Geological parameter spatial data acquisition method and system

Abstract

The invention provides a geological parameter spatial data acquisition method and system, and belongs to the technical field of geostatistics, and the method comprises the following steps: obtaining particle fraction test data and eye estimation data of geological parameters of sampling points in a research area; taking the particle test data as a main variable, taking the visual estimation data as an auxiliary variable, and constructing a direct variation function of the main variable, a direct variation function of the auxiliary variable and a cross variation function of the main variable and the auxiliary variable; and constructing a co-Kriging equation set according to the direct variation function of the main variable, the direct variation function of the auxiliary variable and the cross variation function of the main variable and the auxiliary variable, and calculating the value of the geological parameter of each non-sampling point in the research area. According to the method, multi-source test data can be effectively integrated, the data potential is fully mined, and the dependence on a single data source is reduced; meanwhile, cooperative information between the main variable and the auxiliary variable can be fully utilized, and the spatial estimation precision of the geological parameters of the unsaturated zone is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geostatistics, and in particular relates to a method and system for acquiring geological parameter spatial data. Background Art

[0002] Geostatistics, as a key method for studying the spatial distribution and variability of geological parameters, is widely used in aquifer evaluation, groundwater resource management and other fields.

[0003] The lithologic characteristics of the unsaturated zone significantly influence the physical and chemical properties of soils, as well as their ability to retain and migrate water. The lithologic characteristics of the unsaturated zone are key media for the mutual transformation of atmospheric precipitation, surface water, soil water, and groundwater (collectively referred to as the "four waters"), determining the processes of water infiltration, retention, evapotranspiration, and infiltration. Therefore, a deeper understanding of the lithologic properties and spatial variability of the unsaturated zone is of great scientific value and practical significance for revealing the transformation mechanisms of the "four waters," optimizing water resource management, and improving the ecological environment.

[0004] Traditionally, the lithology of the unsaturated zone has been primarily characterized by soil particle size distribution. Soil particle size distribution data are typically obtained through field sampling and laboratory sieving tests (particle size tests). While this method provides accurate lithologic parameters, it suffers from high data acquisition costs, lengthy experimental times, and limited spatial coverage of sample points. Furthermore, particle size test data suffer from data sparsity and insufficient localized sampling during large-scale spatial interpolation, making it difficult to meet the requirements for high-precision spatial variability analysis.

[0005] To compensate for the inadequacy of experimental particle size data, several spatial interpolation methods based on visually estimated data have been developed in recent years. These data, typically derived from remote sensing imagery, surface observations, and soil surveys, are relatively easy to obtain and can provide lithologic information on a large scale. However, these data suffer from high uncertainty, low precision, and difficulty in directly applying them to high-precision geostatistical analysis. Therefore, relying solely on these data can lead to significant errors, especially in areas with drastic lithologic variations. Summary of the Invention

[0006] In order to overcome the above-mentioned deficiencies in the prior art, the present invention provides a method for acquiring geological parameter spatial data, comprising the following steps:

[0007] Obtaining particle size test data and visual estimation data of geological parameters at sampling points in the study area;

[0008] Taking the particle size test data as the main variable and the visual estimation data as the secondary variable, the direct variogram of the main variable, the direct variogram of the secondary variable, and the cross variogram of the main variable and the secondary variable are constructed;

[0009] A set of cokriging equations is constructed based on the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary and secondary variables. The values of the geological parameters of each unsampled point in the study area are calculated based on the solutions of the cokriging equations.

[0010] Preferably, obtaining the particle size test data and visual estimation data of the geological parameters of the sampling points in the study area includes the following steps:

[0011] Use a geographic information system or mapping tool to evenly divide the study area into a grid of squares;

[0012] Multiple particle size test sampling points and multiple visual estimation sampling points are arranged in each square grid;

[0013] At each particle size test sampling point, stratified sampling is carried out, and the sampled soil is subjected to particle size analysis using a standard screening method to obtain the particle size test data for each sampling point;

[0014] At each visual estimation sampling point, the soil texture was classified using the dry-wet method to obtain the visual estimation data of each sampling point.

