CN112685512A - Extraction method for abnormal information of structural geochemical data - Google Patents
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Abstract
The invention discloses an extraction method for abnormal information of structural geochemical data, which comprises the following steps of geochemical analysis data with spatial coordinates, statistical analysis, cluster analysis and factor analysis: s1: sampling and analyzing in a fracture and fragmentation zone to obtain structural geochemical data; s2: the obtained data is subjected to logarithm taking 10 as a base, and then the element combination is determined through clustering analysis; s3: performing factor analysis on the data set with the base 10 to obtain factors and factor scores, and using the coordinate value of each sampling point and the factor score value (namely X, Y, factor score) to define an abnormal graph; the collected data is a data set with the bottom 10, a plurality of possible solutions can be provided through cluster analysis, the final solution is selected to require subjective judgment and subsequent analysis of a researcher, a proper solution can be found from most analysis results through the analysis, the result condition is accurately analyzed, and therefore the abnormal information condition in the ore deposit under the control of the structure can be accurately extracted.
Description
Technical Field
The invention relates to the technical field of information extraction methods, in particular to an extraction method for abnormal information of structural geochemical data.
Background
Structural geochemistry (tectonochemical) studies the distribution and migration, dispersion and enrichment of elements (isotopes) in various hierarchical tectonic roles. Geochemistry is a discipline that studies the chemical composition and chemical evolution of the earth and its subsystems, including parts of the cosmic body. The chemical composition of the earth (including part of the celestial body) is mainly studied; the disciplines of chemical action mechanism and condition, symbiotic combination of elements, occurrence form of elements, migration and circulation of elements and the like in the geological process are researched.
The prospecting geochemistry is a method for finding an ore by finding abnormality through the research of element distribution in a geologic body, explaining and evaluating the abnormality and further delineating an ore-finding target area. The determination of the lower limit of the element anomaly is the basic content of the exploration of the geochemistry and is also a key problem of the exploration of the geochemistry.
The following problems are proposed aiming at the extraction method of most abnormal information of the structural geochemical data in the market at present:
1. the existing geochemical anomaly identification method needs to require that the geochemical data meet the assumed distribution form in advance, needs to set a fixed threshold value, cannot fully consider the internal relevance of element combination, the spatial structure of the data and the periodic characteristics on regional distribution in the process of distinguishing the background from the anomaly, and cannot accurately extract the anomaly information in the geochemical data;
2. the existing geochemical anomaly identification method generally adopts a single-element anomaly registration method to define combined anomaly, and because the single-element definition range is limited, the method has great limitation in the data extraction process and influences the actual data result.
Disclosure of Invention
The invention aims to provide a method for extracting abnormal information of structural geochemical data, which aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for extracting abnormal information of structural geochemical data comprises the following steps of geochemical analysis data with spatial coordinates, statistical analysis, cluster analysis and factor analysis:
s1: sampling and analyzing in a fracture and fragmentation zone to obtain structural geochemical data;
s2: the obtained data is subjected to logarithm taking 10 as a base, and then the element combination is determined through clustering analysis;
s3: performing factor analysis on the data set with the base 10 to obtain factors and factor scores, and using the coordinate value of each sampling point and the factor score value (namely X, Y, factor score) to define an abnormal graph;
s4: weak abnormal information can be enhanced by using the element combination factor score to define abnormality, and finally, deep mineralization enrichment centers can be defined by combining the abnormality with geological conditions;
s5: the occurrence of the blind ore body can be deduced according to the change gradient of the abnormal contour line, and the flow direction of the ore fluid can be deduced according to the relative position of the abnormal center of the medium-low temperature element combination and the abnormal center of the medium-high temperature element combination.
As a further scheme of the invention: the utility model discloses a data set of 10 bases is all got to cluster analysis and cluster analysis, factor analysis's output and the input interconnect of dividing into two abnormal categories of score, the output of the geochemical analysis data that has space coordinate and the input interconnect of data preliminary treatment one, the output of data preliminary treatment one and the input interconnect of data classification, the output of data classification and cluster analysis's input interconnect, cluster analysis and cluster analysis all take the data set of 10 bases, factor analysis's output and score are drawn the input interconnect of deciding the anomaly.
