Multi-parameter geophysical joint exploration and ore body accurate positioning method and system
Patent Information
- Application Number
- CN202611230638.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-14
- Publication Date
- 2026-10-27
Smart Images
Figure CN122984073A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mineral exploration technology, and in particular to a method and system for multi-parameter geophysical joint exploration and precise ore body positioning. Background Technology
[0002] In the field of geophysical exploration technology, especially in the detection and location of concealed metal deposits such as gold deposits, obtaining various physical parameters of underground media by measuring on the surface or in wells, and then inferring the spatial location and scale of the ore body, is the core means of establishing prospecting target areas and guiding drilling deployment.
[0003] Currently, commonly used physical property parameters include density differences obtained from gravity measurements, magnetic susceptibility obtained from magnetic measurements, resistivity obtained from electrical and electromagnetic methods, polarizability obtained from induced polarization methods, and elastic wave velocity obtained from seismic exploration. However, the inversion results of a single physical property parameter often have serious ambiguity. Models with different geometries and property distributions can fit the same set of observation data, resulting in blurred boundaries of anomalies, large depth positioning errors, and an inability to effectively distinguish between mineral-induced anomalies and non-mineral-induced disturbance anomalies.
[0004] Therefore, designing a multi-parameter geophysical joint exploration and precise ore body location method and system, and establishing a more effective prediction model to accurately realize the exploration and volume location of ore bodies, is an urgent problem to be solved. Summary of the Invention
[0005] This invention provides a method and system for multi-parameter geophysical joint exploration and precise ore body location.
[0006] To achieve the above objectives, the present invention adopts the following technical solution:
[0007] Firstly, a method for multi-parameter geophysical joint exploration and precise ore body location is provided. The method includes: collecting historical multi-parameter exploration data, extracting and analyzing multi-parameter constraints to form multi-parameter correlation constraint data; performing multi-parameter correlation analysis based on historical multi-parameter exploration data to establish multi-parameter correlation analysis data; acquiring real-time multi-parameter exploration information, and combining multi-parameter correlation constraint data and multi-parameter correlation analysis data for predictive analysis to form real-time multi-parameter exploration location data.
[0008] Therefore, the above method, through the analysis of historical multi-parameter exploration data, establishes a multi-parameter collaborative predictive model capable of accurately predicting the existence of ore bodies in the exploration area. Furthermore, it enables reasonable volume positioning using this collaborative data. The establishment of multi-parameter constraints provides an effective preliminary judgment standard for the rational application of the predictive model. The establishment of the distance prediction model accurately expresses the continuous correlation between multi-parameter correlation and Euclidean distance, providing a reference for the accurate determination of Euclidean distance in real-time multi-parameter exploration. This allows for more accurate and effective volume positioning under the volume prediction model using accurately predicted Euclidean distances, achieving reasonable exploration positioning.
[0009] Optionally, historical multi-parameter exploration data is collected, and multi-parameter constraint conditions are extracted and analyzed to form multi-parameter correlation constraint condition data. This includes: extracting exploration data indicating the existence of ore bodies based on historical multi-parameter exploration data to form historical mineralized multi-parameter exploration data; extracting single-parameter constraint conditions based on historical mineralized multi-parameter exploration data to form mineralized single-parameter constraint condition information; extracting multi-parameter collaborative constraint conditions based on historical mineralized multi-parameter exploration data to form mineralized multi-parameter constraint condition information; and combining mineralized single-parameter constraint condition information and mineralized multi-parameter constraint condition information to form multi-parameter correlation constraint condition data.
[0010] Therefore, it is evident that not every orebody exploration data point in historical multi-parameter exploration data will yield results indicating the presence of ore bodies in the exploration area. While multi-parameter exploration data corresponding to exploration results without ore bodies can serve as negative evidence, it is more effective to use data indicating ore bodies for positive prediction. Furthermore, some data may fall between the parameter constraints corresponding to ore bodies and non-ore bodies. Therefore, this application considers directly extracting historical exploration data indicating ore bodies as the basis for constraint extraction and analysis. Of course, considering the judgment of multi-parameter collaborative exploration location results, the constraints for each parameter and the constraints for multi-parameter collaboration should be effectively extracted to ensure that the premise judgment conditions for subsequent real-time multi-parameter analysis using the prediction model are reasonable and effective.
[0011] Optionally, based on historical multi-parameter exploration data of mineralized areas, single-parameter constraints are extracted to form mineralized single-parameter constraint information, including: obtaining exploration parameter values for different exploration parameters corresponding to each exploration for historical multi-parameter exploration data of mineralized areas. Where n represents the number of different exploration sub-items, and m represents the number of different exploration parameters; for different exploration parameters, the parameter characteristic interval is determined based on the exploration parameter values corresponding to each exploration in the historical multi-parameter exploration data of mineralized areas. ,in, , This represents the minimum value of the exploration parameter numbered m in historical mineralized multi-parameter exploration data. This represents the maximum value of exploration parameter number m in historical mineralized multi-parameter exploration data; it also represents the parameter characteristic intervals corresponding to different exploration parameters. This generates information on single-parameter constraints for mineral deposits.
[0012] Therefore, the direct purpose of extracting constraints for single parameters is to accurately determine whether each exploration parameter possesses basic mineralization conditions when conducting real-time multi-parameter exploration positioning result analysis. This is because different exploration parameters are interconnected, and single-parameter constraints can, to a certain extent, provide a reasonable representation of characteristic information. The characteristic range of exploration parameters is mainly determined based on the minimum and maximum values found in historical data. Of course, considering data volatility, the characteristic range can be further narrowed to improve the confidence level of the characteristic interval. The multi-parameters involved in exploration mainly include, but are not limited to, residual density anomalies, magnetic susceptibility, logarithm of resistivity, polarizability, and P-wave velocity. Residual density anomalies reflect the deviation of rock density from the background; magnetic susceptibility reflects the magnetic strength of the rock mass; the logarithm of resistivity characterizes conductivity, and taking the logarithm makes the resistivity variation range linear, reducing the difficulty of data processing and analysis; polarizability reflects the volume content of metal sulfides; and P-wave velocity, to a certain extent, characterizes the degree of mineralization. Multiple exploration parameters for ore bodies are interconnected, which provides a basis for establishing correlation constraints.