[0015] Preferably, after obtaining the particle size test data and visual estimation data of the geological parameters of the sampling points in the study area, the method further includes calculating the overall lithologic average particle size of the particle size test data, including the following steps:

[0016] Calculate the average particle size of each layer at each sampling point based on the particle size test data

[0017] The overall lithologic average particle size G of all sampling points in the study area was calculated based on the average particle size of each layer at each sampling point. p , and G p as the main variable.

[0018] Preferably, after obtaining the particle size test data and visual estimation data of the geological parameters of the sampling points in the study area, the method further includes calculating the overall lithologic average particle size of the visual estimation data, which specifically includes the following steps:

[0019] According to the empirical formula, calculate the average particle size of each layer at each sampling point corresponding to the visual estimation data

[0020] The overall lithologic average particle size H of all sampling points in the study area was calculated based on the average particle size of each layer at each sampling point corresponding to the visual estimation data. q , and H q as a secondary variable.

[0021] Preferably, the direct variogram of the primary variable and the direct variogram of the secondary variable are respectively as follows:

[0022]

[0023] Where, γ G (h) direct variogram of the main variable; γ H (h) is the direct variogram of the secondary variable; h is the distance between spatial locations; N(h) is the number of sample pairs with distance h; G P (x i ) and H q (x i ) are the main variable and the secondary variable at position x i The overall lithologic average grain size at G P (x i +h) and H q (x i +h) are respectively i The overall lithologic mean grain size of the primary and secondary variables at locations h apart.

[0024] Preferably, the cross-variogram of the primary variable and the secondary variable is as follows:

[0025]

[0026] Where, γ GH (h) Cross-variogram between the main variable and the secondary variable, G P (x i ) and H q (x i ) are the main variable and the secondary variable at position x i The overall lithologic average grain size at G P (x i +h) and H q (x i +h) are respectively i The overall lithologic average grain size of the primary and secondary variables at locations h apart, and N(h) is the number of pairs of samples at distance h.

[0027] Preferably, the cokriging equations are as follows:

[0028]

[0029] Where, σ 2 (x0) is the error variance of the estimated point x0, which indicates the uncertainty of the estimate; γ G (x0,x0) is the variance of the main variable in x0; γ G (x0,x i ) is the main variable at position x0 and position x i The covariance between γ GH (x0,x j) is the main variable and the secondary variable at position x0 and position x j The cross covariance between them, s and t are the number of sample points of the main variable and the secondary variable respectively.

[0030] The present invention also provides a geological parameter spatial data acquisition system, comprising:

[0031] Data acquisition module, used to obtain particle size test data and visual estimation data of geological parameters of sampling points in the study area;

[0032] Function construction module, used to take particle size test data as the main variable and visual estimation data as the secondary variable, to construct the direct variogram of the main variable, the direct variogram of the secondary variable, and the cross variogram of the main variable and the secondary variable;

[0033] The geological parameter calculation module is used to construct a cokriging equation group based on the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary variable and the secondary variable, and calculate the numerical value of the geological parameters of each unsampled point in the study area based on the solution of the cokriging equation group.

[0034] The method for acquiring geological parameter spatial data provided by the present invention has the following beneficial effects:

[0035] The present invention uses particle size test data as the primary variable and visual estimation data as the secondary variable, and constructs the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary and secondary variables. This method can effectively integrate multi-source test data, fully tap the data potential, and reduce dependence on a single data source. The cokriging equations are constructed using the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary and secondary variables. The numerical values of the geological parameters of each unsampled point in the study area are calculated based on the solutions of the cokriging equations. This method can fully utilize the collaborative information between the primary and secondary variables, overcome the uncertainty and data sparsity problems caused by a single data source, and significantly improve the spatial estimation accuracy of geological parameters in the unsaturated zone. At the same time, while ensuring estimation accuracy, it significantly reduces the workload of field sampling and testing, improves research efficiency, and enhances the reliability and scientific nature of the results through the collaborative analysis of multi-parameter data. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0037] Figure 1 Flowchart of a method for acquiring geological parameter spatial data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the technical solutions of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0040] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meaning of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0041] Example

[0042] The present invention provides a method for obtaining geological parameter spatial data, specifically Figure 1 As shown in the figure, the aim is to improve the spatial estimation accuracy of unsaturated zone lithologic parameters by integrating the experimental data of multiple geological parameters. This method is applicable to the spatial estimation of multiple geological parameters, such as organic matter content, porosity, water content, etc. The implementation steps of this patent focus on the spatial estimation of particle size, including the following steps:

[0043] Step 1: Arrange sampling points.