As a still further scheme of the invention: the output of statistical analysis and the input interconnect that the special value handled, the output that the special value handled and visual display's input interconnect, visual display's output and curve fitting's input interconnect, curve fitting's output and the input interconnect of figure show, the output of figure show and the input interconnect of weak abnormal information extraction.
As a still further scheme of the invention: the output end of the cluster analysis is connected with the input end of the second data preprocessing, the output end of the second data preprocessing is connected with the input end of the defined distance function, the output end of the defined distance function is connected with the input end of the cluster grouping, and the output end of the cluster grouping is connected with the input end of the evaluation output.
As a still further scheme of the invention: the output end of the factor analysis and the input end of the obtained factor are connected with each other, the output end of the obtained factor and the input end of the verification factor are connected with each other, the output end of the verification factor and the input end of the analysis description are connected with each other, and the output end of the analysis description and the input end of the factor application are connected with each other.
As a still further scheme of the invention: in step S1, a suitable fracture zone is selected, chemical data of the fracture zone is sampled, and structural geochemical data is obtained after analysis.
As a still further scheme of the invention: in step S2, taking all sample data and taking the logarithm of base 10 of the sample data to obtain an analysis value.
As a still further scheme of the invention: in step S, an analysis model is determined, and a discriminant function Y ═ a1X + a2X +. anxn is established, where Y is a discriminant score (discriminant value), x1x2.. xn is a variable reflecting the characteristics of the object under study, a1a2.. an is a coefficient, X ═ a11F + a12F + + a1pFp + V, X ═ a21F + a22F + + a2pFp + V2 ═ AF + V, Xi ═ ai1F + ai2F + + aipFp + Vi, Xm ═ ap1F + ap2F + + ampFm + vm, Xi — ith normalized variable, — standard regression coefficient of ith variable to pth normalized factor, F-common factor, Vi-special factor, F ═ W11X + W12X 1mXm, F ++ W21X + W2X + + + Wi ++ 2, Wi ++ W2 × W + + Wi ++ W + + W2, xf — hfi — th normalized variable, W ++ W2, W ++ W2, W ++ W +.
As a still further scheme of the invention: in step S3, the factors are analyzed and the scores of the factors are compared to determine anomalies, which can enhance weak anomaly information, and finally define deep mineralization-enrichment centers in combination with geological conditions.
As a still further scheme of the invention: in step S4, the factors are collected, and various data are compared to guide the generation of the abnormal graph.
Compared with the prior art, the invention has the beneficial effects that:
1. through the set cluster analysis, the analysis result can be simply and visually observed through the cluster analysis, a plurality of possible solutions can be provided through the cluster analysis, the final solution is selected to require the subjective judgment and subsequent analysis of a researcher, and a proper solution can be found from most analysis results through the analysis, so that the result condition can be accurately analyzed;
2. through the set factor analysis, hidden representative factors can be found out from a plurality of variables through the factor analysis, the variables with the same essence are classified into one factor, the number of the variables can be reduced, the assumption of the relation among the variables can be checked, the abnormality can be defined through the factor analysis and the factor score of the element combination, and weak abnormal information can be enhanced through the element combination;
3. through the set geochemical analysis data with the space coordinates, the sample data of a plurality of regional sections can be coordinated, the coordinated quantity is enough, the subsequent difference calculation of the sample data is convenient, the accidental situation of the subsequent analysis result is avoided, and through the set special value processing, the accidental situation in the actual analysis can be eliminated, and the truth of the analysis result is increased;
4. through the set abnormal information extraction, different numerical conditions among all elements can be accurately calculated through extracting the abnormal information, and through the set verification factor, the real numerical conditions of the factor analysis result can be further ensured through the verification of the factor result through the difference calculation.
