comprehensively reflect the mineral potential of the target area. Through the establishment and verification of the spatial distribution model, scientific identification of high-potential target areas is achieved, avoiding excessive reliance on experience in traditional methods. In addition, the spatial analysis capabilities of the geographic information system can be used to deeply explore the spatial autocorrelation between samples and improve the accuracy of prediction. This not only provides data support for rapid decision-making, but also lays a solid foundation for subsequent exploration work, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0062] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0064] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0065] Embodiment:

[0066] Please refer to Figure 1 , the present invention provides a method for quickly delineating tin ore prospecting target areas, and the specific steps include:

[0067] Step 1: Set the existing tin ore as the target area, uniformly mark N sampling points in each target area, excavate the surface soil and rocks of the sampling points as samples, and record the geographical locations, rock types, ore deposit types and geomorphic topographic data of the samples;

[0068] In this embodiment, an area with known tin ore distribution is selected. Using geographic information system software, the boundary of the target area is drawn. The systematic sampling is carried out by the grid sampling method. The surface soil and rocks at a depth of 0-10 cm at each sampling point are excavated as samples, and 50 grams of samples are collected. The position of the Nth sample is marked as N(x,y), where x is the latitude of this position and y is the longitude of this position. The rock type index is determined based on the rock type, and the ore deposit type index is determined based on the mineral deposit type. The value range of the rock type index is [1,5]. The rock types specifically include granite, metamorphic rock, volcanic rock, quartzite and sedimentary rock. And according to this sorting, the rock type index is from high to low. The rock type index of granite is marked as 4. Since granite has a relatively high correlation with many ore deposits, especially tin ore, a higher index value is given. The rock type index of metamorphic rock is marked as 3. Metamorphic rock also has a certain correlation with ore deposits, but it may be less than that of granite. The rock type index of volcanic rock is marked as 3. Volcanic rock is related to ore deposits in some cases, especially ore deposits related to igneous rocks. The rock type index of quartzite is marked as 3. Quartzite itself may not be directly related to ore deposits, but it has its importance in specific geological environments. The rock type index of sedimentary rock is marked as 1. The correlation between sedimentary rock and ore deposits is relatively low, but it may also contain ore deposits in some specific environments, so the lowest index value is given. The value range of the ore deposit type index is [1,5]. The ore deposit types include hydrothermal deposit, magmatic deposit, metamorphic deposit, sedimentary deposit, volcanic rock deposit, placer deposit. And according to this sorting, the ore deposit type index is from high to low. The ore deposit type index of the hydrothermal deposit is marked as 5. The hydrothermal deposit is one of the important metallogenic environments of tin ore, so the ore deposit type index is the highest. The ore deposit type index of the magmatic deposit is marked as 4. Although tin ore is mainly formed by hydrothermal action, magmatic deposits may also contain tin ore, especially in magmatic activities related to granite. The ore deposit type index of the metamorphic deposit is marked as 3. Metamorphism can re-concentrate and enrich minerals. Although the metamorphic deposit is not the main genetic environment of tin ore, there is still a certain possibility. The ore deposit type index of the sedimentary deposit is marked as 3. Although tin ore is rarely directly formed in the sedimentary environment, due to sedimentation can redistribute the tin minerals in the hydrothermal deposit, so there is still a certain correlation. The ore deposit type index of volcanic rock is marked as 2. Although there may be some metal ores in volcanic rock-related ore deposits, the possibility of the existence of tin ore in this environment is relatively low. The ore deposit type index of the placer deposit is marked as 1. Placer deposits are mainly formed by the enrichment of minerals under mechanical action. Although there may be tin minerals in some places, the overall correlation is the lowest. The geomorphic and topographic data include the contour line and topographic line data of this area. The specific data of the contour line are the contour line density, the height of the highest point corresponding to the contour line and the height of the lowest point. The specific data of the topographic line are the topographic line density, the height of the highest point corresponding to the topographic line and the height of the lowest point.

[0069] Tin ores are usually associated with specific rock types, such as granite, metamorphic rocks, and certain types of volcanic rocks. These rocks tend to be rich in tin minerals, such as cassiterite, and have undergone special geological processes during their formation, such as hydrothermal processes or metamorphism. These processes can promote the enrichment of tin elements and form economically exploitable tin deposits. The rock type not only affects the distribution and concentration of minerals but also reveals the potential ore deposit formation mechanism. By understanding the characteristics of different rock types, researchers can better judge the occurrence conditions of tin ores and the possible mineralization processes.