[0013] Optionally, based on historical multi-parameter exploration data of mineralized areas, multi-parameter collaborative constraints are extracted to form multi-parameter constraint information of mineralized areas, including: extracting the ore body volume measurement value corresponding to each exploration based on historical multi-parameter exploration data of mineralized areas. ;Measured based on the ore body volume determined in each exploration. A quantitative analysis of the effective measurement of ore bodies was conducted using multi-parameter collaborative exploration to determine the effective measurement values of the multi-parameter exploration. Based on the effective measurement values of multi-parameter exploration Multi-parameter collaborative constraint extraction is performed to form multi-parameter constraint information for mineral resources.
[0014] Therefore, when extracting multi-parameter synergistic constraints, it is necessary to consider that the different types and combinations of selected parameters will affect the exploration and positioning results to a certain extent. This influence is most directly reflected in the final predicted ore body size. Different types and combinations of exploration parameters will be more accurate and effective in predicting ore body size within a defined characteristic range. Therefore, to ensure the effectiveness and accuracy of the extracted multi-parameter synergistic constraints, it is first necessary to select the most accurate predicted ore body size measurement value and use the corresponding multi-parameter information as the basis for synergistic constraints to ensure that the extracted synergistic constraints are accurate and effective. The ore body size measurement value is mainly used to characterize the ore body size in the exploration area. It can be a single parameter value, such as ore body volume, ore quantity, grade-thickness product, etc., or a comprehensive expression of these parameters. Any reasonable measurement of the ore body size is acceptable.
[0015] Optionally, the ore body volume is measured based on the value determined in each exploration. A quantitative analysis of the effective measurement of ore bodies was conducted using multi-parameter collaborative exploration to determine the effective measurement values of the multi-parameter exploration. This includes: measurements of the volume of different ore bodies. Arranged in ascending order, the ore body volume measurement values form an ordered set; based on different ore body volume measurement values... The average measurement value of the ore body is determined; based on the average measurement value of the ore body, the ore body volume measurement values that are orderly concentrated below the average measurement value of the ore body are determined. The total number and the ore body volume measurement value greater than the average measurement value of the ore body The total number, and the ore body volume corresponding to the larger total number. Extract and form a set of first high-probability orebody volume measurement values; for the first high-probability orebody volume measurement value set, determine the corresponding first high-probability average volume; based on the first high-probability average volume, obtain the orebody volume measurement values in the first high-probability orebody volume measurement value set that are less than the first high-probability average volume. The total number and value of ore bodies that are greater than the average size of the highest probability ore body are the measure of ore body size. The total number, and the ore body volume corresponding to the larger total number. Extract and form a second high-probability orebody size measurement value set; for the second high-probability orebody size measurement value set, according to the orebody size measurement value... Establish a cumulative volume curve in ascending order, and then assign the nearest ore body volume measurement value to the point on the cumulative volume curve with the largest rate of change. As an effective measure of multi-parameter exploration .
[0016] Therefore, the purpose of confirming the effective measurement value is to extract the effective value of a orebody volume range that can be accurately determined by multi-parameter collaborative exploration as the benchmark reference data obtained under multi-parameter collaborative constraints. It is understandable that the average value merely represents the overall level of the parameters and cannot reflect the accurate concentration range of the data. The same applies to orebody volume measurement values; the average value only reflects the average level of the parameter data that big data can display, and cannot express the concentration of the parameter data. The orebody volume characteristic range that multi-parameter collaborative exploration can determine must consider the concentration of the data in order to intuitively see the range of accurate orebody volume prediction that multi-parameter collaborative exploration analysis can perform. Here, the effective measurement value of the ore body is obtained by considering the distribution density of the measurement values with the average value as a reference. The greater the concentration of measurement values on the side deviating from the average value, the higher the prediction confidence of the multi-parameter collaborative exploration analysis in the corresponding numerical interval. After two consecutive concentration analyses, a reasonable ore body volume measurement value needs to be selected as the effective measurement value within the determined interval range. Here, the most recent historical volume measurement value around the point of maximum change in the cumulative curve is used as the effective measurement value. The point of maximum change represents the singularity of the change in volume prediction accuracy based on multi-parameter collaboration within the high-probability distribution interval. This singularity is the turning point of the abrupt change in the accuracy of multi-parameter collaborative volume prediction of the framework. It means that the volume data before the turning point has the highest prediction accuracy based on multi-parameter collaboration, and the prediction accuracy gradually decreases after the turning point. Therefore, it is reasonable and effective to use the most recent historical measurement value near this turning point as the effective measurement value. Of course, it is also advisable to confirm the effective measure from the perspective of the probability confidence interval of the data distribution, but this method is not as efficient as the location of the high distribution quantity and the inflection point of the rate of change based on the mean in this application.
[0017] Optionally, based on the effective measurement values of multi-parameter exploration. Multi-parameter collaborative constraint extraction is performed to form mineral-rich multi-parameter constraint information, including: extracting effective measurement values of multi-parameter exploration. Corresponding different exploration parameter values Calibrated as benchmark values for exploration parameters ; for different exploration parameter benchmark values Obtain the corresponding exploration parameter values for other exploration sub-items in historical multi-parameter mineral exploration data. Same exploration parameter benchmark value Exploration parameter difference Where i is the number of different exploration sub-items, and i does not take the effective measurement value of multi-parameter exploration. The corresponding exploration sub-item number value; for any two types of exploration parameters, based on the differences of all corresponding exploration parameters. The ratio of the differences between the two types of exploration parameters is obtained, and the corresponding exploration parameter coordination interval is determined based on the minimum and maximum values of the ratio. Where j and k represent the numbers of exploration parameters, and j ≠ k; the set of different exploration parameter coordination intervals This generates information on multi-parameter constraints related to mineral deposits.