Drawings
FIG. 1 is a schematic flow diagram of a method for extracting anomaly information from geochemical data;
FIG. 2 is a schematic block diagram of a method for extracting anomaly information from geochemical data;
FIG. 3 is a schematic diagram of a framework for extracting abnormal information in a method for extracting abnormal information from structural geochemical data;
FIG. 4 is a schematic diagram of a frame of cluster analysis in an extraction method for constructing abnormal information of geochemical data;
FIG. 5 is a schematic diagram of a framework for factor analysis in a method for extracting anomaly information from structural geochemical data;
in the figure: 1. geochemical analysis data having spatial coordinates; 2. preprocessing data; 3. classifying data; 4. clustering analysis; 5. factor analysis; 6. scoring delineation anomalies; 7. carrying out statistical analysis; 8. processing a special value; 9. visually displaying; 10. fitting a curve; 11. displaying a graph; 12. extracting weak abnormal information; 13. preprocessing the data; 14. defining a distance function; 15. clustering and grouping; 16. evaluating the output; 17. obtaining a factor; 18. verifying the factor; 19. analyzing the description; 20. factor application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 5, in an embodiment of the present invention, a method for extracting abnormal information of structural geochemical data includes a geochemical analysis data 1 with spatial coordinates, a statistical analysis 7, a cluster analysis 4 and a factor analysis 5:
s1: sampling and analyzing in a fracture and fragmentation zone to obtain structural geochemical data;
s2: the obtained data is subjected to logarithm taking 10 as a base, and then the element combination is determined through clustering analysis;
s3: performing factor analysis on the data set with the base 10 to obtain factors and factor scores, and using the coordinate value of each sampling point and the factor score value (namely X, Y, factor score) to define an abnormal graph;
s4: weak abnormal information can be enhanced by using the element combination factor score to define abnormality, and finally, deep mineralization enrichment centers can be defined by combining the abnormality with geological conditions;
s5: the occurrence of the blind ore body can be deduced according to the change gradient of the abnormal contour line, and the flow direction of the ore fluid can be deduced according to the relative position of the abnormal center of the medium-low temperature element combination and the abnormal center of the medium-high temperature element combination.
Preferably, the output end of the geochemical analysis data 1 with the space coordinates is connected with the input end of the data preprocessing one 2, the output end of the data preprocessing one 2 is connected with the input end of the data classification 3, the output end of the data classification 3 is connected with the input end of the cluster analysis 4, the output end of the cluster analysis 4 is connected with the input end of the factor analysis 5, the cluster analysis 4 and the cluster analysis 4 both adopt a data set with the base 10, and the output end of the factor analysis 5 is connected with the input end of the score delineation anomaly 6, so that the anomaly data can be processed.
Preferably, the output end of the statistical analysis 7 is connected with the input end of the special value processing 8, the output end of the special value processing 8 is connected with the input end of the visual display 9, the output end of the visual display 9 is connected with the input end of the curve fitting 10, the output end of the curve fitting 10 is connected with the input end of the graph display 11, the output end of the graph display 11 is connected with the input end of the weak abnormal information extraction 12, and the weak abnormal information can be visually displayed.
Preferably, the output end of the cluster analysis 4 is connected with the input end of the data preprocessing II 13, the output end of the data preprocessing II 13 is connected with the input end of the defined distance function 14, the output end of the defined distance function 14 is connected with the input end of the cluster grouping 15, the output end of the cluster grouping 15 is connected with the input end of the evaluation output 16, through the set cluster analysis, the analysis result can be simply and visually observed through the cluster analysis 4, a plurality of possible solutions can be provided through the cluster analysis 4, the final solution is selected to require the subjective judgment and subsequent analysis of a researcher, and through the analysis, a proper solution can be found from most analysis results, so that the result condition can be accurately analyzed.
Preferably, the output terminal of the factor analysis 5 and the input terminal of the factor 17 are connected to each other, the output terminal of the factor 17 and the input terminal of the verification factor 18 are connected to each other, the output terminal of the verification factor 18 and the input terminal of the analysis description 19 are connected to each other, the output terminal of the analysis description 19 and the input terminal of the factor application 20 are connected to each other, a hidden representative factor can be found out from many variables by the factor analysis 5, the variables of the same nature are classified into one factor, the number of variables can be reduced, the assumption of the relationship between the variables can be checked, the anomaly 6 can be defined by the factor analysis 5 and the factor score of the element combination, and the weak anomaly information can be enhanced by the element combination.