[0070] In the area where tin ores are located, they are often associated with granite, especially with the ore deposits formed by hydrothermal processes in granite and its intrusions, usually with relatively high contents. Followed by metamorphic rocks, quartzite, sedimentary rocks, and volcanic rocks.

[0071] The ore deposit type helps identify the characteristics and distribution patterns of mineral resources in a specific area. Different types of ore deposits have significant differences in the mineralization process, mineral assemblage, and their economic value. By analyzing the ore deposit type, it can effectively guide the exploration strategy, focus on the most promising areas, and thus improve the success rate and efficiency of ore prospecting. Therefore, the collection of rock type and ore deposit type provides an important basis for subsequent exploration decision-making and resource assessment.

[0072] Hydrothermal ore deposits are the most common type of tin ore deposits, which are closely related to intrusive rock bodies such as granite. The formation of tin ores mainly depends on hydrothermal processes. Due to the action of water flow, tin ore minerals (such as cassiterite) are easily transported and deposited from their original places. Therefore, placer deposits are also an important source of tin ores. Followed by metamorphic ore deposits, sedimentary ore deposits, volcanic rock ore deposits, and magmatic ore deposits.

[0073] Tin ore areas tend to be located in mountainous or hilly regions. Therefore, the contour lines may be relatively dense, indicating a large topographic relief. Due to its association with geological structures (such as folds, faults), the highest point in the tin ore area may be relatively high, while the lowest point may be relatively low, forming an obvious height difference. In contrast, in some plains or sedimentary basins where there are no tin ores, the contour lines are relatively sparse, indicating a relatively flat terrain, and the height difference between the highest point and the lowest point may be relatively small. Therefore, it is necessary to record the topographic lines and contour line data of the target area to provide a basis for subsequent screening of high-potential ore target areas.

[0074] Step 2: Analyze the geomorphic topographic data of the samples, generate a topographic complexity index, and combine the rock type and ore deposit type of the samples to obtain the comprehensive index of the samples;

[0075] In this embodiment, the logic for obtaining the comprehensive index of the samples is as follows:

[0076] The formula for obtaining the contour line density is:

[0077]

[0078] Among them, D i is the contour density of the area where the i-th sample is located, M i is the number of contour lines in the area where the i-th sample is located, L i is the perimeter of the area where the i-th sample is located;

[0079] The formula for obtaining the terrain line density is:

[0080]

[0081] Among them, F i represents the terrain line density of the area where the i-th sample is located, LF i is the total length of the terrain lines in the area where the i-th sample is located, A i is the area of the area where the i-th sample is located;

[0082] The formula for obtaining the terrain complexity index is:

[0083]

[0084] Among them, S i is the terrain complexity index of the area where the i-th sample is located, is the height of the highest point of the terrain lines in the area where the i-th sample is located, is the height of the lowest point of the terrain lines in the area where the i-th sample is located, is the height of the highest point of the contour lines in the area where the i-th sample is located, is the height of the lowest point of the contour lines in the area where the i-th sample is located;

[0085] The formula for obtaining the comprehensive index of the sample is:

[0086] Q i = ω1 * R q i + ω2 * O q i + ω3 * S i

[0087] Among them, Q i is the comprehensive index of the i-th sample, R q i is the rock type index of the i-th sample, O q i is the ore deposit type index of the i-th sample, S i is the terrain complexity index of the i-th sample, ω1, ω2, ω3 are weight coefficients, ω2 > ω1 > ω3 and ω1 + ω2 + ω3 = 1.