[0018] Therefore, once the effective measurement value of multi-parameter synergy is determined, the analysis under multi-parameter synergy constraints has a unified data reference basis. Considering the number of parameter types, directly performing synergy analysis on all types of parameters would inevitably increase the complexity and workload of data analysis. Therefore, this application uses the minimum number of two types as the basic unit of synergy analysis to obtain synergy data of two types. The synergy data is characterized by the ratio of the exploration parameter values of the two types. Here, before obtaining the ratio, the exploration parameter values are processed by difference with the exploration parameter benchmark values. The purpose of this processing is to avoid the situation where the denominator is zero when performing ratio processing. At the same time, the basic data processed by difference can further adjust the size of the ratio, making the resulting data more reasonable and effective.
[0019] Optionally, based on historical multi-parameter exploration data, multi-parameter correlation analysis is performed to establish multi-parameter correlation analysis data, including: a set of benchmark values for different exploration parameters. Forming a baseline vector for exploration parameters Different exploration parameter values formed by other exploration sub-items in historical multi-parameter exploration data. The set forms the exploration parameter vector corresponding to the exploration sub-item. ; based on different exploration parameter vectors and exploration parameter benchmark vector Determine the effective distance range D of the reference, where D = , Represents the exploration parameter vector and exploration parameter benchmark vector European distance, This represents the minimum value among all Euclidean distances. Represents the maximum value among all Euclidean distances; Euclidean distances for different exploration sub-items. Obtain the corresponding ore body volume measurement value si and the effective measurement value of multi-parameter exploration. The difference forms the volume measurement difference. ; Set the Euclidean distance corresponding to each exploration sub-item Difference in size measurement Multi-parameter correlation analysis was performed to generate multi-parameter localization correlation analysis data.
[0020] Therefore, the purpose of multi-parameter correlation analysis includes two aspects. Firstly, it establishes a multi-parameter constraint model based on the vector relationship analysis between multi-parameter exploration data from different exploration sub-items and benchmark exploration data. This constraint model primarily establishes correlations between multiple parameters within a high-dimensional data space, rather than simple constraint conditions. Secondly, it forms a predictive relationship model between multi-parameter data and orebody volume based on the established constraint model. Since directly establishing a relationship model between multi-parameter data and orebody volume is complex and has low accuracy, a more comprehensive multi-parameter constraint model is defined using Euclidean distance after vectorization, thereby forming a simpler and more accurate location prediction model for orebody volume.
[0021] Optionally, this can be combined with the Euclidean distance corresponding to each exploration sub-item. Difference in size measurement Perform multi-parameter correlation analysis to generate multi-parameter localization correlation analysis data, including: establishing a multi-parameter correlation analysis model. -S And determine the number of unanalyzed constants Q in the model; Euclidean distances for different exploration terms Difference in size measurement The data was extracted using Q as the group size, forming multiple correlation analysis groups; based on the different correlation analysis groups, the multi-parameter correlation analysis model was... -S The analysis is performed to form the corresponding multi-parameter correlation analysis formula; based on the different multi-parameter correlation analysis formulas, the average values of different constants are determined, and the average values of different constants are used as the values of different constants to be analyzed in the multi-parameter correlation analysis model to form the multi-parameter localization correlation analysis formula.
[0022] Therefore, it can be seen that for establishing a correlation analysis formula with ore body volume using Euclidean distance, the main task is to analyze the constants to be analyzed in the model. This application divides historical data into multiple data groups that can analyze the model based on the number of constants to be analyzed. Each data group is analyzed separately to obtain an analytical formula. Then, the average of the common constants in all analytical formulas is used as the value of the corresponding constant in the final analytical formula. This allows for the rapid and accurate establishment of a reasonable correlation analysis formula. The established multi-parameter correlation analysis model can be a linear function or a power function. Of course, the higher the power, the higher the accuracy of the analytical formula, but the data processing volume during subsequent analysis is larger. Therefore, the specific functional form of the analytical model can be determined based on the actual accuracy requirements.
[0023] Optionally, real-time multi-parameter exploration information is acquired, and predictive analysis is performed by combining multi-parameter correlation constraint data and multi-parameter correlation analysis data to form real-time multi-parameter exploration positioning data. This includes: extracting real-time exploration parameter values corresponding to different exploration parameters based on the real-time multi-parameter exploration information; and for different real-time parameter values, analyzing them according to different parameter characteristic intervals. Different exploration parameter coordination ranges And based on the effective distance range D, the following judgment analysis is performed: If all real-time exploration parameter values belong to the corresponding parameter characteristic intervals... The ratios of the exploration parameter differences formed by different types of real-time exploration parameter values all belong to the corresponding exploration parameter coordination intervals. Furthermore, the parameter vectors formed by different real-time exploration parameter values are the same as the exploration parameter reference vector. If the Euclidean distance falls within the effective range D of the benchmark, then the presence of a ore body in the real-time exploration area is confirmed; otherwise, no framework exists. For real-time multi-parameter exploration information confirming the presence of a ore body in the real-time exploration area, the parameter vector formed by combining different real-time exploration parameter values is compared with the exploration parameter benchmark vector. Based on the Euclidean distance and multi-parameter positioning correlation analysis, the corresponding real-time predicted ore body volume positioning data is determined.
[0024] Therefore, after obtaining the constraints of single parameters, multi-parameter constraints, and the correlation formula for ore body volume location, we can use these data to perform multi-parameter exploration location analysis on real-time exploration parameter data. During the analysis, we first need to judge the constraints of the multi-parameters to determine whether there is an ore body in the exploration area. Of course, the standard for judgment is that all parameters belong to the corresponding constraint range. On the basis of satisfying these conditions, we can locate the ore body volume and directly use the multi-parameter location correlation analysis formula to predict the ore body volume.
