CN116467855A - Multi-point geostatistical simulation method based on multi-scale survey geochemical data - Google Patents
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Abstract
The invention provides a multipoint geostatistical simulation method based on multi-scale survey geochemical data. Aiming at the characteristics of multiple scale types, uneven distribution and the like of the exploration geochemical data, the exploration geochemical data with different scales is converted into grid images by a deterministic interpolation technology, and the background in the images is removed through fractal filtering to extract a complexity mode, and the complexity mode is used as a training image; constructing a grid image of multi-scale survey geochemical data according to a gridding mode, and performing fractal filtering to obtain a geochemical element anomaly graph serving as a simulation grid and an element content background graph serving as a trend component; and performing downscaling simulation by using a direct sampling method, interpolating the trend component, and combining the trend component with a simulation result to realize characterization of the geochemical field with fine resolution. The invention expands the applicability of multi-point geostatistics in multi-scale exploration of geochemical data, realizes trend-based fine-scale geologic modeling, and can provide finer geochemical anomalies for prospecting work.
Description
Technical Field
The invention relates to the field of geochemical exploration and geologic modeling, in particular to a multipoint geostatistical simulation method based on multi-scale exploration geochemical data.
Background
The multipoint geostatistical simulation (MPS) method reads the multipoint statistical characteristics of the object through the training image, and because the training image can be obtained by an algorithm based on a target or a pixel, the method is not limited by data conditions to a certain extent, and therefore, the defect that a variation function in two-point geostatistical simulation cannot represent the spatial relationship between a plurality of points at a certain position can be overcome, and the spatial variability of a complex geological structure is effectively reflected (Strebelle & journal, 2001). The training image in the multipoint geostatistical simulation contains heterogeneous characteristics of reservoir or oilfield related geological variables, and meanwhile attribute distribution of known observation points is reserved, so that the method can describe a complex spatial structure of heterogeneous phenomena more accurately.
In order to characterize complex and heterogeneous geologic structures, the core idea of the MPS algorithm is to extract simulated values of unknown location points from training image data based on all condition information within a particular region. The attributes of these data or images may be specific to being decentralized or exhaustive, they provide structural information corresponding to the spatial attribute distribution pattern of the geologic variables, and adjustments in the available information (hard/soft data) may be used to constrain the simulation results. Depending on whether the training image is used for probability statistics or data pattern generation during simulation, the multi-point geostatistical simulation algorithm can be divided into pattern-based simulation and pel-based simulation (mariothoz, 2014). The pixel-based multi-point geostatistical simulation process mainly comprises the steps of simulating the input of grids and training images, acquiring data events and conditional probability distribution functions and simulating the attribute values of the grids to be simulated (Strebelle and Levy, 2008).
In the development process of multipoint geostatistical simulation, the SNESIM method and the DS method are two important development stages in the pixel-based simulation method. The SNESIM method is initially applied to reservoir simulation, the algorithm introduces a data structure of a search tree to store modes appearing in training images, all multi-point modes and corresponding conditional probability distribution functions (cpdf) can be obtained by only scanning the training images once by establishing the search tree, and the running time of the MPS algorithm is greatly saved (Boucher, 2009; strebelle, 2002). Meanwhile, strebelle also applies the concept of multiple meshes to the SNESIM algorithm to capture heterogeneities at different scales. However, when applied to large reservoir simulations, the relevant scholars point out that SNESIM requires a lot of memory to meet the CPU requirements (tahmasbi, 2018;Zakeri and Mariethoz,2021). Furthermore, SNESIM is only applicable to simpler classification variables, when the attribute class of the training image exceeds a certain number, it will seriously affect the continuity of the simulated reservoir, and multiple grid simulation will lead to redistribution of the condition data, a process which may lead to larger errors (Caters & ARPat,2004; xie & Niu, 2022). The DS algorithm can directly sample training images without storing available data events in a database, and subsequent application studies also show that the method has better applicability to both classification variables and continuous variables (Mariethoz, 2010,2011). The method is mainly characterized in that the DS algorithm simulates n grid nodes closest to the position of the point to be simulated in the search area as a neighborhood, and the effective range in the neighborhood is changed along with the change of the position of the access node in the simulation process, so that the defect that the fixed data template in the SNESIM is difficult to reproduce different scale space distribution modes is overcome. However, for most pixel-based simulation methods, where the resolution of the training image is low or the number of samples is small, using current MPS techniques can create more serious discontinuities and impractical structures, while using very high resolution or multiple training images can result in higher costs.
