Three-dimensional geological modeling method and device based on attribute variable joint simulation

Abstract

The invention provides a three-dimensional geological modeling method and device based on attribute variable joint simulation, and relates to the technical field of computers, and the method comprises the steps: obtaining lithology data and grade data of a mining area; classifying the lithologic data, converting lithologic variables in the lithologic data into indication variables by utilizing an indication criterion, performing Gaussian variation on the grade data, and converting the grade data into normal distribution data with a mean value of 0 and a variance of 1; determining the boundary type of the lithologic boundary by using the lagging scatter diagram, and carrying out joint modeling based on the spatial correlation of the boundary grade under the condition that the boundary type is a soft boundary to obtain an initial model; and determining the number of Gaussian random fields, a truncation rule, a truncation threshold, a suite structure and a common region matrix by using the indication variable and the normal distribution data so as to adjust the parameters of the initial model to obtain a target three-dimensional geologic model. According to the method, the data processing flow is simplified, the data search and simulation efficiency is improved, and the adaptability and accuracy of the model are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a three-dimensional geological modeling method and device based on joint simulation of attribute variables. Background Art

[0002] Underground lithology identification is an important task in the fields of earth science and engineering. It involves the understanding and study of the properties of underground rocks, which is of great significance for geological mapping, mineral exploration, oil and gas exploration, geological engineering and other fields. For mineral exploration, lithology identification can help determine the location and distribution of ores, as well as the properties of ores, which is very important for the development and management of downstream mineral resources; for oil and gas exploration, lithology identification plays a crucial role in oil and gas exploration. By analyzing the properties of underground rocks, explorers can determine the location, thickness and properties of oil and gas reservoirs, thus guiding drilling and development work; for geological engineering, the field of geological engineering needs to accurately identify the properties of underground rocks to guide the engineering design and construction process. Lithology identification can help engineers understand the stability, strength and permeability of underground rocks, etc., so as to ensure the safety and feasibility of the project. Geological modeling is one of the typical technical means to achieve underground lithology identification. In the field of geological modeling, especially for the evaluation and development of mineral resources, three-dimensional geological modeling technology has become a key technology. The core purpose of this technology is to reconstruct a three-dimensional model of the underground structure from limited exploration data to support the efficient management and utilization of mineral resources. However, in the current three-dimensional models, less attention is paid to variables of different attributes, the correlation between lithology and grade in the three-dimensional geological model is not considered, and the composite error caused by separately modeling lithology and grade is not solved, resulting in poor adaptability and accuracy for underground lithology identification. Summary of the Invention

[0003] The main object of the present invention is: to solve the problem that the current three-dimensional geological model does not meet the requirements of adaptability and accuracy for underground lithology identification, the present invention provides a three-dimensional geological modeling method and device based on joint simulation of attribute variables.

[0004] The technical solution of the embodiment of the present application is realized as follows: The first aspect of the embodiment of the present application provides a three-dimensional geological modeling method based on joint simulation of attribute variables, including: Obtaining borehole data of a mining area; the borehole data includes lithology data and grade data; Classifying the lithology data, transforming the lithology variables in the lithology data into indicator variables by using an indicator criterion, and performing Gaussian transformation on the grade data to convert the grade data into normal distribution data with a mean of 0 and a variance of 1; The boundary type of the lithological boundary is determined by using a hysteresis scatter plot. When the boundary type is a soft boundary, joint modeling is performed based on the spatial correlation of the boundary grade to obtain an initial model. The indicator variables and the normal distribution data are used to determine the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures and common-region matrices to adjust the parameters of the initial model to obtain a target three-dimensional geological model.

[0005] Optionally, after obtaining the drilling data of the mining area, the method further includes: The extremely high values of the grade data are processed, the lower limit of the extremely high values is determined using the grade variation coefficient, and the abnormal data in the grade data are eliminated.

[0006] Optionally, the determining the number of Gaussian random fields, truncation rules, truncation thresholds, the number of nested structures, and nested structures using the indicator variable and the normal distribution data includes: The indicator variable and the normal distribution data are used to determine the lithology contact relationship, define a truncation rule, and establish a Gaussian random field according to the truncation rule. The number of the Gaussian random fields is determined in combination with the lithology contact relationship.