[0025] Secondly, a multi-parameter geophysical joint exploration and precise orebody positioning system is provided. The system is configured to perform the methods described in the first aspect, including: a data acquisition unit for acquiring historical multi-parameter exploration data and real-time multi-parameter exploration information; a constraint analysis unit for extracting and analyzing multi-parameter constraints from the historical multi-parameter exploration data acquired by the data acquisition unit to form multi-parameter correlation constraint data; a correlation analysis unit for performing multi-parameter correlation analysis on the historical multi-parameter exploration data acquired by the data acquisition unit to establish multi-parameter correlation analysis data; and a real-time analysis unit for predictive analysis of the real-time multi-parameter exploration information acquired by the data acquisition unit based on the multi-parameter correlation constraint data formed by the constraint analysis unit and the multi-parameter correlation analysis data formed by the correlation analysis unit, to form real-time multi-parameter exploration positioning data.
[0026] Therefore, the system acquires multi-parameter data through a data acquisition unit, extracts multi-parameter constraint features based on historical data for mineralized conditions, establishes a correlation model for ore body volume location analysis by combining historical data, and finally, uses real-time analysis to locate ore body volume based on multi-parameter real-time exploration data by integrating the acquired feature data and the correlation model. These interconnected functional units form a cohesive whole, providing a crucial material foundation for effective and accurate ore body exploration and location. Attached Figure Description
[0027] Figure 1 This is a schematic diagram illustrating a scenario application of the multi-parameter geophysical joint exploration and precise ore body positioning system provided in this embodiment of the invention.
[0028] Figure 2 A flowchart illustrating the multi-parameter geophysical joint exploration and precise ore body positioning method provided in this embodiment of the invention;
[0029] Figure 3 A schematic diagram illustrating the extraction of multi-parameter constraints for the multi-parameter geophysical joint exploration and precise ore body positioning method provided in this embodiment of the invention.
[0030] Figure 4 This is a schematic diagram of the structure of the multi-parameter geophysical joint exploration and ore body precise positioning system provided in an embodiment of the present invention. Detailed Implementation
[0031] The technical solution of the present invention will now be described with reference to the accompanying drawings.
[0032] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.
[0033] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.
[0034] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.
[0035] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or processing device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or processing device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.
[0036] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.
[0037] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.
[0038] To facilitate understanding of the embodiments of the present invention, firstly, using Figure 1 The multi-parameter geophysical joint exploration and precise ore body positioning scenario shown in the figure is used as an example to illustrate in detail the multi-parameter geophysical joint exploration and precise ore body positioning method and system applicable to the embodiments of the present invention.
[0039] For example, Figure 1 This is a schematic diagram illustrating a scenario application of the multi-parameter geophysical joint exploration and precise ore body positioning system provided in an embodiment of the present invention.
[0040] Figure 2 This is a flowchart illustrating the multi-parameter geophysical joint exploration and precise ore body positioning method provided in this embodiment of the invention.
[0041] Figure 3 This is a schematic diagram illustrating the extraction of multi-parameter constraints for the multi-parameter geophysical joint exploration and precise ore body positioning method provided in this embodiment of the invention.
[0042] This multi-parameter geophysical joint exploration and precise ore body location method is applicable to the above system, and the specific process is as follows:
[0043] S1: Collect historical multi-parameter exploration data, extract and analyze multi-parameter constraints, and form multi-parameter correlation constraint data.
[0044] Historical multi-parameter exploration data is collected, and multi-parameter constraint conditions are extracted and analyzed to form multi-parameter correlation constraint condition data. This includes: extracting exploration data indicating the existence of ore bodies from historical multi-parameter exploration data to form historical mineralized multi-parameter exploration data; extracting single-parameter constraint conditions from historical mineralized multi-parameter exploration data to form mineralized single-parameter constraint condition information; extracting multi-parameter collaborative constraint conditions from historical mineralized multi-parameter exploration data to form mineralized multi-parameter constraint condition information; and combining mineralized single-parameter constraint condition information and mineralized multi-parameter constraint condition information to form multi-parameter correlation constraint condition data.
[0045] Not every exploration of a ore body in historical multi-parameter exploration data point yields results indicating the presence of an ore body in the exploration area. While multi-parameter exploration data corresponding to exploration results without ore bodies can serve as negative evidence, it is more effective to use data indicating ore presence for positive prediction. Furthermore, some data may fall between the parameter constraints corresponding to ore presence and non-ore presence. Therefore, this application considers directly extracting historical exploration data indicating ore presence as the basis for constraint extraction and analysis. Of course, considering the collaborative exploration location result judgment of multiple parameters, the constraints for each parameter and the constraints for multi-parameter collaboration should be effectively extracted to ensure that the premise judgment conditions for subsequent real-time multi-parameter analysis using the prediction model are reasonable and effective.
[0046] Based on historical multi-parameter exploration data of mineral deposits, single-parameter constraints are extracted to form mineral deposit single-parameter constraint information, including: obtaining exploration parameter values for different exploration parameters corresponding to each exploration for historical multi-parameter exploration data of mineral deposits. Where n represents the number of different exploration sub-items, and m represents the number of different exploration parameters; for different exploration parameters, the parameter characteristic interval is determined based on the exploration parameter values corresponding to each exploration in the historical multi-parameter exploration data of mineralized areas. ,in, , This represents the minimum value of the exploration parameter numbered m in historical mineralized multi-parameter exploration data. This represents the maximum value of exploration parameter number m in historical mineralized multi-parameter exploration data; it also represents the parameter characteristic intervals corresponding to different exploration parameters. This generates information on single-parameter constraints for mineral deposits.