Geochemical data is typical spatial attribute data, and is characterized by various characteristics such as non-uniformity, spatial anisotropy and non-stationarity of element content distribution in a geologic body. In addition, the distribution pattern of the geochemical element concentration has scale invariance, and the survey geochemical data is a continuous geological variable, meets the assumption of stationarity, and can extrapolate possible values of the non-sampling points according to continuous changes in the local area. However, in actual survey work, it is difficult to find a training image that can be subjected to similarity comparison because the survey geochemical data has a complicated spatial distribution characteristic. Fortunately, geochemical data in the same area often has data with multiple scale types, and effective fusion of training images obtained by converting the multi-scale data has important significance for improving accuracy of MPS simulation results. The direct sampling method is used as a general super-resolution method, can simulate without an external training image, has non-parametric statistical characteristics, can process continuous variables and classified variables and is popularized to multiple variables, so that the direct sampling method is completely suitable for constructing an geochemical field.
In conclusion, how to extract the complexity mode of the geochemical element distribution according to the multi-scale exploration geochemical data and on the premise of only the data, and the multi-point geostatistical simulation of the geochemical element distribution is carried out as a training image, and the method has important significance for realizing the accurate characterization of the fine resolution geochemical field.
Disclosure of Invention
The invention aims to solve the technical problems that: according to multi-scale exploration geochemistry data, extracting a complexity mode of the geochemistry data and taking the complexity mode as a training image to carry out multi-point geostatistical simulation of geochemistry element distribution, so that accurate representation of a fine resolution geochemistry field is realized.
In order to solve the technical problem, the invention adopts the following technical scheme: a multipoint geostatistical simulation method based on multi-scale survey geochemical data is provided, comprising the following steps:
s1: loading geochemical measurement data of water system sediments with different scales, constructing a grid image according to the spatial scale of sample data with the maximum scale, and assigning attribute values of the sample data to grid center nodes;
s2: fractal filtering is carried out on the constructed grid image, the decomposed low-frequency component, namely geochemistry anomaly, is used as a simulation grid of a multipoint geological simulation algorithm, and the trend component, namely geochemistry background, is used for generating a trend field with a fine scale;
s3: if two scale chemical detection data exist in the research area in the same space range, respectively generating grids of the two scales according to the space scales corresponding to the large scale chemical detection data and the small scale chemical detection data, and then respectively calculating non-sampled attribute values near the sampling points by adopting a deterministic interpolation technology to obtain a large scale grid image and a small scale grid image;
s4: removing geochemical backgrounds of the large scale grid image and the small scale grid image respectively by a fractal filtering method to obtain abnormal grid images of the large scale grid image and the small scale grid image, and taking the abnormal grid images of the two scales as training images of a direct sampling algorithm;
s5: setting a search radius r, a distance threshold t, a maximum proportion f allowed to scan the training image, a maximum node number n allowed in a search neighborhood and the iteration number of simulation according to the training image and the simulation grid, and continuously performing downscaling simulation by using a direct sampling algorithm until the grid scale of the required target space is reached;
s6: and (3) carrying out deterministic interpolation on the geochemical background in the step S2 according to the grid scale realized by simulation, and integrating the interpolation result with the simulation result in the step S5 to obtain a final exploration geochemical element distribution diagram with finer scale.
Further, in step S1, the data set of known sampling points is constructed by superimposing the survey geochemical data of a small scale as the supplementary data to the non-sampled area of a large scale with the survey geochemical data of a large scale as the basic sampling data.
Further, in step S1, the distances between the grid center node and the sampling points in the grid are counted, and the attribute value is assigned to the grid center node by adopting a nearest rule, averaging or weighted averaging, and selecting the sampling point mode with the highest occurrence frequency.
Further, in step S2, the fractal filtering adopts an S-A fractal model of energy spectrum density S and accumulated arese:Sup>A A (more than or equal to S), and se:Sup>A threshold value for dividing element geochemical background and anomaly is determined according to the slope of curve fitting by making se:Sup>A double logarithmic curve of A (more than or equal to S) and S.
Optionally, in step S3, the deterministic interpolation method is a radial basis function method RBF or a kriging interpolation method.
Optionally, in step S3, the deterministic interpolation method is an inverse distance weighting method IDW.
Preferably, in the inverse distance weighting method IDW, the power exponent is equal to 2, the search neighborhood is set to be a standard circle, the number of maximum adjacent sample points contained in the search radius is 15, and the minimum number is 10.