[0007] Optionally, the determining of the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures and common region matrices using the indicator variables and the normal distribution data includes: Obtaining the lithology ratio in the drilling data using a vertical ratio curve; The cutoff threshold is determined based on the lithology ratio and the Gaussian random field.

[0008] Optionally, the determining of the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures and common region matrices using the indicator variables and the normal distribution data includes: Determine the initial nesting structure based on the grade variation function in the horizontal and vertical directions; The cross variogram between different lithology data is fitted by trial and error using the variogram model and the lithology contact relationship, and the spatial variation structure of the Gaussian random field is fitted using the initial fitting structure to determine the final fitting structure.

[0009] Optionally, the determining the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures and common region matrices using the indicator variables and the normal distribution data includes: After determining the variogram structure of the Gaussian random field, a common-region model is created to characterize the spatial cross-correlation of the variogram structure of the Gaussian random field; the common-region model includes a common-region matrix, the diagonal parameters of the common-region matrix represent the direct variogram coefficients, and the non-diagonal parameters represent the cross-variogram coefficients.

[0010] Optionally, determining the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures, and co-regional matrices by using the indicator variable and the normal distribution data to adjust the parameters of the initial model to obtain a target three-dimensional geological model includes: Co-simulating Gaussian random fields by using the Gibbs sampler algorithm and the transition band algorithm, inverse-transforming the normal distribution data into grade data by using the inverse transformation algorithm, and inverse-transforming the indicator data into rock types by using the defined truncation rules.

[0011] Inverse-transforming the normal distribution data into grade data by using the inverse transformation algorithm, and inverse-transforming the indicator data into rock types by using the defined truncation rules.

[0012] Optionally, after obtaining the target three-dimensional geological model, the method further includes: Performing geological simulation by using the target three-dimensional geological model to obtain a corresponding first lithology proportion histogram and a first grade variogram; Comparing the first lithology proportion histogram and the first grade variogram with a second lithology proportion histogram and a second grade variogram of borehole data to determine a simulation error; Adjusting the parameters of the target three-dimensional geological model based on the simulation error until the simulation error is lower than a preset error threshold.

[0013] A three-dimensional geological modeling device based on joint simulation of attribute variables according to a second aspect of an embodiment of the present application includes: a data acquisition module, a data processing module, and a model creation module, where The data acquisition module is configured to acquire borehole data of a mining area; the borehole data includes lithology data and grade data; The data processing module is configured to classify the lithology data, transform lithology variables in the lithology data into indicator variables by using an indicator criterion, and perform a Gaussian transformation on the grade data to transform the grade data into a normal distribution data with a mean of 0 and a variance of 1; The model creation module is configured to determine a boundary type of a lithology boundary by using a lag scatter plot, and in the case where the boundary type is a soft boundary, perform joint modeling based on the spatial correlation of boundary grades to obtain an initial model, and determine the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures, and co-regional matrices by using the indicator variable and the normal distribution data to adjust the parameters of the initial model to obtain a target three-dimensional geological model.

[0014] A third aspect of the embodiments of the present application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the three-dimensional geological modeling method based on joint simulation of attribute variables described in the first aspect.

[0015] Compared with the prior art, the beneficial effects brought by the technical solution provided by the present application are as follows: The present invention provides a three-dimensional geological modeling method and device based on joint simulation of attribute variables. By obtaining lithology data and grade data of a mining area; classifying the lithology data and using an indicator criterion to transform the lithology variables in the lithology data into indicator variables, the data processing flow is simplified, the efficiency of data search and simulation is improved, and the grade data is subjected to Gaussian transformation to convert the grade data into normal distribution data with a mean of 0 and a variance of 1, providing accurate input data for joint simulation and ensuring the accuracy of the simulation results. On this basis, the nature of the lithology boundary is intelligently determined using a lag scatter plot to distinguish hard boundaries and soft boundaries, providing key boundary conditions for subsequent joint simulation and enhancing the adaptability and accuracy of the model. The truncation rule and threshold are dynamically determined according to the lithology ratio and Gaussian random field, avoiding the one-size-fits-all static method, enabling the model to flexibly adapt to different geological conditions, and improving the flexibility and accuracy of the simulation. The spatial cross-correlation between different Gaussian random fields is flexibly constructed through a co-kriging model, enhancing the performance ability of the three-dimensional model for the complexity of geological features and improving the reliability of the simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of a three-dimensional geological modeling method based on joint simulation of attribute variables provided by an embodiment of the present application; Figure 2 It is a schematic vertical ratio diagram of rock types provided by an embodiment of the present application; Figure 3 It is a schematic diagram of examples of two Gaussian random fields and different truncation thresholds and truncation rules provided by an embodiment of the present invention; Figure 4 It is a schematic flow chart of Gibbs sampling provided by an embodiment of the present application; Figure 5 It is a schematic structural diagram of a three-dimensional geological modeling device based on joint simulation of attribute variables provided by an embodiment of the present application; Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present application. In the following detailed description, for the sake of explanation, numerous specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0018] The terms used herein are merely for describing specific embodiments and are not intended to limit the present application. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components. All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0019] Some block diagrams and / or flowcharts are shown in the accompanying drawings. It should be understood that some blocks or combinations thereof in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, so that when executed by the processor, these instructions can create a device for implementing the functions / operations illustrated in these block diagrams and / or flowcharts.

