CN111965721B - Transient electromagnetic data multi-attribute fusion technology - Google Patents
Transient electromagnetic data multi-attribute fusion technology Download PDFInfo
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Abstract
The invention discloses a transient electromagnetic data multi-attribute fusion technology, which comprises the following steps: s1, normalizing transient electromagnetic data attributes; s2, carrying out correlation analysis among attributes on the standardized data attributes in the S1, and judging whether the PCA dimension reduction processing is suitable; s3, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S2; and S4, extracting a main component according to the characteristic value and the characteristic vector obtained in the step S3. The invention avoids the difference of the interpretation methods of the results obtained by exploration caused by the difference of transient electromagnetic exploration devices, unifies the anomalies obtained by different devices, solves the problem of the spatial variation of the resistivity caused by the aqueous geologic body by adding 5 attributes besides the resistivity, enriches the related attributes of the electrical anomalies caused by the aqueous geologic body, and is more accurate in analyzing the electrical anomalies caused by the aqueous geologic body.
Description
Technical Field
The invention relates to the technical field of mining exploitation, in particular to a transient electromagnetic data multi-attribute fusion technology.
Background
In electrical prospecting, which is applied to detecting the water content of a stratum, predicting the water damage of a coal mine area and searching for a ground water source area, the water richness of a rock stratum is generally evaluated by using resistivity, polarization rate and derived parameters thereof. In coal mine transient electromagnetic water damage prediction, only the resistivity is generally used for evaluating the water enrichment of geological bodies such as faults, collapse columns, pore cracks, goafs and the like, so that the interpretation result is unilateral.
In the actual achievement interpretation, the following 2 factors should be considered:
1. different transient electromagnetic exploration devices are different, and the spatial variation characteristics of resistivity obtained by abnormal calculation caused by different water-containing geological bodies are inconsistent. Firstly, under the condition of the same transient electromagnetic device, the spatial variation characteristics of resistivity obtained by abnormal calculation caused by different water-containing geologic bodies are inconsistent; secondly, under the condition of different transient electromagnetic devices, the spatial variation characteristics of resistivity obtained by abnormal calculation caused by the same geologic body are inconsistent.
2. Electrical anomalies caused by aqueous geologic bodies are related not only to the magnitude of resistivity, but also to the spatially varying characteristics of resistivity.
It is clear that there are a number of problems with the prior art.
Disclosure of Invention
Therefore, in order to solve the above problems in the prior art, the present invention provides a transient electromagnetic data multi-attribute fusion technique.
The invention solves the problems by the following technical means:
a transient electromagnetic data multi-attribute fusion technique, comprising:
s1, normalizing transient electromagnetic data attributes;
s2, carrying out correlation analysis among attributes on the standardized data attributes in the S1, and judging whether the PCA dimension reduction processing is suitable;
s3, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S2;
and S4, extracting a main component according to the characteristic value and the characteristic vector obtained in the step S3.
Further, the transient electromagnetic data attribute in S1 includes:
resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient, and resistivity level gradient.
Further, the mathematical formula adopted for normalization in S1 is as follows:
wherein sigma is the standard deviation of attribute data,
is the average value of the attribute data.
Further, the S2 employs KMO test statistics.
Further, the KMO has a value between 0 and 1, and the judgment standard of whether the PCA dimension reduction processing is suitable is:
0.9 or more indicates perfect fit, 0.8 indicates fit, 0.7 indicates general, 0.6 indicates less fit, and 0.5 or less indicates extreme unfitness.
Further, the eigenvalue and eigenvector in S3 are obtained by a correlation matrix method or a singular value decomposition method.
Further, according to the feature value and the feature vector obtained in S3, a feature value greater than 1 or a cumulative ratio greater than 80% is selected as the main component.
Compared with the prior art, the invention has the beneficial effects that at least:
the invention further enriches the prediction exploration of transient electromagnetic water damage in a coal mine area by carrying out Principal Component Analysis (PCA) on 6 attributes such as resistivity R, resistivity X-direction (along measuring points) derivative RDx, resistivity Y-direction (along measuring lines) derivative RDy, resistivity Z-direction (depth) derivative RDz, resistivity horizontal total gradient RGxy, resistivity total gradient RGxyz and the like through data attribute standardization, attribute correlation analysis, calculation of attribute characteristic values and characteristic vectors and principal component extraction and analysis. The method has the advantages that the problem that the results obtained by exploration are different due to different transient electromagnetic exploration devices are avoided, the problem can be solved through the technology, the anomalies obtained by different devices are unified, the problem of spatial variation of the resistivity caused by the aqueous geologic body is solved by adding 5 other attributes except the resistivity, the related attributes of the electrical anomalies caused by the aqueous geologic body are enriched, and the analysis of the electrical anomalies caused by the aqueous geologic body is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a transient electromagnetic data multi-attribute fusion technique provided by the present invention;
FIG. 2 is a graph of a slice of the resistivity of a roof of an experimental coal seam according to an embodiment of the invention;
FIG. 3 is a graph of a derivative slice of the resistivity X direction of the roof of an experimental coal seam according to an embodiment of the invention;
FIG. 4 is a graph of Y-direction derivative of the resistivity of the roof of an experimental coal seam according to an embodiment of the invention;
FIG. 5 is a graph of a derivative slice of the resistivity Z direction of the roof of an experimental coal seam according to an embodiment of the invention;
FIG. 6 is a graph of horizontal gradient slices of the resistivity of the roof of an experimental coal seam according to an embodiment of the invention;
FIG. 7 is a graph of total gradient slices of experimental coal seam roof resistivity according to an embodiment of the invention;
fig. 8 is a graph of the experimental coalbed PCA composite outcome of an embodiment of the present invention.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, the following detailed description of the technical solution of the present invention refers to the accompanying drawings and specific embodiments. It should be noted that the described embodiments are only some embodiments of the present invention, and not all embodiments, and that all other embodiments obtained by persons skilled in the art without making creative efforts based on the embodiments in the present invention are within the protection scope of the present invention.