Preferably, in step S5, parameters with high accuracy and fast running speed of the simulation result are selected, and the range of n being greater than or equal to 30,0.5 and less than or equal to f <1, t being less than or equal to 0.1 is satisfied by the distance threshold t, the maximum proportion f allowed to scan the training image, and the maximum number n of nodes allowed in the search neighborhood.
Preferably, in step S5, the ratio of each downscaling is set to 2.
Preferably, in step S5, the ratio of the initial simulated grid scale to the final simulated grid scale is not more than 2 3 。
The technical scheme provided by the invention has the following beneficial effects:
the invention provides a multipoint geostatistical simulation method based on multi-scale exploration geochemical data. Firstly, converting exploration geochemical data of different scales into grid images by using a deterministic interpolation technology, removing the background in the images by a fractal filtering method to extract a complexity mode, and taking the complexity mode as a training image; then constructing a grid image of the multi-scale survey geochemical data according to a gridding mode, and performing fractal filtering to obtain an abnormal map of the geochemical elements as a simulation grid, wherein a background map of the element content is used as a trend component; and finally, performing downscaling simulation by using a direct sampling method, interpolating the trend component, and combining the trend component with a simulation result to realize characterization of the geochemical field with fine resolution. Therefore, the method realizes the accurate characterization of the geochemical field with fine resolution, improves the accuracy of simulation realization of the reproduction of the local distribution characteristics of the geochemical element anomalies and the spatial distribution patterns with similar trends, improves the applicability of a multi-point geostatistical simulation algorithm in the spatial modeling of geochemical data in multi-scale investigation, and provides finer geochemical anomalies for the prospecting work.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a multi-point geostatistical simulation method based on multi-scale survey geochemical data in an embodiment of the invention;
FIG. 2 is a diagram of a process for integrating survey geochemical data at two scales in an embodiment of the invention;
FIG. 3 is a diagram of a process for generating a grid to be simulated in an embodiment of the invention;
FIG. 4 is a diagram of a process for generating a two-scale training image in an embodiment of the invention;
FIG. 5 is a diagram of a comparison of an analog implementation with IDW-based interpolation results in an embodiment of the invention;
FIG. 6 is a graph of the horizontal variation function for 15 implementations of an embodiment of the invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present invention, a detailed description of embodiments of the present invention will be made with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of a multi-point geostatistical simulation method based on multi-scale survey geochemical data according to an embodiment of the invention, which specifically includes the following steps:
step one: loading and integrating sample data with different scales.
Referring to fig. 2, in this example, geochemical measurements of 1:5 ten thousand and 1:2.5 ten thousand of aqueous sediments in the same study area were loaded, as shown in fig. 2 (a) and 2 (b). Then, 1:2.5 ten thousand of survey geochemical data is used as basic condition data, and 1:5 ten thousand of survey geochemical data is used as supplementary data to be superimposed on 1:2.5 ten thousand of non-sampled areas, so that a data set of known sampling points is constructed, as shown in fig. 2 (c).
It should be noted that: in other embodiments, the geochemical measurement data of the water system sediment can be 1:10 ten thousand or 1:5 ten thousand, or the chemical detection data of other scales fall within the protection scope of the present invention.
Step two: and generating a grid to be simulated.
Referring to fig. 3, in the present embodiment, the cell dimensions of the spatial grid are set according to the sampling interval of 1:2.5 ten thousand exploration geochemical data and a corresponding grid is constructed, and superimposed on the integrated data, as shown in fig. 3 (a). Then, referring to fig. 3 (b), by setting the search neighborhood as a standard circle, searching the size of 2 grids with a radius, counting the distance between each grid center node and the sampling point in the grid, and calculating the attribute state of the grid center node by adopting a weighted average method based on the distance, wherein the general formula is as follows:
wherein Z is j An estimated value representing an unknown point at position j, n representing the total number of known observation points for prediction, d ij Representing the distance, Z, between the known point i and the unknown point j i Is the attribute value at the known point i, p is the custom weighted power exponent.
A gridded image of the water system sediment sampling points of the research area is obtained according to the formula (1), and the gridded image is shown in fig. 3 (c). Then, the constructed grid image is subjected to fractal filtering, the decomposed geochemical anomaly is used as a simulation grid of a multipoint geologic simulation algorithm, and the geochemical background is used for generating a trend field with a fine scale. Specifically, the fractal filtering method in this embodiment adopts se:Sup>A fractal model of energy spectrum density-cumulative arese:Sup>A (S-se:Sup>A), and the expression is
A(≥S)∝S-β (2)
Wherein S is energy spectrum density, A (more than or equal to S) is the accumulated area of the energy spectrum density of the image grid unit in the frequency domain, and the index beta represents the degree of self-similarity and is reflected as the slope change of the A and S double logarithmic curve.