[0020] In some embodiments, please refer to Figure 1 , Figure 1 which is a schematic flow diagram of the three-dimensional geological modeling method based on joint simulation of attribute variables provided by the embodiments of the present application; the three-dimensional geological modeling method based on joint simulation of attribute variables provided by the embodiments of the present application includes: S110, obtaining borehole data of the mining area; the borehole data includes lithology data and grade data.

[0021] Here, the borehole data may further include orifice data and formation data.

[0022] In some embodiments, after obtaining the borehole data of the mining area in S110, this method further includes: Processing the extremely high values of the grade data, determining the lower limit of the extremely high values using the grade variation coefficient, and removing the abnormal data in the grade data.

[0023] S120. Classify the lithology data, transform the lithology variables in the lithology data into indicator variables using the indicator criterion, and perform Gaussian transformation on the grade data to convert the grade data into normal distribution data with a mean of 0 and a variance of 1.

[0024] In this embodiment, the nscore function in GSLIB can be used to perform Gaussian transformation on the grade data to make its mean 0 and variance 1, conforming to the normal distribution. Use the indicator criterion to transform the lithology variables into indicator variables, thereby defining the lithology indicator data of the location points. Import the processed grade data and lithology indicator data into SKUA-GOCAD to form a database.

[0025] S130. Use the lag scatter plot to determine the boundary type of the lithology boundary. In the case where the boundary type is a soft boundary, perform joint modeling based on the spatial correlation of the boundary grade to obtain an initial model, and use the indicator variables and normal distribution data to determine the number of Gaussian random fields, truncation rules, truncation thresholds, nesting structures, and co-regional matrices to adjust the parameters of the initial model to obtain the target three-dimensional geological model.

[0026] In this embodiment, use the lag scatter plot to determine whether the lithology boundary is a hard boundary or a soft boundary. If it is a hard boundary, the boundary grades of different lithology types can be modeled separately; if it is a soft boundary, consider the spatial correlation of the boundary grade and use joint modeling.

[0027] In some embodiments, S130. Determining the number of Gaussian random fields, truncation rules, truncation thresholds, nesting structures, and co-regional matrices using the indicator variables and normal distribution data includes: Use the indicator variables and normal distribution data to determine the lithology contact relationship, define the truncation rules, and establish a Gaussian random field according to the truncation rules, and determine the number of Gaussian random fields in combination with the lithology contact relationship.

[0028] In some embodiments, S130. Determining the number of Gaussian random fields, truncation rules, truncation thresholds, nesting structures, and co-regional matrices using the indicator variables and normal distribution data includes: Obtain the lithology ratio in the borehole data using the vertical proportion curve; Determine the truncation threshold based on the lithology ratio and the Gaussian random field.

[0029] In some embodiments, S130. Determining the number of Gaussian random fields, truncation rules, truncation thresholds, nesting structures, and co-regional matrices using the indicator variables and the normal distribution data includes: Determine the initial nesting structure based on the grade variograms in the horizontal and vertical directions; Trial-and-error fitting of the cross-variogram between different lithology data is carried out using the variogram model and the lithology contact relationship. The spatial variation structure of the Gaussian random field is fitted using the initial nested structure, and the final nested structure is determined.

[0030] In this embodiment, the gamv program in GSLIB can be used to calculate the grade variogram in the horizontal and vertical directions to determine the initial nested structure. The fitting result is represented by the parameter WSS, and the smaller the parameter, the better the fitting result.