[0088] The dependent variable Q iReflects the comprehensive mineral potential of the i-th sample. By comprehensively considering factors such as rock type, deposit type, and terrain complexity, a comprehensive score is obtained to guide prospecting decisions. The rock type index reflects the geological background of the sample, directly affecting the abundance and types of minerals. Different rock types may imply different metallogenic environments. The rock type index is positively correlated with the comprehensive index. The higher the rock type index, the relatively larger the comprehensive index, indicating a greater potential for the target area to be a tin ore target area. The deposit type index characterizes the deposit type in the area where the sample is located, determines the characteristics of the regional mineral resources, and is positively correlated with the comprehensive index. The higher the deposit type index, the relatively larger the comprehensive index, indicating a greater possibility for the target area to be a tin ore target area. The terrain complexity index is also positively correlated with the comprehensive index. Relatively speaking, the terrain complexity in the tin ore area is more complex than that in the non-tin ore area. The higher the terrain complexity index, the larger the comprehensive index value. The deposit type directly affects the formation and distribution of minerals. The formation of tin ore is closely related to the deposit type, so the weight of the deposit type is the largest. The rock type also has an important impact on the formation of tin ore, especially as tin ore is usually associated with certain specific rock types (such as granite). However, compared with the deposit type, the rock type may have a relatively smaller impact on the prospecting target area. Although the terrain complexity may affect the exposure and mining of ore bodies, in the metallogenic environment of tin ore, the terrain factor is usually not the most important influencing factor, so its weight should be relatively small. ω1 + ω2 + ω3 = 1. This constraint ensures that the comprehensive index is a normalized index, ensuring that the total contribution of the weighted combination of the three factors is 100%.

[0089] Step 3: Obtain the geochemical data of the samples in the target area through laser ablation-inductively coupled plasma mass spectrometry technology. The geochemical data of the samples in the target area include the concentrations of elements such as tin, copper, lead, and zinc, and calculate the relative abundance of each element;

[0090] In this embodiment, the logic for calculating the relative abundance of each element is as follows:

[0091] Use a laser ablation instrument to ablate the surface of the sample through a laser beam to generate aerosol. The aerosol is introduced into the inductively coupled plasma through a conduit and ionized at high temperature. By measuring the mass-to-charge ratio of different ions and calculating the element concentration based on the specific peak intensity; use the standard curve method for quantitative analysis of element concentration. By measuring standard samples with known concentrations, establish the relationship between concentration and the corresponding mass spectrometry signal intensity. The specific formula based on the standard curve is:

[0092] Y = δX + b

[0093] Where Y is the mass spectrometry signal intensity, X is the known concentration, δ is the slope, representing the signal change caused by per unit concentration change, and b is the intercept, representing the signal at a concentration of 0;

[0094] The specific formula for obtaining the relative abundances of each element is as follows:

[0095]

[0096] Among them, the tin element, copper element, lead element, and zinc element in the sample are labeled as 1, 2, 3, and 4 respectively, and the concentration of the ath element in the ith sample is calibrated as a ∈ [1, 4], which is the relative abundance of the ath element in the ith sample.

[0097] Laser ablation-inductively coupled plasma mass spectrometry technology can detect elements at very low concentrations, has high sensitivity, can improve accurate element abundance data, is applicable to the analysis of geological, mineral, and environmental samples, and this technology can simultaneously analyze multiple elements in a sample, can obtain relative concentrations and abundances in the same experiment, saves time and resources, and also avoids the interference of human factors.

[0098] The abundances and relative concentrations of different elements can provide important information about the metallogenic environment. Tin deposits are usually related to specific rock types, ore deposit types, and metallogenic processes. By analyzing the distribution of elements in the sample, the possible metallogenic environment can be inferred. The analysis of relative concentrations and abundances can reveal the characteristics of the mineral assemblage and help identify potential mineralized areas. Elements that may be associated with tin deposits or related to the tin metallogenic process can be more effectively located in the target area by detecting their relative abundances.

[0099] Step 4: Calculate the mean and standard deviation of the relative abundances of all samples, standardize the relative abundances of each element, integrate the standardized data into a data set, and divide it into a training set and a validation set;

[0100] In this embodiment, the specific logic for standardizing the relative abundances of each element is as follows:

[0101] The formula for obtaining the mean of the relative abundances of each element is as follows:

[0102]

[0103] Among them, is the mean of the relative abundances of the element in the ith sample, n is the number of elements, which is 4 here;

[0104] The formula for obtaining the standard deviation of the relative abundances is as follows:

[0105]

[0106] Among them, σ iis the standard deviation of the relative abundance of the i-th sample, n is the number of elements, which is 4 here;

[0107] The formula for normalizing the relative abundances of each element is:

[0108]

[0109] where is the value after normalizing the relative abundance of element a in the i-th sample.