The S-A fractal model can effectively extract complex modes in the spatial distribution of geological datse:Sup>A, and becomes se:Sup>A preferred method for spatial mode decomposition. Fig. 3 (d) is se:Sup>A double logarithmic curve of se:Sup>A (not less than S) and S plotted after S-se:Sup>A analysis is performed according to the content of Au element in the mesh datse:Sup>A in the embodiment, se:Sup>A threshold for dividing the background and the anomaly can be determined according to the slope of curve fitting, the filtered image in the range smaller than the threshold is an anomaly map, see fig. 3 (e), and the filtered image obtained in the range larger than the threshold is se:Sup>A background map, see fig. 3 (f).
Step three: and (5) constructing a double-scale training image.
Referring to fig. 4, in the same spatial range, grids of two scales are respectively generated according to sampling intervals corresponding to geochemical data of 1:5 ten thousand and 1:2.5 ten thousand scale investigation, and then an IDW interpolation technology is adopted to calculate non-sampled attribute values near sampling points respectively, wherein the attribute values are the content of Au elements, so that 1:5 ten thousand and 1:2.5 ten thousand Au element geochemical diagrams are obtained, as shown in fig. 4 (a) and 4 (c). And then, respectively eliminating the background in the 1:5 ten thousand and 1:2.5 ten thousand Au element geochemistry diagrams by an S-A method to obtain abnormal images of the two, and further taking the abnormal images of the two scales as training images of se:Sup>A next-step multipoint geostatistical simulation algorithm.
It should be noted that, the IDW interpolation technique adopted in this embodiment is only a preferred implementation, and in other embodiments, the deterministic interpolation method may be a spatial interpolation method such as Radial Basis Function (RBF) or kriging interpolation, and good interpolation effects can be achieved.
Step four: and (5) realizing multipoint geostatistical simulation.
Referring to fig. 5, first, 1:5 ten thousand and 1:2.5 ten thousand element anomaly maps are used as training images of a direct sampling algorithm, as shown in fig. 5 (a), and anomaly values of a sample point grid image are used as condition data, as shown in fig. 5 (b). Then, through setting up the search radius r, the distance threshold t, the maximum number of nodes n allowed in the search neighborhood and the iteration number of the simulation, the downscaling simulation is continuously carried out by utilizing a Direct Sampling (DS) algorithm until the grid scale of the required target space is reached, the setting of simulation parameters is shown in a table 1, the code of the algorithm is disclosed by a geology statistics algorithm and image analysis research group of the university of Loose, and the specific implementation process https:// wp. Unil. Ch/gaia/mps/. Can be further known by referring to the official website of the team. Wherein, as a training image with double scales is used, two candidate data events N can be obtained after the searching process is carried out for each grid node x to be simulated y From which data event N is then selected and modeled x N with minimum distance y Is assigned to the current simulation node x. Secondly, according to the grid scale of the simulation implementation, IDW interpolation is carried out by taking the background value of the grid image of the sampling point as the condition data, as shown in (d) of fig. 5, and the interpolation result is integrated with the simulation result (c) of the direct sampling method, so that a geochemical element distribution diagram with finer scale is finally obtained, as shown in (e) of fig. 5. Meanwhile, in the embodiment of the present invention, the element content of the sample point gridding image is also used as the condition data to directly perform IDW interpolation, as shown in fig. 5 (f). From a comparison of simulation implementation 1 with the element content IDW interpolation results, it can be seen that simulation implementation 1 reduces the interference of uncorrelated background with the indication of mineralized geochemical anomalies.
Table 1 direct sampling algorithm analog parameter table
Referring to fig. 6, fig. 6 is a graph of horizontal direction variation functions for 15 simulation implementations in an embodiment of the present invention. As can be seen from fig. 6, the variation function curve of the simulation result is between the variation function curve of 1:5 ten thousand and the variation function curve of 1:2.5 ten thousand IDW interpolation, and the similarity between the first half section curve of the rapid variation and the curve of 1:5 ten thousand is higher, and the variation trend of the second half section is similar to that of the curve of 1:2.5 ten thousand. The variation function curve of the interpolation result of the gridding sampling data IDW is slightly upward deviated from the variation function curve of the interpolation of 1:5 ten thousand IDW. The feature display simulation results give consideration to the space variability and structural features of the two scale training images, and the result obtained by using only simple IDW interpolation is corrected to a certain extent.