Preferably, in step S1, a suitable fracture zone is selected, chemical data of the fracture zone is sampled, and structural geochemical data is obtained after analysis.
Preferably, in step S2, all sample data are taken, and the logarithm of base 10 is taken for the sample data, so as to obtain the analysis value.
Preferably, in step S, an analysis model is determined, a discriminant function Y ═ a1X + a2X +. anxn is established, where Y is a discriminant score (discriminant value), x1x2.. xn is a variable reflecting the characteristics of the object under study, a1a2.. an is a coefficient, X ═ a11F + a12F + + a1pFp + V, X ═ a21F + a22F + + a2pFp + V2 ═ AF + V, Xi ═ ai1F + ai2F aipFp + Vi, Xm ═ a 1F + ap2F + + ampFm + vm, Xi — ith normalized variable, a standard regression coefficient of the ith variable to the pth normalized factor, F-normalized factor, Vi-unique factor, F ═ 11X + W12 mxw 1mXm, F ++ W2X + + W2 × 21, W + + Wi + X + + Wi + W2, and a final weighting factor can be calculated.
Preferably, in step S3, the factors are analyzed and the scores of the factors are compared to define anomalies, which can enhance weak anomaly information, and finally combine the anomalies with geological conditions to define deep mineralization enrichment centers.
Preferably, in step S4, the factors are collected, and various data are compared to guide the generation of the abnormal graph, so as to form a final abnormal information graph, which facilitates the intuitive understanding of the abnormal situation of the area.
The working principle of the invention is as follows:
when in use, sampling and analyzing in a fracture and fragmentation zone to obtain structural geochemical data; the obtained data is subjected to logarithm taking 10 as a base, and then the element combination is determined through cluster analysis 4; obtaining factors and factor scores by performing factor analysis 5 on a data set with the base 10, and delineating an abnormal graph by using the coordinate value of each sampling point and the factor score value (namely X, Y and factor score); weak abnormal information can be enhanced by using the element combination factor score to define abnormality, and finally, deep mineralization enrichment centers can be defined by combining the abnormality with geological conditions; the method can deduce the appearance of the blind ore body according to the abnormal isoline change gradient, and deduces the flow direction of the ore fluid according to the relative position of the medium-low temperature element combination abnormal center and the medium-high temperature element combination abnormal center through clustering analysis and factor analysis to determine factors and verification factors, determine an analysis model, establish a discriminant function Y + a1X1+ a2X2+. anxn, wherein Y is a discriminant score (discriminant value), x1x2.. xn is a variable reflecting the characteristics of a study object, a1a2.. an is a coefficient, X1+ a11F1+ a12F2+ … + a1pFP + V1, X2 is a21F1+ a22F2+ 9 + a2pFP + V2X + AF + V X + AF + V, Xi + aip F56 + 2+ … + ai + Vi, Xapf 2+ aip, Xapf aip + aip F aip + aip, XamF + aip, Xapf + aip, XamW 7 + aip, a F + aip, and a F + aip is a F + aip, a F + 7 th common regression factor of a, wi1X1+ Wi2X2+ … + WimXm, Fp-Wp 1X1+ Wp2X2+ … + WpmXm, Wi-weight, factor score coefficient, estimated value of Fi-ith factor (factor score), analyzing the factors, using element combination factor score to define abnormal conditions to enhance weak abnormal information, deducing hidden ore body shape according to abnormal contour change gradient, guiding abnormal map formation, information extraction, summarizing the factors, comparing various data, guiding abnormal map formation, clustering analysis 4 aims to collect data on similar basis to classify, using factor analysis 5 to find hidden representative factors among many variables, classifying the same essential variables into one factor, reducing the number of variables, checking hypothesis of relationship among variables, on the other hand using factor analysis 5 and using element combination factor score to define abnormal 6, the less enhanced exception information can be achieved by element combination.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes in the embodiments and/or modifications of the invention can be made, and equivalents and modifications of some features of the invention can be made without departing from the spirit and scope of the invention.