[0031] In some embodiments, S130, determining the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures, and co-regional matrices using indicator variables and normal distribution data includes: After determining the variogram structure of the Gaussian random field, a co-regional model is created to characterize the spatial cross-correlation of the Gaussian random field variogram structure; the co-regional model includes a co-regional matrix, and the diagonal parameters of the co-regional matrix represent the direct variogram coefficients, and the off-diagonal parameters represent the cross-variogram coefficients.

[0032] In an example, a co-regionalization model structure is established to represent the spatial cross-correlation of the Gaussian random field variogram structure. The diagonal parameters of the co-regionalization model are the direct variogram coefficients of the Gaussian random field; the off-diagonal parameters are fitted to the cross-function of lithology and grade by trial and error. The joint simulation algorithm is used for the target mining area, specifically including the following steps: First, input grid parameters, including the grid origin and the grid cell size. The grid origin is the minimum value of the x, y, and z direction coordinates. Secondly, parameters such as the number of Gaussian random fields, truncation rules, truncation thresholds, the number of nested structures, and nested structures need to be input.

[0033] In some embodiments, S130, determining the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures, and co-regional matrices using indicator variables and normal distribution data to adjust the parameters of the initial model to obtain the target three-dimensional geological model includes: The Gibbs sampler algorithm and the transformation band algorithm are used to jointly simulate the Gaussian random field, and the inverse transformation algorithm is used to inverse-transform the normal distribution data into grade data, and the defined truncation rules are used to inverse-transform the indicator data into rock types.

[0034] In this embodiment, the simulation results can be visualized by the GSLIB arrangement method. On this basis, the visualized values are imported into SKUA-GOCAD for simulation.

[0035] In some embodiments, after obtaining the target three-dimensional geological model, the method further includes: Using the target three-dimensional geological model for geological simulation to obtain the corresponding first lithology proportion histogram and the first grade variogram. Compare the first lithology proportion histogram, the first grade variogram, the second lithology proportion histogram of the borehole data, and the second grade variogram to determine the simulation error; Adjust the parameters of the target three-dimensional geological model based on the simulation error until the simulation error is lower than the preset error threshold.

[0036] In this embodiment, draw the first lithology proportion histogram and the first grade variogram, and compare them with the second lithology proportion histogram and the second grade variogram of the borehole data; in addition, the simulation results can be overlaid with the cross-section of the mining area to verify the overlap between the high-grade areas in the simulation results and the ore bodies in the cross-section, so as to verify the effectiveness of the simulation results.

[0037] In an alternative embodiment, refer to Figure 2 , Figure 2 which is a schematic diagram of the vertical proportion of rock types provided by the embodiment of the present application; there are five virtual boreholes, and each borehole contains five core segments of equal length. The lithology of the boreholes includes sandstone, argillaceous sandstone, and shale. For the five cores in the upper row, all are shale (i.e., 100% shale), and the shale at the top is more than that at the bottom. The second row is 80% shale and 20% argillaceous sandstone. At the bottom, there is 40% argillaceous sandstone and 60% sandstone. The vertical proportions of these boreholes are as Figure 2 shown.

[0038] In an alternative embodiment, refer to Figure 3 , Figure 3 which is a schematic diagram of two Gaussian random fields and different truncation thresholds and truncation rules provided by the embodiment of the present invention; the four main steps of complex Gaussian simulation are: select a suitable model type; estimate its parameters; generate Gaussian values corresponding to the lithology of the sample points; and finally run conditional simulation using the Gaussian values generated in the previous step. First, select a single or multiple Gaussian random field models according to the natural order between lithologies; secondly, determine the truncation threshold and variogram model parameters, which involve the proportion of rock types, truncation rules, and the correlation between Gaussian variables; then, use the Gibbs sampler to generate Gaussian values corresponding to the lithology of the sample points within an appropriate interval; finally, simulate the Gaussian values at the grid nodes through the conditional simulation algorithm, and apply the truncation rules to convert these values into lithology categories. This process not only requires accurate model selection and parameter estimation, but also effective Gaussian value generation and conditional simulation to ensure that the simulation results match the actual geological data, which is crucial for understanding and predicting the underground rock formation structure.