[0110] There may be large differences in the order of magnitude of the abundance values of different elements in the sample. Normalization can convert these data to the same scale, making the comparison between different elements more reasonable and effective. In addition, normalization can help remove the influence of background noise, making the signal clearer, thereby improving the reliability of the data and the effectiveness of the analysis.

[0111] Step 5: Analyze the spatial distribution based on the geographic information system, construct a spatial distribution model, input the relative abundances of each element in the training set, combine the comprehensive indicators of the samples, obtain the spatial autocorrelation index of each element as a label, and verify the spatial distribution model;

[0112] In this embodiment, the formula for obtaining the spatial autocorrelation index of each element is:

[0113]

[0114] where I i is the spatial autocorrelation index of the i-th sample, N is the number of samples, W is the total weight, η1 is the spatial weight of the relative abundance, η2 is the spatial weight of the comprehensive indicator, η1>η2>0, and the size of W depends on the number of samples N and the specific values of the weights of each sample. The relative abundance of the sample has a greater weight in the spatial aggregation effect and can better reflect the content of cassiterite in the sample.

[0115] This spatial autocorrelation index can help identify the spatial distribution model of each element. By analyzing the spatial autocorrelation of the element, its aggregation degree in the geological body can be judged, which helps to understand the formation mechanism and distribution characteristics of the ore body. High autocorrelation values usually indicate the spatial aggregation of the element, which may indicate the existence of a mineralized area. By identifying these areas, potential ore prospecting target areas can be determined more effectively, improving the exploration efficiency. And the spatial autocorrelation index can help identify the outliers of the element abundances in the sample, and these outliers may indicate the existence of the ore body or important information in the mineralization process.

[0116] Step 6: Screen out the area to be measured according to the geomorphic and topographic data of the target area, obtain samples of the area to be measured, collect relevant data and input them into the spatial distribution model, sort the predicted spatial autocorrelation indexes, and screen out the target areas with high tin ore potential.

[0117] In this embodiment, the logic for screening out the target areas with high tin ore potential is as follows:

[0118] By comparing with the contour density, contour height difference, topographic line density and topographic line height difference of the target area, if the total difference is lower than the preset similarity threshold, the area is set as the area to be measured; if the total difference is higher than the preset similarity threshold, the area is judged as a non-area to be measured.

[0119] Collect samples of the area to be measured, divide them into N parts, calculate the comprehensive index of the samples to be measured and the relative abundance values of the elements, input them into the spatial distribution model, sort the predicted spatial autocorrelation indexes, and mark the areas where the samples corresponding to the top three spatial autocorrelation indexes are located as the target areas with high tin ore potential.

[0120] Input the calculated element abundance values into the spatial distribution model, and a spatial distribution map of the area can be generated. These models are based on the geological background and the spatial characteristics of element abundances and can effectively predict potential mineralized areas. By sorting the predicted spatial autocorrelation indexes, areas where element abundances are highly concentrated in space can be identified. High autocorrelation values usually mean that the element abundances in the area are higher than those in the surrounding areas, indicating the potential for the existence of tin ore.

[0121] Please refer to Figure 2 , the present invention also provides a system for quickly delineating tin ore prospecting target areas. The system for quickly delineating tin ore prospecting target areas is used to implement the above method for quickly delineating tin ore prospecting target areas, and includes:

[0122] A sample collection module, which is used to set existing tin ore as the target area, uniformly mark N sampling points in each target area, excavate the surface soil and rocks of the sampling points as samples, and record the geographical location, rock type, ore deposit type and geomorphic and topographic data of the samples;

[0123] A geomorphic feature analysis module, which is used to analyze the geomorphic and topographic data of the samples, generate a terrain complexity index, and combine the rock type and ore deposit type of the samples to obtain the comprehensive index of the samples;

[0124] A geochemical data acquisition module, which is used to obtain the geochemical data of the target area samples by laser ablation-inductively coupled plasma mass spectrometry technology. The geochemical data of the target area samples include the concentrations of tin, copper, lead and zinc elements, and calculate the relative abundance of each element;

[0125] The data preprocessing module is used to calculate the relative abundance mean and standard deviation of all samples, standardize the relative abundance of each element, integrate the standardized data into a data set, and divide it into a training set and a validation set;

[0126] The spatial distribution model building module is used to analyze spatial distribution based on the geographic information system, build a spatial distribution model, input the relative abundance of each element in the training set, combine the comprehensive indicators of the sample, obtain the spatial autocorrelation index of each element as a label, and verify the spatial distribution model;

[0127] The potential target area screening module is used to screen out the area to be tested based on the geomorphic and topographic data of the target area, obtain samples from the area to be tested and collect relevant data to input into the spatial distribution model, sort the predicted spatial autocorrelation indicators, and screen the target area with high tin ore potential.