[0047] The direct purpose of extracting constraints for single parameters is to accurately determine whether each exploration parameter possesses basic mineralization conditions during subsequent real-time multi-parameter exploration location result analysis. This is because different exploration parameters are interconnected, and single-parameter constraints can provide a reasonable representation of characteristic information. The characteristic range of exploration parameters is mainly determined based on the minimum and maximum values found in historical data. However, considering data volatility, the characteristic range can be further narrowed to improve the confidence level of the characteristic interval. The multi-parameters involved in exploration mainly include, but are not limited to, residual density anomalies, magnetic susceptibility, logarithm of resistivity, polarizability, and P-wave velocity. Residual density anomalies reflect the deviation of rock density from the background; magnetic susceptibility reflects the magnetic strength of the rock mass; the logarithm of resistivity characterizes conductivity, and taking the logarithm makes the resistivity variation range linear, reducing the difficulty of data processing and analysis; polarizability reflects the volume content of metal sulfides; and P-wave velocity, to some extent, characterizes the degree of mineralization. Multiple exploration parameters for ore bodies are interconnected, which provides a basis for establishing correlation constraints.
[0048] Based on historical multi-parameter exploration data of mineralized areas, multi-parameter collaborative constraints are extracted to form multi-parameter constraint information for mineralized areas, including: extracting the ore body volume measurement value corresponding to each exploration based on historical multi-parameter exploration data of mineralized areas. ;Measured based on the ore body volume determined in each exploration. A quantitative analysis of the effective measurement of ore bodies was conducted using multi-parameter collaborative exploration to determine the effective measurement values of the multi-parameter exploration. Based on the effective measurement values of multi-parameter exploration Multi-parameter collaborative constraint extraction is performed to form multi-parameter constraint information for mineral resources.
[0049] When extracting multi-parameter synergistic constraints, it is important to consider that the type and combination of selected parameters will influence the exploration and positioning results to some extent. This influence is most directly reflected in the final predicted orebody size. Therefore, different types and combinations of exploration parameters will be more accurate and effective in predicting orebody size within a defined characteristic range. To ensure the effectiveness and accuracy of the extracted multi-parameter synergistic constraints, it is first necessary to select the most accurate orebody size measurement value and use the corresponding multi-parameter information as the basis for synergistic constraints, ensuring that the extracted synergistic constraints are accurate and effective. The orebody size measurement value is mainly used to characterize the orebody size in the exploration area. It can be a single parameter value, such as orebody volume, ore quantity, grade-thickness product, etc., or a comprehensive expression of these parameters. Any reasonable measurement of orebody size is acceptable.
[0050] Measurement based on the ore body volume determined in each exploration. A quantitative analysis of the effective measurement of ore bodies was conducted using multi-parameter collaborative exploration to determine the effective measurement values of the multi-parameter exploration. This includes: measurements of the volume of different ore bodies. Arranged in ascending order, the ore body volume measurement values form an ordered set; based on different ore body volume measurement values... The average measurement value of the ore body is determined; based on the average measurement value of the ore body, the ore body volume measurement values that are orderly concentrated below the average measurement value of the ore body are determined. The total number and the ore body volume measurement value greater than the average measurement value of the ore body The total number, and the ore body volume corresponding to the larger total number. Extract and form a set of first high-probability orebody volume measurement values; for the first high-probability orebody volume measurement value set, determine the corresponding first high-probability average volume; based on the first high-probability average volume, obtain the orebody volume measurement values in the first high-probability orebody volume measurement value set that are less than the first high-probability average volume. The total number and value of ore bodies that are greater than the average size of the highest probability ore body are the measure of ore body size. The total number, and the ore body volume corresponding to the larger total number. Extract and form a second high-probability orebody size measurement value set; for the second high-probability orebody size measurement value set, according to the orebody size measurement value... Establish a cumulative volume curve in ascending order, and then assign the nearest ore body volume measurement value to the point on the cumulative volume curve with the largest rate of change. As an effective measure of multi-parameter exploration .
[0051] The purpose of confirming effective measurement values is to extract the effective values of a orebody volume range that can be accurately determined by multi-parameter collaborative exploration as benchmark reference data for obtaining multi-parameter collaborative constraints. It is understandable that the average value merely represents the overall level of the parameters and cannot reflect the accurate concentration range of the data. The same applies to orebody volume measurement values; the average value only reflects the average level of the parameter data that big data can display, and cannot express the concentration of the parameter data. The orebody volume characteristic range that multi-parameter collaborative exploration can determine must consider the concentration of the data, and only then can we intuitively see the range of accurate orebody volume prediction that multi-parameter collaborative exploration analysis can perform. Here, the effective measurement value of the ore body is obtained by considering the distribution density of the measurement values with the average value as a reference. The greater the concentration of measurement values on the side deviating from the average value, the higher the prediction confidence of the multi-parameter collaborative exploration analysis in the corresponding numerical interval. After two consecutive concentration analyses, a reasonable ore body volume measurement value needs to be selected as the effective measurement value within the determined interval range. Here, the most recent historical volume measurement value around the point of maximum change in the cumulative curve is used as the effective measurement value. The point of maximum change represents the singularity of the change in volume prediction accuracy based on multi-parameter collaboration within the high-probability distribution interval. This singularity is the turning point of the abrupt change in the accuracy of multi-parameter collaborative volume prediction of the framework. It means that the volume data before the turning point has the highest prediction accuracy based on multi-parameter collaboration, and the prediction accuracy gradually decreases after the turning point. Therefore, it is reasonable and effective to use the most recent historical measurement value near this turning point as the effective measurement value. Of course, it is also advisable to confirm the effective measure from the perspective of the probability confidence interval of the data distribution, but this method is not as efficient as the location of the high distribution quantity and the inflection point of the rate of change based on the mean in this application.