[0044] (1) The study area was divided into multiple 3 km × 3 km square grids. N sampling points were arranged in each grid. The sampling points were divided into two categories:

[0045] ① Particle classification test sampling points (p): used for accurate particle classification test data collection;

[0046] ② Visual estimation sampling points (q points): Auxiliary data collection is performed through visual estimation.

[0047] Where n=p+q.

[0048] Specifically, the square grid division and sampling point marking operations are as follows:

[0049] ① Use a geographic information system (GIS) or mapping tool to divide the study area into multiple square grids, ensuring that the square grids are evenly distributed.

[0050] ②Determine the specific coordinates of each sampling point and record them.

[0051] ③Mark the sampling point location and set up markers on site if necessary.

[0052] Step 2: Sampling and data acquisition.

[0053] (1) Particle size test data collection, that is, data collection through the particle size test method. The particle size test method is also called particle size analysis method. It is a method of studying the characteristics of soil or other granular materials by analyzing the particle size distribution. This method is commonly used in fields such as soil science, soil and water conservation, and geology. The purpose is to determine the relative content of particles of different particle size ranges in the sample. The particle size test method can be carried out using screening method (for large particles) and sedimentation method (for fine particles). Combined with the distribution of particles of different particle sizes, the permeability, structural stability and other properties of the soil can be evaluated.

[0054] In this invention, it refers to the soil particle data in geology, and the specific acquisition process is as follows:

[0055] ①At each particle size test sampling point, stratified sampling is carried out to ensure the representativeness of samples from each soil layer.

[0056] ② Use the standard screening method to perform particle analysis on the sampled soil and draw a particle size distribution curve. Particle analysis refers to the analysis of soil particle size distribution. The final collected data is the soil particle size distribution of different layers, which is represented by the particle size distribution curve.

[0057] (2) Visual data collection, that is, data collection through visual estimation. Visual estimation is a method of directly measuring data without experimental means, obtained through visual observation and subjective judgment based on visual inspection or secondary information such as remote sensing images, surface surveys, and soil census data. In the fields of soil science and agriculture, visual estimation is often used to quickly assess soil characteristics such as particle size, structure, and color, or to assess plant growth and coverage. It relies on the observer's experience and intuition and does not require complex instruments and equipment.

[0058] In the present invention, at each estimated sampling point, the dry-wet method is used to classify soil texture, that is, the process of dividing soil into different categories based on the size and proportion of soil particles.

[0059] Step 3: Calculate the average particle size.

[0060] (1) The process of obtaining the overall lithologic average particle size of the particle size test is as follows:

[0061] ① Calculate the average particle size of each layer based on the particle size test results It is calculated by the following formula:

[0062]

[0063] Where, is the average particle size of the i-th layer at the p-th sampling point obtained from the particle classification test; r j is the particle size of the jth particle size distribution; w ij is the percentage of the jth particle size distribution in the i-th layer; k is the number of particle groups; m is the number of layers at the p-th sampling point.

[0064] ②Calculate the overall average particle size G of the lithology in the particle size test p The overall lithologic average particle size refers to the thickness-weighted comprehensive average of the particle sizes of all lithologic layers within a sampling point. It reflects the overall particle distribution characteristics of the sampling point and is calculated using the following formula:

[0065]

[0066] Where, is the lithologic thickness of the i-th layer at the p-th sampling point, m is the number of layers at the p-th sampling point, is the average particle size of the i-th layer at the p-th sampling point obtained from the particle classification test.