[0039] In an alternative embodiment, refer to Figure 4 , Figure 4Schematic diagram of the Gibbs sampling process provided by the embodiments of the present application; through a simple example involving two lithology types (coded as "1" or "2"), the complex Gaussian simulation algorithm is explained. This algorithm uses a single Gaussian random field Y(x) and a single truncation threshold t to define the rock types, where the indicator value of Y(x) needs to meet the conditions of the geological body at each data location: if it corresponds to rock type 1, the value should be less than t; if it corresponds to rock type 2, the value should be greater than t. In the second stage of the algorithm, by iteratively selecting the data location xα, calculating the conditional distribution of Y(x) under given conditions, and simulating the random variable Y(a), it is checked whether it matches the main rock type. If it matches, the value of Y(xα) is updated; if it does not match, the simulation is repeated or another data location is selected. Through multiple iterations, the algorithm gradually optimizes the simulation results to improve the accuracy and reliability of the simulation.

[0040] In the embodiments of the present invention, by obtaining the lithology data and grade data of the mining area; classifying the lithology data, using the indicator criterion to transform the lithology variables in the lithology data into indicator variables, simplifying the data processing flow, improving the efficiency of data search and simulation, and performing Gaussian transformation on the grade data to convert the grade data into normal distribution data with a mean of 0 and a variance of 1, providing accurate input data for joint simulation and ensuring the accuracy of the simulation results. On this basis, the nature of the lithology boundary is intelligently determined using the lag scatter plot, distinguishing between hard boundaries and soft boundaries, providing key boundary conditions for subsequent joint simulation, and enhancing the adaptability and accuracy of the model. According to the lithology ratio and Gaussian random field, the truncation rule and threshold are dynamically determined, avoiding the one-size-fits-all static method, enabling the model to flexibly adapt to different geological conditions, and improving the flexibility and precision of the simulation. By flexibly constructing the spatial cross-correlation between different Gaussian random fields through the co-kriging model, the performance ability of the three-dimensional model for the complexity of geological features is enhanced, and the reliability of the simulation results is improved.

[0041] In some embodiments, please refer to Figure 5 , Figure 5 Schematic diagram of the structure of a three-dimensional geological modeling device for joint simulation based on attribute variables provided by the embodiments of the present application; the embodiments of the present application provide a three-dimensional geological modeling device 500 for joint simulation based on attribute variables, including: a data acquisition module 510, a data processing module 520, and a model creation module 530, where The data acquisition module 510 is configured to acquire the borehole data of the mining area; the borehole data includes lithology data and grade data.

[0042] The data processing module 520 is configured to classify the lithology data, use the indicator criterion to transform the lithology variables in the lithology data into indicator variables, and perform Gaussian transformation on the grade data to convert the grade data into normal distribution data with a mean of 0 and a variance of 1.

[0043] The model creation module 530 is configured to use the lag scatter plot to determine the boundary type of the lithological boundary. When the boundary type is a soft boundary, joint modeling is performed based on the spatial correlation of the boundary grade to obtain an initial model, and the indicator variables and normal distribution data are used to determine the number of Gaussian random fields, truncation rules, truncation thresholds, nested structures and common-region matrices to adjust the parameters of the initial model to obtain a target three-dimensional geological model.

[0044] In some embodiments, the data processing module 520 is further configured to: The extremely high values of grade data are processed, the lower limit of the extremely high values is determined using the grade variation coefficient, and abnormal data in the grade data are eliminated.

[0045] In some embodiments, the model creation module 530 is specifically configured to: The indicator variables and normal distribution data are used to determine the contact relationship between lithologies, and the cutoff rules are defined. Gaussian random fields are established based on the cutoff rules, and the number of Gaussian random fields is determined in combination with the contact relationship between lithologies.

[0046] In some embodiments, the model creation module 530 is specifically configured to: Use vertical ratio curves to obtain lithology ratios in borehole data; The cutoff threshold is determined based on the lithology ratio and the Gaussian random field.

[0047] In some embodiments, the model creation module 530 is specifically configured to: Determine the initial nesting structure based on the grade variation function in the horizontal and vertical directions; The cross variogram between different lithology data is fitted by trial and error using the variogram model and the lithology contact relationship. The initial fitting structure is used to fit the spatial variation structure of the Gaussian random field to determine the final fitting structure.