[0052] Based on the effective measurement values of multi-parameter exploration Multi-parameter collaborative constraint extraction is performed to form mineral-rich multi-parameter constraint information, including: extracting effective measurement values of multi-parameter exploration. Corresponding different exploration parameter values Calibrated as benchmark values for exploration parameters ; for different exploration parameter benchmark values Obtain the corresponding exploration parameter values for other exploration sub-items in historical multi-parameter mineral exploration data. Same exploration parameter benchmark value Exploration parameter difference Where i is the number of different exploration sub-items, and i does not take the effective measurement value of multi-parameter exploration. The corresponding exploration sub-item number value; for any two types of exploration parameters, based on the differences of all corresponding exploration parameters. The ratio of the differences between the two types of exploration parameters is obtained, and the corresponding exploration parameter coordination interval is determined based on the minimum and maximum values of the ratio. Where j and k represent the numbers of exploration parameters, and j ≠ k; the set of different exploration parameter coordination intervals This generates information on multi-parameter constraints related to mineral deposits.
[0053] Once the effective measurement value for multi-parameter synergy is determined, the analysis under multi-parameter synergy constraints has a unified data reference basis. Considering the number of parameter types, directly performing synergy analysis on all types of parameters would inevitably increase the complexity and workload of data analysis. Therefore, this application uses the minimum number of two types as the basic unit of synergy analysis to obtain synergy data for each pair of types. The synergy data is characterized by the ratio of the exploration parameter values of the two types. Before obtaining the ratio, the exploration parameter values are processed by difference with the exploration parameter benchmark values. The purpose of this processing is to avoid the situation where the denominator is zero when performing ratio processing. At the same time, the basic data processed by difference can be used to further adjust the size of the ratio, making the resulting data more reasonable and effective.
[0054] S2: Based on historical multi-parameter exploration data, conduct multi-parameter correlation analysis and establish multi-parameter correlation analysis data.
[0055] Based on historical multi-parameter exploration data, multi-parameter correlation analysis was conducted to establish multi-parameter correlation analysis data, including: a set of benchmark values for different exploration parameters. To form a baseline vector of exploration parameters Different exploration parameter values formed by other exploration sub-items in historical multi-parameter exploration data. The set forms the exploration parameter vector corresponding to the exploration sub-item. ; based on different exploration parameter vectors and exploration parameter benchmark vector Determine the effective distance range D of the reference, where D = , Represents the exploration parameter vector and exploration parameter benchmark vector European distance, This represents the minimum value among all Euclidean distances. Represents the maximum value among all Euclidean distances; Euclidean distances for different exploration sub-items. Obtain the corresponding ore body volume measurement value si and the effective measurement value of multi-parameter exploration. The difference forms the volume measurement difference. ; Set the Euclidean distance corresponding to each exploration sub-item Difference in size measurement Multi-parameter correlation analysis was performed to generate multi-parameter localization correlation analysis data.
[0056] The purpose of multi-parameter correlation analysis includes two aspects. Firstly, it establishes a multi-parameter constraint model based on the vector relationship analysis between multi-parameter exploration data from different exploration sub-items and baseline exploration data. This constraint model primarily establishes correlations between multiple parameters within a high-dimensional data space, rather than simple constraint conditions. Secondly, it forms a predictive relationship model between multi-parameter data and orebody volume based on the established constraint model. Since directly establishing a relationship model between multi-parameter data and orebody volume is complex and has low accuracy, a more comprehensive multi-parameter constraint model is defined using Euclidean distance after vectorization, thereby forming a simpler and more accurate location prediction model for orebody volume.
[0057] Combined with the Euclidean distance corresponding to each exploration sub-item Difference in size measurement Perform multi-parameter correlation analysis to generate multi-parameter localization correlation analysis data, including: establishing a multi-parameter correlation analysis model. -S And determine the number of unanalyzed constants Q in the model; Euclidean distances for different exploration terms Difference in size measurement The data was extracted using Q as the group size, forming multiple correlation analysis groups; based on the different correlation analysis groups, the multi-parameter correlation analysis model was... -S The analysis is performed to form the corresponding multi-parameter correlation analysis formula; based on the different multi-parameter correlation analysis formulas, the average values of different constants are determined, and the average values of different constants are used as the values of different constants to be analyzed in the multi-parameter correlation analysis model to form the multi-parameter localization correlation analysis formula.
[0058] For establishing a correlation analysis formula with ore body volume using Euclidean distance, the main task is to analyze the constants to be analyzed in the model. This application divides historical data into multiple data groups that can analyze the model based on the number of constants to be analyzed. Each data group is analyzed to obtain an analytical formula. Then, the average of the common constants in all analytical formulas is used as the value of the corresponding constant in the final analytical formula. This allows for the rapid and accurate establishment of a reasonable correlation analysis formula. The established multi-parameter correlation analysis model can be a linear function or a power function. Of course, the higher the power, the higher the accuracy of the analytical formula, but the data processing volume during subsequent analysis is larger. Therefore, the specific functional form of the analytical model can be determined based on the actual accuracy requirements.
[0059] S3: Obtain real-time multi-parameter exploration information, combine multi-parameter correlation constraint data and multi-parameter correlation analysis data to perform predictive analysis, and form real-time multi-parameter exploration positioning data.
[0060] Real-time multi-parameter exploration information is acquired, and predictive analysis is performed by combining multi-parameter correlation constraint data and multi-parameter correlation analysis data to form real-time multi-parameter exploration positioning data. This includes: extracting real-time exploration parameter values corresponding to different exploration parameters based on the real-time multi-parameter exploration information; and for different real-time parameter values, analyzing them according to different parameter characteristic intervals. Different exploration parameter coordination ranges And based on the effective distance range D, the following judgment analysis is performed: If all real-time exploration parameter values belong to the corresponding parameter characteristic intervals... The ratios of the exploration parameter differences formed by different types of real-time exploration parameter values all belong to the corresponding exploration parameter coordination intervals. Furthermore, the parameter vectors formed by different real-time exploration parameter values are the same as the exploration parameter reference vector. If the Euclidean distance falls within the effective range D of the benchmark, then the presence of a ore body in the real-time exploration area is confirmed; otherwise, no framework exists. For real-time multi-parameter exploration information confirming the presence of a ore body in the real-time exploration area, the parameter vector formed by combining different real-time exploration parameter values is compared with the exploration parameter benchmark vector. Based on the Euclidean distance and multi-parameter positioning correlation analysis, the corresponding real-time predicted ore body volume positioning data is determined.