Claims (10)
1. A method for extracting abnormal information of structural geochemical data comprises geochemical analysis data (1) with spatial coordinates, statistical analysis (7), cluster analysis (4) and factor analysis (5), and is characterized in that:
s1: sampling and analyzing in a fracture and fragmentation zone to obtain structural geochemical data;
s2: the obtained data is subjected to logarithm taking 10 as a base, and then the element combination is determined through clustering analysis;
s3: performing factor analysis on the data set with the base 10 to obtain factors and factor scores, and using the coordinate value of each sampling point and the factor score value (namely X, Y, factor score) to define an abnormal graph;
s4: weak abnormal information can be enhanced by using the element combination factor score to define abnormality, and finally, deep mineralization enrichment centers can be defined by combining the abnormality with geological conditions;
s5: the occurrence of the blind ore body can be deduced according to the change gradient of the abnormal contour line, and the flow direction of the ore fluid can be deduced according to the relative position of the abnormal center of the medium-low temperature element combination and the abnormal center of the medium-high temperature element combination.
2. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: the output of the geochemical analysis data (1) with the space coordinates and the input of the data preprocessing one (2) are connected with each other, the output of the data preprocessing one (2) and the input of the data classification (3) are connected with each other, the output of the data classification (3) and the input of the cluster analysis (4) are connected with each other, the output of the cluster analysis (4) and the input of the factor analysis (5) are connected with each other, the cluster analysis (4) and the cluster analysis (4) all take the data set with the bottom 10, and the output of the factor analysis (5) and the input of the score delineation anomaly (6) are connected with each other.
3. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: the output of statistical analysis (7) and the input interconnect of special value processing (8), the output of special value processing (8) and the input interconnect of visual display (9), the output of visual display (9) and the input interconnect of curve fitting (10), the output of curve fitting (10) and the input interconnect of figure show (11), the output of figure show (11) and the input interconnect of weak abnormal information extraction (12).
4. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: the output of the cluster analysis (4) and the input of the data preprocessing unit (13) are connected to one another, the output of the data preprocessing unit (13) and the input of the defined distance function (14) are connected to one another, the output of the defined distance function (14) and the input of the cluster grouping (15) are connected to one another, and the output of the cluster grouping (15) and the input of the evaluation output (16) are connected to one another.
5. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: the output of the factor analysis (5) and the input of the derived factor (17) are connected to each other, the output of the derived factor (17) and the input of the verification factor (18) are connected to each other, the output of the verification factor (18) and the input of the analysis description (19) are connected to each other, and the output of the analysis description (19) and the input of the factor application (20) are connected to each other.
6. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: in step S1, a suitable fracture zone is selected, chemical data of the fracture zone is sampled, and structural geochemical data is obtained after analysis.
7. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: in step S2, taking all sample data and taking the logarithm of base 10 of the sample data to obtain an analysis value.
8. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: in step S, an analysis model is determined, and a discriminant function Y ═ a1X + a2X +. anxn is established, where Y is a discriminant score (discriminant value), x1x2.. xn is a variable reflecting the characteristics of the object under study, a1a2.. an is a coefficient, X ═ a11F + a12F + + a1pFp + V, X ═ a21F + a22F + + a2pFp + V2 ═ AF + V, Xi ═ ai1F + ai2F + + aipFp + Vi, Xm ═ ap1F + ap2F + + ampFm + vm, Xi — ith normalized variable, — standard regression coefficient of ith variable to pth normalized factor, F-common factor, Vi-special factor, F ═ W11X + W12X 1mXm, F ++ W21X + W2X + + + Wi ++ 2, Wi ++ W2 × W + + Wi ++ W + + W2, xf — hfi — th normalized variable, W ++ W2, W ++ W2, W ++ W +.
9. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: in step S3, the factors are analyzed and the scores of the factors are compared to determine anomalies, which can enhance weak anomaly information, and finally define deep mineralization-enrichment centers in combination with geological conditions.
10. The method for extracting anomaly information of structural geochemical data as claimed in claim 1, wherein: in step S4, the factors are collected, and various data are compared to guide the generation of the abnormal graph.
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