[0048] In some embodiments, the model creation module 530 is specifically configured to: After determining the variogram structure of the Gaussian random field, a common-region model is created to characterize the spatial cross-correlation of the variogram structure of the Gaussian random field; the common-region model includes a common-region matrix, the diagonal parameters of the common-region matrix represent the direct variogram coefficients, and the non-diagonal parameters represent the cross-variogram coefficients.

[0049] In some embodiments, the model creation module 530 is specifically configured to: The Gaussian random field is simulated jointly by the Gibbs sampler algorithm and the conversion zone algorithm, and the normal distribution data are back-transformed into grade data using the back-transformation algorithm, and the indicator data are back-transformed into rock type using the defined cutoff rules.

[0050] In some embodiments, the three-dimensional geological modeling device further includes a verification and adjustment module; the verification and adjustment module is configured to: Perform geological simulation using the target three-dimensional geological model to obtain a corresponding first lithology proportion histogram and a first grade variogram; Compare the first lithology proportion histogram and the first grade variogram with the second lithology proportion histogram and the second grade variogram of the borehole data to determine the simulation error; Adjust the parameters of the target three-dimensional geological model based on the simulation error until the simulation error is lower than a preset error threshold.

[0051] The three-dimensional geological modeling device provided by the embodiments of the present application can implement each process in the corresponding embodiments of the above-mentioned three-dimensional geological modeling method based on joint simulation of attribute variables. To avoid repetition, it will not be elaborated here.

[0052] It should be noted that the three-dimensional geological modeling device provided by the embodiments of the present application and the three-dimensional geological modeling method provided by the embodiments of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the above-mentioned three-dimensional geological modeling method based on joint simulation of attribute variables, and the repeated parts will not be elaborated.

[0053] In some embodiments, please refer to Figure 6 , Figure 6 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. An electronic device 600 provided by an embodiment of the present application includes a processor 610 and a memory 620; the memory 620 stores a computer program, wherein the computer program, when executed by the processor, implements the above-mentioned three-dimensional geological modeling method based on joint simulation of attribute variables.

[0054] Specifically, the processor 610 may include, for example, a general microprocessor, an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), and so on. The processor 610 may also include on-board memory for caching purposes. The processor 610 may be a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiments of the present application.

[0055] The memory 620 may be, for example, any medium capable of containing, storing, transmitting, propagating, or transporting instructions. For example, the memory 620 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, components, or propagation media. Specific examples of the memory 620 include: magnetic storage devices, such as magnetic tapes or hard disk drives (HDDs); optical storage devices, such as compact discs (CD-ROMs); and may also be, for example, random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0056] The present application also provides a computer-readable medium, on which a computer program is stored, and when the program is executed by a processor, the three-dimensional geological modeling method based on joint simulation of attribute variables as described above is implemented. The computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist separately and not be assembled into the device / apparatus / system. The above computer-readable medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present application is implemented.

[0057] According to an embodiment of the present application, the computer-readable medium may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may, for example, but not be limited to, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, on which computer-readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted by any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signal, etc., or any suitable combination of the above.

[0058] Those skilled in the art will understand that the features recited in the various embodiments and / or claims of the present application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly recited in the present application. In particular, without departing from the spirit and teachings of the present application, the features recited in the various embodiments and / or claims of the present application can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above embodiments, but should be determined not only by the appended claims, but also by equivalents of the appended claims.

Claims

1. A three-dimensional geological modeling method based on the combined simulation of attribute variables, characterized in that Including: Obtaining borehole data of a mining area; the borehole data includes lithology data and grade data; Classifying the lithology data, transforming the lithology variables in the lithology data into indicator variables by using an indicator criterion, and performing Gaussian transformation on the grade data to convert the grade data into normal distribution data with a mean of 0 and a variance of 1; Determining the boundary type of the lithology boundary by using a lag scatter plot. In the case where the boundary type is a soft boundary, performing joint modeling based on the spatial correlation of the cut-off grade to obtain an initial model, and using the indicator variables and the normal distribution data to determine the number of Gaussian random fields, cut-off rules, cut-off thresholds, nested structures, and co-regional matrices to adjust the parameters of the initial model to obtain a target three-dimensional geological model.

2. The 3D geological modeling method based on combined simulation of attribute variables according to claim 1, characterized in that After obtaining the borehole data of the mining area, the method further includes: Processing the extremely high values of the grade data, determining the lower limit of the extremely high values by using the coefficient of variation of the grade, and removing the abnormal data in the grade data.