[0061] After obtaining the constraints of single parameters, multi-parameter constraints, and the correlation formula for ore body volume location, these data can be used to perform multi-parameter exploration location analysis on real-time exploration parameter data. During the analysis, it is first necessary to judge the constraints of the multi-parameters to determine whether there is an ore body in the exploration area. Of course, the standard for judgment is that all parameters belong to the corresponding constraint range. On the basis of satisfying these conditions, the ore body volume is located, and the ore body volume is predicted directly by using the multi-parameter location correlation analysis formula.
[0062] Figure 4This is a schematic diagram of the structure of a multi-parameter geophysical joint exploration and precise ore body positioning system provided in an embodiment of the present invention. Exemplarily, the system includes: a data acquisition unit for acquiring historical multi-parameter exploration data and real-time multi-parameter exploration information; a constraint analysis unit for extracting and analyzing multi-parameter constraints on the historical multi-parameter exploration data acquired by the data acquisition unit to form multi-parameter correlation constraint data; a correlation analysis unit for performing multi-parameter correlation analysis on the historical multi-parameter exploration data acquired by the data acquisition unit to establish multi-parameter correlation analysis data; and a real-time analysis unit for predictive analysis of the real-time multi-parameter exploration information acquired by the data acquisition unit based on the multi-parameter correlation constraint data formed by the constraint analysis unit and the multi-parameter correlation analysis data formed by the correlation analysis unit, to form real-time multi-parameter exploration positioning data.
[0063] The system acquires multi-parameter data through a data acquisition unit. Based on this, a constraint analysis unit extracts multi-parameter constraint features using historical data for mineralized conditions. A correlation analysis unit, combining historical data, establishes a correlation model for orebody volume location analysis. Finally, a real-time analysis unit integrates the acquired feature data and the correlation model to locate the orebody volume based on real-time multi-parameter exploration data. These interconnected functional units form a cohesive whole, providing a crucial material foundation for effective and accurate orebody exploration and location.
[0064] It should be understood that the processor involved in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.
[0065] It should also be understood that the memory involved in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).
[0066] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.
[0067] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0068] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.
[0069] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0070] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.
[0071] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0072] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0073] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0074] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for multi-parameter geophysical joint exploration and precise ore body location, characterized in that, The method includes: Collect historical multi-parameter exploration data, extract and analyze multi-parameter constraints, and form multi-parameter correlation constraint data; Based on the historical multi-parameter exploration data, multi-parameter correlation analysis was performed to establish multi-parameter correlation analysis data; Real-time multi-parameter exploration information is acquired, and predictive analysis is performed by combining the multi-parameter correlation constraint data and the multi-parameter correlation analysis data to form real-time multi-parameter exploration positioning data.
2. The method according to claim 1, characterized in that, The collected historical multi-parameter exploration data is used to extract and analyze multi-parameter constraints, forming multi-parameter correlation constraint data, including: Based on the historical multi-parameter exploration data, the exploration data that indicates the existence of ore bodies is extracted to form historical ore-bearing multi-parameter exploration data. Based on the historical multi-parameter exploration data of mineral deposits, single-parameter constraints are extracted to form mineral single-parameter constraint information. Based on the historical mineral exploration data with multiple parameters, multi-parameter collaborative constraints are extracted to form mineral multi-parameter constraint information. The multi-parameter constraint information of the mineral resource is combined with the multi-parameter constraint information of the mineral resource to form the multi-parameter associated constraint data.
3. The method according to claim 2, characterized in that, The step of extracting single-parameter constraints based on the historical multi-parameter mineral exploration data to form mineral single-parameter constraint information includes: For the historical multi-parameter exploration data of mineral deposits, obtain the exploration parameter values for different exploration parameters corresponding to each exploration. Where n represents the number of different exploration sub-items, and m represents the number of different exploration parameters; For different exploration parameters, the parameter characteristic range is determined based on the exploration parameter values corresponding to each exploration in the historical mineralized multi-parameter exploration data. ,in, , This indicates the minimum value of the exploration parameter numbered m in the historical multi-parameter exploration data of mineralized areas. This indicates the maximum value of the exploration parameter value numbered m in the historical mineralized multi-parameter exploration data; The parameter feature ranges corresponding to different exploration parameters are set. This forms the mineral single-parameter constraint information.
4. The method according to claim 3, characterized in that, The step of extracting multi-parameter collaborative constraints based on the historical mineral exploration data to form mineral multi-parameter constraint information includes: Based on the historical multi-parameter mineral exploration data, extract the ore body volume measurement value corresponding to each exploration. ; The ore body volume determined based on each exploration is measured. A quantitative analysis of the effective measurement of ore bodies was conducted using multi-parameter collaborative exploration to determine the effective measurement values of the multi-parameter exploration. ; Based on the aforementioned effective measurement values of multi-parameter exploration Multi-parameter collaborative constraint conditions are extracted to form the mineral multi-parameter constraint condition information.