[0067] In order to better understand the overall average particle size of lithology, an example is given. For example, if a sampling point contains a sand layer (2m thick, average particle size 0.5mm) and a clay layer (1m thick, average particle size 0.01mm), then G p The representative particle size of the entire point (eg, about 0.34 mm) is obtained by weighting by thickness.

[0068] (2) The process of obtaining the overall lithologic average particle size from the visual estimation test is as follows:

[0069] ① Calculate the average particle size by visual estimation according to the empirical formula

[0070]

[0071] Where, is the average particle size of the i-th layer at the q-th sampling point; v t is the proportion of the tth lithologic layer; z it is the particle size of the tth lithologic layer in the i-th layer; u is the number of lithologic categories classified by visual estimation.

[0072] ②Calculate the overall average lithologic particle size H of the visual estimation test q :

[0073]

[0074] Where, is the lithologic thickness of the i-th layer at the q-th sampling point determined by visual estimation; o is the number of layers at the q-th sampling point, is the average particle size of the i-th layer at the q-th sampling point.

[0075] Step 4: Data preprocessing and analysis.

[0076] (1) Statistical analysis was performed on the overall average lithologic particle size of multiple particle size test sampling points and multiple estimated sampling points within a grid, and basic statistical parameters (such as maximum value, minimum value, mean value and variance) were calculated.

[0077] (2) Check the consistency and outliers of the data and make corrections or eliminations if necessary.

[0078] (3) Verify the collaborative regionalization characteristics of particle size test data and visual estimation data to ensure that the two data have statistical correlation and spatial variation consistency.

[0079] Step 5: Cokriging valuation.

[0080] (1) Construct the cokriging equations:

[0081] ① Main variable: Overall lithologic average particle size G of the particle size test p , used to provide accurate lithologic distribution information.

[0082] ② Secondary variable: Overall lithologic average particle size H of the visual estimation test q , used to supplement spatial distribution information.

[0083] (2) Construct theoretical variogram:

[0084] ① The direct variation functions of the main variable and the secondary variable are as follows:

[0085]

[0086]

[0087] Where, γ G (h) direct variogram of the main variable; γ H(h) is the direct variogram of the secondary variable; h is the distance between spatial locations; N(h) is the number of sample pairs with distance h; G P (x i ) and H q (x i ) are the main variable and the secondary variable at position x i The overall lithologic average grain size at G P (x i +h) and H q (x i +h) are respectively i The overall lithologic mean grain size of the primary and secondary variables at locations h apart.

[0088] ②The cross-variogram of the main variable and the secondary variable is as follows:

[0089]

[0090] Where, γ GH (h) Cross-variogram between the main variable and the secondary variable, G P (x i ) and H q (x i ) are the main variable and the secondary variable at position x i The overall lithologic average grain size at G P (x i +h) and H q (x i +h) are respectively i The overall lithologic average grain size of the primary and secondary variables at locations h apart, and N(h) is the number of pairs of samples at distance h.

[0091] ③ Fitting of variogram model.

[0092] After obtaining the direct variogram and cross variogram through experimental calculation, a theoretical model (such as spherical model, exponential model or Gaussian model) is selected for fitting to ensure the stability of the variogram and the applicability of the co-regionalized variables.

[0093] (3) Establish the cokriging equations.

[0094] The core of cokriging estimation is to construct a set of cokriging equations to calculate the estimated value of each unknown point. The set of equations is as follows:

[0095]

[0096] Where, α i and β jare the weight coefficients of the main variable and the secondary variable respectively; v is the Lagrange multiplier, which is used for smoothness constraint; x0 is the position of the point to be estimated; s and t are the number of sample points of the main variable and the secondary variable respectively; γ G (x i ,x j ) is the main variable at position x i and position x j The covariance between γ GH (x i ,x j ) is the main variable and the secondary variable at position x i and position x j The cross covariance between γ GH (x j ,x k ) is the main variable and the secondary variable at position x j and position x k The cross covariance between γ H (x j ,x k ) is the secondary variable at position x j and position x k The covariance between γ G (x0,x k ) is the main variable at position x0 and position x k The covariance between γ GH (x0,x k ) is the main variable and the secondary variable at position x0 and position x k The cross covariance between .