Referring to fig. 7, a schematic diagram of an entity structure of an electronic device is illustrated, where the electronic device may include: processor (processor) 610, communication interface (Communications Interface) 620, memory (memory) 630, and communication bus 640, wherein processor 610, communication interface 620, memory 630 communicate with each other via communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform the steps of the multi-point geostatistical simulation method based on multi-scale survey geochemical data described above, including in particular: s1: loading geochemical measurement data of water system sediments with different scales, constructing a grid image according to the spatial scale of sample data with the maximum scale, and assigning attribute values of the sample data to grid center nodes; s2: fractal filtering is carried out on the constructed grid image, the decomposed low-frequency component, namely geochemistry anomaly, is used as a simulation grid of a multipoint geological simulation algorithm, and the trend component, namely geochemistry background, is used for generating a trend field with a fine scale; s3: if two scale chemical detection data exist in the research area in the same space range, respectively generating grids of the two scales according to the space scales corresponding to the large scale chemical detection data and the small scale chemical detection data, and then respectively calculating non-sampled attribute values near the sampling points by adopting a deterministic interpolation technology to obtain a large scale grid image and a small scale grid image; s4: removing geochemical backgrounds of the large scale grid image and the small scale grid image respectively by a fractal filtering method to obtain abnormal grid images of the large scale grid image and the small scale grid image, and taking the abnormal grid images of the two scales as training images of a direct sampling algorithm; s5: setting a search radius r, a distance threshold t, a maximum proportion f allowed to scan the training image, a maximum node number n allowed in a search neighborhood and the iteration number of simulation according to the training image and the simulation grid, and continuously performing downscaling simulation by using a direct sampling algorithm until the grid scale of the required target space is reached; s6: and (3) carrying out deterministic interpolation on the geochemical background in the step S2 according to the grid scale realized by simulation, and integrating the interpolation result with the simulation result in the step S5 to obtain a final exploration geochemical element distribution diagram with finer scale.
Further, the logic instructions in the memory 630 may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.
In still another aspect, an embodiment of the present invention further provides a storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the multi-point geostatistical simulation method based on multi-scale survey geochemical data described above, and specifically includes: s1: loading geochemical measurement data of water system sediments with different scales, constructing a grid image according to the spatial scale of sample data with the maximum scale, and assigning attribute values of the sample data to grid center nodes; s2: fractal filtering is carried out on the constructed grid image, the decomposed low-frequency component, namely geochemistry anomaly, is used as a simulation grid of a multipoint geological simulation algorithm, and the trend component, namely geochemistry background, is used for generating a trend field with a fine scale; s3: if two scale chemical detection data exist in the research area in the same space range, respectively generating grids of the two scales according to the space scales corresponding to the large scale chemical detection data and the small scale chemical detection data, and then respectively calculating non-sampled attribute values near the sampling points by adopting a deterministic interpolation technology to obtain a large scale grid image and a small scale grid image; s4: removing geochemical backgrounds of the large scale grid image and the small scale grid image respectively by a fractal filtering method to obtain abnormal grid images of the large scale grid image and the small scale grid image, and taking the abnormal grid images of the two scales as training images of a direct sampling algorithm; s5: setting a search radius r, a distance threshold t, a maximum proportion f allowed to scan the training image, a maximum node number n allowed in a search neighborhood and the iteration number of simulation according to the training image and the simulation grid, and continuously performing downscaling simulation by using a direct sampling algorithm until the grid scale of the required target space is reached; s6: and (3) carrying out deterministic interpolation on the geochemical background in the step S2 according to the grid scale realized by simulation, and integrating the interpolation result with the simulation result in the step S5 to obtain a final exploration geochemical element distribution diagram with finer scale.