3. The 3D geological modeling method based on combined simulation of attribute variables according to claim 1, characterized in that The determining the number of Gaussian random fields, cut-off rules, cut-off thresholds, nested structures, and co-regional matrices by using the indicator variables and the normal distribution data includes: Determining the lithology contact relationship by using the indicator variables and the normal distribution data, defining cut-off rules, and establishing a Gaussian random field according to the cut-off rules, and determining the number of Gaussian random fields in combination with the lithology contact relationship.

4. The 3D geological modeling method based on combined simulation of attribute variables according to claim 3, characterized in that The determining the number of Gaussian random fields, cut-off rules, cut-off thresholds, nested structures, and co-regional matrices by using the indicator variables and the normal distribution data includes: Obtaining the lithology ratio in the borehole data by using a vertical ratio curve; Determining the cut-off threshold based on the lithology ratio and the Gaussian random field.

5. The 3D geological modeling method based on joint simulation of attribute variables according to claim 3, wherein The determining the number of Gaussian random fields, cut-off rules, cut-off thresholds, the number of nested structures, and nested structures by using the indicator variables and the normal distribution data includes: Determining an initial nested structure based on the grade variogram in the horizontal and vertical directions; Performing trial-and-error fitting on the cross-variogram between different lithology data by using a variogram model and the lithology contact relationship, and fitting the spatial heterogeneous structure of the Gaussian random field by using the initial nested structure to determine the final nested structure.

6. The three-dimensional geological modeling method based on joint simulation of attribute variables according to claim 5, characterized in that The determining the number of Gaussian random fields, cut-off rules, cut-off thresholds, nested structures, and co-regional matrices by using the indicator variables and the normal distribution data includes: After determining the variogram structure of the Gaussian random field, creating a co-regional model to characterize the spatial cross-correlation of the variogram structure of the Gaussian random field; the co-regional model includes a co-regional matrix, and the diagonal parameters of the co-regional matrix represent the direct variogram coefficients, and the off-diagonal parameters represent the cross-variogram coefficients.

7. The 3D geological modeling method based on joint simulation of attribute variables according to claim 3, characterized in that The determining the number of Gaussian random fields, cut-off rules, cut-off thresholds, nested structures, and co-regional matrices by using the indicator variables and the normal distribution data to adjust the parameters of the initial model to obtain a target three-dimensional geological model includes: The Gibbs sampler algorithm and the transformation band algorithm are used together to simulate a Gaussian random field, and the inverse transformation algorithm is used to inverse-transform the normal distribution data into grade data, and the defined truncation rule is used to inverse-transform the indicator data into rock types.

8. The 3D geological modeling method based on joint simulation of attribute variables according to claim 1, characterized in that After obtaining the target three-dimensional geological model, the method further includes: Using the target three-dimensional geological model to conduct geological simulation to obtain a corresponding first lithology proportion histogram and a first grade variogram; Comparing the first lithology proportion histogram and the first grade variogram with the second lithology proportion histogram and the second grade variogram of the borehole data to determine the simulation error; Adjusting the parameters of the target three-dimensional geological model based on the simulation error until the simulation error is lower than a preset error threshold.

9. A three-dimensional geological modeling device based on joint simulation of attribute variables, characterized in that, Including: A data acquisition module, a data processing module, and a model creation module, where The data acquisition module is configured to acquire borehole data of a mining area; the borehole data includes lithology data and grade data; The data processing module is configured to classify the lithology data, transform the lithology variables in the lithology data into indicator variables using an indicator criterion, and perform a Gaussian transformation on the grade data to transform the grade data into normal distribution data with a mean of 0 and a variance of 1; The model creation module is configured to determine the boundary type of the lithology boundary using a lag scatter plot, and in the case where the boundary type is a soft boundary, perform joint modeling based on the spatial correlation of the boundary grade to obtain an initial model, and use the indicator variables and the normal distribution data to determine the number of Gaussian random fields, truncation rules, truncation thresholds, nesting structures, and co-region matrices to adjust the parameters of the initial model to obtain a target three-dimensional geological model.

10. An electronic device includes a processor and a memory; the memory stores a computer program, wherein, The computer program, when executed by the processor, implements the three-dimensional geological modeling method based on joint simulation of attribute variables according to any one of claims 1 to 7.

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