5. The method according to claim 4, characterized in that, The ore body volume measurement value determined based on each exploration. A quantitative analysis of the effective measurement of ore bodies was conducted using multi-parameter collaborative exploration to determine the effective measurement values of the multi-parameter exploration. ,include: Different ore body volume measurement values Arranged in ascending order, they form an ordered set of ore body volume measurement values; Based on different ore body volume measurements Determine the average measurement value of the ore body; Based on the average measurement value of the ore body, determine the ore body volume measurement values that are in an ordered set smaller than the average measurement value of the ore body. The total number and the ore body volume measurement value greater than the average measurement value of the ore body The total number, and the ore body volume measurement value corresponding to the larger total number. Extract and form the first high-probability ore body volume measurement value set; For the first high-probability ore body volume measurement value set, determine the corresponding first high-probability volume average value; Based on the first high-probability volume average value, obtain the volume measurement values of the ore bodies whose values in the first high-probability ore body volume measurement value set are less than the first high-probability volume average value. The total number and value of the ore body are greater than the average value of the first high-probability ore body. The total number, and the ore body volume measurement value corresponding to the larger total number. Extract and form a second set of high-probability ore body volume measurement values; For the second set of high-probability orebody size measurement values, according to the orebody size measurement values Establish a cumulative volume curve in ascending order, and assign the nearest ore body volume measurement value to the point on the cumulative volume curve with the largest rate of change. As an effective measure of the multi-parameter exploration .
6. The method according to claim 5, characterized in that, The effective measurement value based on the multi-parameter exploration Multi-parameter collaborative constraint extraction is performed to form the mineralized multi-parameter constraint information, including: The effective measurement value of the multi-parameter exploration The corresponding different exploration parameter values Calibrated as benchmark values for exploration parameters ; For different exploration parameter benchmark values Obtain the exploration parameter values corresponding to other exploration sub-items in the historical mineral multi-parameter exploration data. Same as the exploration parameter benchmark value Exploration parameter difference Where i is the number of different exploration sub-items, and i does not take the effective measurement value of the multi-parameter exploration. The corresponding exploration sub-item number value; For any two types of exploration parameters, based on the differences between all the corresponding exploration parameters. The ratio of the differences between the two types of exploration parameters is obtained, and the corresponding exploration parameter coordination interval is determined based on the minimum and maximum values of the ratio. , where j and k both represent the numbers of exploration parameters, and j≠k; Combined range of different exploration parameters This forms the multi-parameter constraint information for the mineralized area.
7. The method according to claim 6, characterized in that, The step of performing multi-parameter correlation analysis based on the historical multi-parameter exploration data and establishing multi-parameter correlation analysis data includes: A set of different exploration parameter benchmark values To form a baseline vector of exploration parameters ; Different exploration parameter values formed from other exploration sub-items in the historical multi-parameter exploration data. The set forms the exploration parameter vector corresponding to the exploration sub-item. ; According to different exploration parameter vectors and the exploration parameter reference vector Determine the effective distance range D of the reference, where D = , Represents the exploration parameter vector and the exploration parameter reference vector European distance, This represents the minimum value among all Euclidean distances. This represents the maximum value among all Euclidean distances; Euclidean distances for different exploration sub-items Obtain the corresponding ore body volume measurement value si and the multi-parameter exploration effective measurement value. The difference forms the volume measurement difference. ; The set of Euclidean distances corresponding to each exploration sub-item and the difference in the body mass measurement Multi-parameter correlation analysis was performed to generate multi-parameter localization correlation analysis data.
8. The method according to claim 7, characterized in that, The Euclidean distance corresponding to each exploration sub-item and the difference in the body mass measurement Multi-parameter correlation analysis was performed to generate multi-parameter localization correlation analysis data, including: Establish a multi-parameter correlation analysis model -S And determine the number Q of constants to be analyzed in the model; The Euclidean distance for different exploration sub-items and the difference in the body mass measurement Extraction is performed using Q as the group size, forming multiple correlation analysis groups; Based on the different correlation analysis groups, the multi-parameter correlation analysis model... -S The analysis is performed to form the corresponding multi-parameter correlation analysis expression; Based on the different analytical expressions of the multi-parameter correlation analysis, the average values of different constants are determined, and the average values of different constants are used as the values of different constants to be analyzed in the multi-parameter correlation analysis model to form a multi-parameter localization correlation analysis expression.
9. The method according to claim 8, characterized in that, The process of acquiring real-time multi-parameter exploration information, combining the multi-parameter correlation constraint data and the multi-parameter correlation analysis data for predictive analysis, and forming real-time multi-parameter exploration positioning data includes: Based on the real-time multi-parameter exploration information, extract the real-time exploration parameter values corresponding to different exploration parameters; For different real-time exploration parameter values, based on different parameter characteristic ranges Different exploration parameter coordination ranges And based on the aforementioned effective distance range D, the following judgment and analysis are performed: If all the real-time exploration parameter values belong to the corresponding parameter characteristic range The ratios of the exploration parameter differences formed by the real-time exploration parameter values of different types all belong to the corresponding exploration parameter coordination intervals. Furthermore, the parameter vectors formed by different real-time exploration parameter values are the same as the exploration parameter reference vector. If the Euclidean distance falls within the effective distance range D of the benchmark, then it is determined that there is a ore body in the real-time exploration area; otherwise, there is no frame. For real-time multi-parameter exploration information that confirms the existence of ore bodies in the real-time exploration area, the parameter vector formed by combining different real-time exploration parameter values is compared with the exploration parameter reference vector. The Euclidean distance and the multi-parameter positioning correlation analysis formula are used to determine the corresponding real-time predicted ore body volume positioning data.
10. A multi-parameter geophysical joint exploration and precise ore body positioning system, characterized in that, Configured to perform the method as described in any one of claims 1-9, comprising: The data acquisition unit is used to acquire historical multi-parameter exploration data and real-time multi-parameter exploration information; The constraint analysis unit is used to extract and analyze multi-parameter constraints from the historical multi-parameter exploration data acquired by the data acquisition unit, and form multi-parameter correlation constraint data. The correlation analysis unit is used to perform multi-parameter correlation analysis on the historical multi-parameter exploration data acquired by the data acquisition unit and establish multi-parameter correlation analysis data. The real-time analysis unit is used to predict and analyze the real-time multi-parameter exploration information acquired by the data acquisition unit based on the multi-parameter correlation constraint data formed by the constraint analysis unit and the multi-parameter correlation analysis data formed by the correlation analysis unit, so as to form real-time multi-parameter exploration positioning data.