[0097] (4) Calculate the cokriging estimate.

[0098] ① According to the Cokriging equations, solve the weight coefficient α i and β j , the final valuation formula is:

[0099]

[0100] Where Z(x0) is the estimated average particle size of the overall lithology at the estimated point x0; G p (x i ) and H q (x j ) are the main variable and the secondary variable at position x i and x j The overall lithologic average grain size at , which are all known values; s and t are the number of sample points of the main variable and the secondary variable, respectively.

[0101] ②The valuation variance is:

[0102]

[0103] Where σ 2 (x0) is the error variance of the estimated point x0, which indicates the uncertainty of the estimate; γ G (x0,x0) is the variance of the main variable in x0; γ G (x0,x i ) is the main variable at position x0 and position x i The covariance between γ GH (x0,x j ) is the main variable and the secondary variable at position x0 and position x j The cross covariance between them, s and t are the number of sample points of the main variable and the secondary variable respectively.

[0104] Step 6: Output and visualization.

[0105] (1) Valuation results.

[0106] ① According to the solution of the cokriging equations, calculate the estimated value Z(x0) and the estimated variance σ of each unsampled point in the study area (the solution is the value of other points in each grid except the sampling points) 2 (x0).

[0107] ② Output valuation results, including point value table and error analysis report.

[0108] (2)Visualization.

[0109] ① Use GIS tools to draw the spatial distribution map of the average particle size of the lithology.

[0110] ② Draw a valuation variance distribution chart to show the uncertainty area of the valuation.

[0111] The present invention also provides a geological parameter spatial data acquisition system, comprising:

[0112] Data acquisition module, used to obtain particle size test data and visual estimation data of geological parameters of sampling points in the study area;

[0113] Function construction module, used to take particle size test data as the main variable and visual estimation data as the secondary variable, to construct the direct variogram of the main variable, the direct variogram of the secondary variable, and the cross variogram of the main variable and the secondary variable;

[0114] The geological parameter calculation module is used to construct a cokriging equation group based on the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary variable and the secondary variable, and calculate the numerical value of the geological parameters of each unsampled point in the study area based on the solution of the cokriging equation group.

[0115] The method of the present invention has the following significant advantages:

[0116] (1) Multi-parameter data integration capability: This method not only supports the spatial estimation of particle size distribution data, but also can integrate data on multiple geological parameters such as organic matter content, porosity, and water content, providing comprehensive spatial estimation capabilities to meet different research needs.

[0117] (2) Significantly improve the estimation accuracy: Through the co-kriging method, the synergistic information between particle size as the main variable and other auxiliary geological parameters is fully utilized to significantly improve the spatial estimation accuracy of unsaturated zone particle size, overcoming the uncertainty and data sparsity problems caused by a single data source.

[0118] (3) Efficient data utilization and cost savings: This method effectively integrates multi-source test data, fully taps the data potential, and reduces dependence on a single data source, thereby reducing the cost of data collection and processing and saving a lot of manpower and material resources.

[0119] (4) Improve research efficiency and reliability: While ensuring the accuracy of estimation, the workload of field sampling and testing is greatly reduced, the research efficiency is improved, and the reliability and scientific nature of the results are enhanced through the collaborative analysis of multi-parameter data.

[0120] (5) Broad prospects for promotion and application: This method not only has broad application prospects in the fields of geostatistics and hydrological research, but can also be extended to many fields such as environmental science and agricultural soil research, and has high promotion value and practical potential.

[0121] The above-described embodiments are only preferred specific implementation methods of the present invention, and the protection scope of the present invention is not limited thereto. Any simple changes or equivalent replacements of the technical solutions that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention fall within the protection scope of the present invention.

Claims

1. A method for acquiring geological parameter spatial data, characterized in that: The steps include: Obtaining particle size test data and visual estimation data of geological parameters at sampling points in the study area; Taking the particle size test data as the main variable and the visual estimation data as the secondary variable, the direct variogram of the main variable, the direct variogram of the secondary variable, and the cross variogram of the main variable and the secondary variable are constructed; A set of cokriging equations is constructed based on the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary and secondary variables. The values of the geological parameters of each unsampled point in the study area are calculated based on the solutions of the cokriging equations.