[0128] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0129] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0130] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for quickly delineating prospecting target areas for tin ore deposits, characterized in that, The specific steps include: Step 1: Set the existing tin mine as the target area, evenly calibrate N sampling points in each target area, dig the surface soil and rocks of the sampling points as samples, and record the geographical location, rock type, ore deposit type and geomorphic data of the samples; Step 2: Analyze the geomorphic data of the target area, generate a terrain complexity index, and combine the rock type and deposit type of the sample to obtain a comprehensive index of the sample; Step 3: Obtaining geochemical data of samples in the target area by laser ablation-inductively coupled plasma mass spectrometry technology, wherein the geochemical data of samples in the target area include the concentrations of tin, copper, lead, and zinc elements, and calculating the relative abundance of each element; Step 4: Calculate the relative abundance mean and standard deviation of all samples, standardize the relative abundance of each element, integrate the standardized data into a data set, and divide it into a training set and a validation set; Step 5: Analyze the spatial distribution based on the geographic information system, build a spatial distribution model, input the relative abundance of each element in the training set, combine the comprehensive indicators of the sample, obtain the spatial autocorrelation index of each element as a label, and verify the spatial distribution model; Step 6: Screen out the area to be tested based on the geomorphic and topographic data of the target area, obtain samples from the area to be tested and collect relevant data to input into the spatial distribution model, sort the predicted spatial autocorrelation indicators, and screen the target area with high tin ore potential; Select an area where tin ore is known to be distributed, use geographic information system software to draw the boundary of the target area, use grid sampling method to perform systematic sampling, dig 0-10 cm deep surface soil and rock at each sampling point as samples, collect 50 grams of samples, calibrate the position of the Nth sample as N(x,y), x is the latitude of the position, y is the longitude of the position, determine the rock type index based on the rock type, and determine the deposit type index based on the mineral type, the rock types include granite, metamorphic rock, volcanic rock, quartzite and sedimentary rock, the deposit types include hydrothermal deposits, magmatic deposits, metamorphic deposits, sedimentary deposits, volcanic rock deposits, and placer deposits, the geomorphological terrain data include contour lines and terrain line data of the area, the specific data of the contour lines are the density of the contour lines, the height of the highest point and the height of the lowest point corresponding to the contour lines, and the specific data of the terrain lines are the density of the terrain lines, the height of the highest point and the height of the lowest point corresponding to the terrain lines; The formula for obtaining the contour density is: Among them, D i is the contour density of the area where the i-th sample is located, M i is the number of contour lines in the area where the i-th sample is located, L i is the perimeter of the area where the i-th sample is located; The formula for obtaining terrain line density is: Among them, F i represents the terrain line density of the area where the i-th sample is located, LF i is the total length of the terrain lines in the area where the i-th sample is located, A i is the area of the area where the i-th sample is located; The formula for obtaining the terrain complexity index is: Among them, S i is the terrain complexity index of the area where the i-th sample is located, is the height of the highest point of the terrain line in the area where the i-th sample is located, is the height of the lowest point of the terrain line in the area where the i-th sample is located, is the height of the highest point of the contour line in the area where the i-th sample is located, is the height of the lowest point of the contour line in the area where the i-th sample is located; The formula for obtaining the comprehensive index of the sample is: Q i = ω1 * R q i + ω2 * O q i + ω3 * S i Among them, Q i is the comprehensive index of the i-th sample, and R q i is the rock type index of the i-th sample, and O q i is the deposit type index of the i-th sample, and S i is the terrain complexity index of the i-th sample. ω1, ω2, and ω3 are weight coefficients, where ω2 > ω1 > ω3 and ω1 + ω2 + ω3 = 1.