The multi-point geostatistical simulation method based on multi-scale survey geochemical data has the following beneficial effects: according to the invention, the multi-scale characteristics of the exploration geochemical data are fully considered, anomalies obtained by fractal filtering of different-scale chemical detection data and grid data to be simulated are used as training images and condition data of a multi-point statistical simulation algorithm (mainly a direct sampling method), so that the applicability of the direct sampling method in the exploration geochemical space modeling is effectively improved, the constructed geochemical element distribution can be effectively combined with the implicit distribution characteristics of the indication mineralized geochemical anomalies in the different-scale chemical detection data, the identification capability of weak and slow geochemical anomalies is further improved, and a reference basis is provided for subsequent prospecting work.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third, etc. do not denote any order, but rather the terms first, second, third, etc. are used to interpret the terms as labels.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A multi-point geostatistical simulation method based on multi-scale survey geochemical data, comprising the steps of:
s1: loading geochemical measurement data of water system sediments with different scales, constructing a grid image according to the spatial scale of sample data with the maximum scale, and assigning attribute values of the sample data to grid center nodes;
s2: fractal filtering is carried out on the constructed grid image, the decomposed low-frequency component, namely geochemistry anomaly, is used as a simulation grid of a multipoint geological simulation algorithm, and the trend component, namely geochemistry background, is used for generating a trend field with a fine scale;
s3: if two scale chemical detection data exist in the research area in the same space range, respectively generating grids of the two scales according to the space scales corresponding to the large scale chemical detection data and the small scale chemical detection data, and then respectively calculating non-sampled attribute values near the sampling points by adopting a deterministic interpolation technology to obtain a large scale grid image and a small scale grid image;
s4: removing geochemical backgrounds of the large scale grid image and the small scale grid image respectively by a fractal filtering method to obtain abnormal grid images of the large scale grid image and the small scale grid image, and taking the abnormal grid images of the two scales as training images of a direct sampling algorithm;
s5: setting a search radius r, a distance threshold t, a maximum proportion f allowed to scan the training image, a maximum node number n allowed in a search neighborhood and the iteration number of simulation according to the training image and the simulation grid, and continuously performing downscaling simulation by using a direct sampling algorithm until the grid scale of the required target space is reached;
s6: and (3) carrying out deterministic interpolation on the geochemical background in the step S2 according to the grid scale realized by simulation, and integrating the interpolation result with the simulation result in the step S5 to obtain a final exploration geochemical element distribution diagram with finer scale.
2. The multi-point geostatistical simulation method based on multi-scale survey geochemical data according to claim 1, wherein in step S1, a dataset of known sampling points is constructed by superimposing a small scale survey geochemical data as supplementary data to an un-sampled area of a large scale on the basis of a large scale survey geochemical data.
3. The multi-point geostatistical simulation method based on multi-scale survey geochemical data according to claim 1, wherein in step S1, the distances between the grid center node and the sampling points in the grid are counted, and the attribute values are assigned to the grid center node by adopting a nearest rule, averaging or weighted averaging, and selecting the sampling point mode with the highest occurrence frequency.
4. The multipoint geostatistical simulation method based on the multi-scale survey geochemical datse:Sup>A according to claim 1, wherein in step S2, the fractal filtering adopts an S-se:Sup>A fractal model of energy spectrum density S and cumulative arese:Sup>A se:Sup>A (not less than S), and the threshold value for dividing element geochemical background and anomaly is determined according to the slope of curve fitting by making se:Sup>A double logarithmic curve of se:Sup>A (not less than S) and S.
5. The multi-point geostatistical simulation method based on multi-scale survey geochemical data according to claim 1, wherein in step S3, the deterministic interpolation method is a radial basis function method RBF or a kriging interpolation method.
6. The multi-point geostatistical simulation method based on multi-scale survey geochemical data according to claim 1, wherein in step S3 the deterministic interpolation method is an inverse distance weighting method IDW.
7. The multi-point geostatistical simulation method based on multi-scale survey geochemical data according to claim 6, wherein in the inverse distance weighting method IDW, the power exponent is equal to 2, the search neighborhood is set to be a standard circle, the number of maximum adjacent sample points contained in the search radius is 15, and the minimum number is 10.
8. The multipoint geostatistical simulation method based on the multi-scale survey geochemical data according to claim 1, wherein in step S5, parameters with high simulation result accuracy and high running speed are selected, and the range of n being larger than or equal to 30,0.5 is smaller than or equal to f <1, and t being smaller than or equal to 0.1 is satisfied by a distance threshold t, a maximum proportion f allowed to scan training images and a maximum number n of nodes allowed in a search neighborhood.
9. The multi-point geostatistical simulation method based on multi-scale survey geochemical data according to claim 1, wherein in step S5, the ratio of each downscaling is set to 2.
10. Multi-point geostatistical simulation based on multi-scale survey geochemical data as recited in claim 1The method is characterized in that in step S5, the ratio of the initial simulated grid scale to the final simulated grid scale is not more than 2 3 。
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