2. The method for acquiring geological parameter spatial data according to claim 1, characterized in that: Obtaining particle size test data and visual estimation data of geological parameters at sampling points in the study area includes the following steps: Use a geographic information system or mapping tool to evenly divide the study area into a grid of squares; Multiple particle size test sampling points and multiple visual estimation sampling points are arranged in each square grid; At each particle size test sampling point, stratified sampling is carried out, and the sampled soil is subjected to particle size analysis using a standard screening method to obtain the particle size test data for each sampling point; At each visual estimation sampling point, the soil texture was classified using the dry-wet method to obtain the visual estimation data of each sampling point.

3. The method for acquiring geological parameter spatial data according to claim 1, wherein: After obtaining the particle size test data and visual estimation data of the geological parameters of the sampling points in the study area, the method further includes calculating the overall lithologic average particle size of the particle size test data, including the following steps: Calculate the average particle size of each layer at each sampling point based on the particle size test data The overall lithologic average particle size G of all sampling points in the study area was calculated based on the average particle size of each layer at each sampling point. p , and G p as the main variable.

4. The method for acquiring geological parameter spatial data according to claim 1, wherein: After obtaining the particle size test data and visual estimation data of the geological parameters of the sampling points in the study area, the method further includes calculating the overall lithologic average particle size of the visual estimation data, which specifically includes the following steps: According to the empirical formula, calculate the average particle size of each layer at each sampling point corresponding to the visual estimation data The overall lithologic average particle size H of all sampling points in the study area was calculated based on the average particle size of each layer at each sampling point corresponding to the visual estimation data. q , and H q as a secondary variable.

5. The method for acquiring geological parameter spatial data according to claim 1, wherein: The direct variogram of the main variable and the direct variogram of the secondary variable are as follows: Where, γ G (h) direct variogram of the main variable; γ H (h) is the direct variogram of the secondary variable; h is the distance between spatial locations; N(h) is the number of sample pairs with distance h; G P (x i ) and H q (x i ) are the main variable and the secondary variable at position x i The overall lithologic average grain size at G P (x i +h) and H q (x i +h) are respectively i The overall lithologic mean grain size of the primary and secondary variables at locations h apart.

6. The method for acquiring geological parameter spatial data according to claim 1, wherein: The cross-variogram of the main variable and the secondary variable is as follows: Where, γ GH (h) Cross-variogram between the main variable and the secondary variable, G P (x i ) and H q (x i ) are the main variable and the secondary variable at position x i The overall lithologic average grain size at G P (x i +h) and H q (x i +h) are respectively i The overall lithologic average grain size of the primary and secondary variables at locations h apart, and N(h) is the number of pairs of samples at distance h.

7. The method for acquiring geological parameter spatial data according to claim 1, wherein: The cokriging equations are as follows: Where σ 2 (x0) is the error variance of the estimated point x0, which indicates the uncertainty of the estimate; γ G (x0,x0) is the variance of the main variable in x0; γ G (x0,x i ) is the main variable at position x0 and position x i The covariance between γ GH (x0,x j ) is the main variable and the secondary variable at position x0 and position x j The cross covariance between them, s and t are the number of sample points of the main variable and the secondary variable respectively.

8. A geological parameter spatial data acquisition system, characterized in that: include: Data acquisition module, used to obtain particle size test data and visual estimation data of geological parameters of sampling points in the study area; Function construction module, used to take particle size test data as the main variable and visual estimation data as the secondary variable, to construct the direct variogram of the main variable, the direct variogram of the secondary variable, and the cross variogram of the main variable and the secondary variable; The geological parameter calculation module is used to construct a cokriging equation group based on the direct variogram of the primary variable, the direct variogram of the secondary variable, and the cross variogram of the primary variable and the secondary variable, and calculate the numerical value of the geological parameters of each unsampled point in the study area based on the solution of the cokriging equation group.

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