2. A method for quickly delineating a prospecting target area for tin ore according to claim 1, characterized in that, The logic behind calculating the relative abundance of each element is: Using a laser ablation instrument, the surface of the sample is ablated by a laser beam to generate an aerosol. The aerosol is introduced into an inductively coupled plasma through a conduit and ionized at high temperature. By measuring the mass-to-charge ratio of different ions and calculating the element concentration based on the specific peak intensity; the standard curve method is used for quantitative analysis of element concentration. By measuring standard samples with known concentrations, the relationship between concentration and the corresponding mass spectrometry signal intensity is established. The specific formula on which the standard curve is based is: Y = δX + b where Y is the mass spectrometry signal intensity, X is the known concentration, δ is the slope, representing the signal change caused by each unit change in concentration, and b is the intercept, representing the signal at a concentration of 0; The specific formula for obtaining the relative abundance of each element is: Among them, the tin element, copper element, lead element, and zinc element in the sample are labeled as 1, 2, 3, and 4 respectively, and the concentration of the a-th element in the i-th sample is calibrated as is the relative abundance of the a-th element in the i-th sample.

3. A method for quickly delineating a prospecting target area for tin ore according to claim 2, characterized in that, The specific logic for standardizing the relative abundances of each element is: The formula for obtaining the mean relative abundance of each element is: Among them, is the mean of the relative abundances of the elements in the $i$-th sample, $n$ is the number of elements, which is 4 here; The formula for obtaining the standard deviation of the relative abundance is: where σ i is the standard deviation of the relative abundance of the i-th sample, n is the number of elements, which is 4 here; The formula for standardizing the relative abundances of each element is: Among them, is the value after standardizing the relative abundance of element a in the i-th sample.

4. A method for quickly delineating a prospecting target area for tin ore according to claim 3, characterized in that, The formula for obtaining the spatial autocorrelation index of each element is: Among them, I i is the spatial autocorrelation index of the i-th sample, N is the number of samples, W is the total weight, η1 is the spatial weight of relative abundance, η2 is the spatial weight of the comprehensive index, and η1 > η2 > 0.

5. A method for quickly delineating tin ore prospecting target areas according to claim 4, characterized in that, The logic for screening target areas with high tin ore potential is: By comparing with the contour density, contour height difference, terrain line density, and terrain line height difference of the target area, if the total difference is lower than the preset similarity threshold, the area is set as the area to be measured. If the total difference is higher than the preset similarity threshold, the area is determined as a non-area to be measured; Collect samples from the area to be measured, divide them into N parts, calculate the comprehensive index of the samples to be measured and the relative abundance values of the elements, and input them into the spatial distribution model. Sort the predicted spatial autocorrelation indices, and calibrate the areas where the samples corresponding to the top three spatial autocorrelation indices are located as target areas with high tin ore potential.

6. A system for quickly delineating tin ore prospecting target areas, the system being used to execute the method for quickly delineating tin ore prospecting target areas according to any one of claims 1-5, characterized in that, Including: A sample collection module for setting existing tin ore as the target area, uniformly calibrating N sampling points in each target area, excavating the surface soil and rocks of the sampling points as samples, and recording the geographical location, rock type, ore deposit type, and geomorphic terrain data of the samples; A geomorphic feature analysis module for analyzing the geomorphic terrain data of the samples, generating a terrain complexity index, and combining the rock type and ore deposit type of the samples to obtain the comprehensive index of the samples; A geochemical data acquisition module for obtaining the geochemical data of the target area samples through laser ablation-inductively coupled plasma mass spectrometry technology. The geochemical data of the target area samples includes the concentrations of tin, copper, lead, and zinc elements, and calculates the relative abundance of each element; A data preprocessing module for calculating the mean relative abundance and standard deviation of all samples, standardizing the relative abundances of each element, integrating the standardized data into a data set, and dividing it into a training set and a validation set; A spatial distribution model construction module for analyzing the spatial distribution based on geographic information systems, constructing a spatial distribution model, inputting the relative abundance of each element in the training set, combining the comprehensive index of the samples, obtaining the spatial autocorrelation index of each element as a label, and validating the spatial distribution model; A potential target area screening module is used to screen out the areas to be measured according to the geomorphic and topographic data of the target area, obtain samples of the areas to be measured and collect relevant data, input them into the spatial distribution model, sort the predicted spatial autocorrelation indexes, and screen out the target areas with high potential for tin ore.

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