Examples
As shown in fig. 1, a transient electromagnetic data multi-attribute fusion technique includes:
s1, normalizing transient electromagnetic data attributes; preferably, the transient electromagnetic data attribute in S1 includes: resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total gradient of resistivity, and horizontal gradient of resistivity.
The statistical characteristics of the attribute data are greatly different, the attributes are standardized into the attributes with the same statistical characteristics before principal component analysis, and the general principal component analysis standardized method is expressed by the following mathematical expression:
where sigma is the standard deviation of the attribute data,
is the average value of the attribute data. This normalization method allows each attribute to have the same mean (0) and standard deviation (1), but still retains the data distribution characteristics.
S2, carrying out correlation analysis among attributes on the standardized data attributes in the S1, and judging whether the PCA dimension reduction processing is suitable; preferably, the S2 employs KMO (proposed by Kaiser, meyer and Olkin) test statistics. Preferably, the KMO has a value between 0 and 1, and the judgment criterion for determining whether PCA dimension reduction is suitable is: 0.9 or more indicates perfect fit, 0.8 indicates fit, 0.7 indicates general, 0.6 indicates less fit, and 0.5 or less indicates extreme unfitness.
After normalization of the S1 transient electromagnetic data attributes, it is first analyzed whether these attributes are suitable for PCA dimension reduction processing, where KMO test statistics are used. The KMO statistic has a value between 0 and 1. When the simple correlation coefficient square sum among all the attributes is far greater than the partial correlation coefficient square sum, the KMO value is close to 1, and the KMO value is close to 1, which means that the correlation among the attributes is stronger, and the original attributes are more suitable for PCA (Principal Component Analysis ) dimension reduction processing; when the sum of squares of simple correlation coefficients among all attributes is close to 0, the KMO value is close to 0, the more the KMO value is close to 0, which means that the weaker the correlation among the attributes is, the more the original attributes are not suitable for PCA. Different researchers evaluated whether attribute variables fit PCA with different KMO values, a common KMO metric given by Kaiser: 0.9 or more indicates perfect fit, 0.8 indicates fit, 0.7 indicates general, 0.6 indicates less fit, and 0.5 or less indicates extreme unfitness.
S3, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S2; preferably, the eigenvalues and eigenvectors in S3 are obtained by a correlation matrix method or a singular value decomposition method.
After the judgment of the S2 is carried out, after the attribute suitable for carrying out PCA dimension reduction processing is adopted, calculating a correlation matrix or a covariance matrix of the attribute variable, and solving the eigenvalue of the matrix and the corresponding unit eigenvector, wherein the covariance between the two attributes X, Y is expressed by the following formula:
for transient electromagnetic data with 6 attributes, a 6 multiplied by 6 covariance matrix S is formed, and a characteristic equation is solved
|s-λI|＝0
Where λ is a eigenvalue and I is a eigenvector. The 6 attributes have 6 eigenvalues and corresponding unit vectors. Preferably, the eigenvalues and eigenvectors of the covariance matrix S may also be solved by a singular value decomposition (Singular Value Decomposition, SVD).
And S4, extracting a main component according to the characteristic value and the characteristic vector obtained in the step S3. Preferably, the feature value and the feature vector obtained in S3 are selected as the main component, and the feature value is greater than 1 or the cumulative ratio is greater than 80%.
According to the invention, 6 characteristic values (namely 6 components, namely the resistivity, the resistivity X-direction derivative, the resistivity Y-direction derivative, the resistivity Z-direction derivative, the resistivity total gradient and the resistivity level gradient) are obtained, and the contribution of each component is evaluated by taking the duty ratio of the component, and the characteristic value is generally greater than 1 or the accumulated duty ratio is generally greater than 80% as the main component.
By combining the transient electromagnetic data multi-attribute fusion technology provided by the invention, through experiments on experimental coal seams, the attribute of resistivity R (experimental feedback is shown in fig. 2) is singly used as in the prior art, and meanwhile, 5 attributes of a derivative RDx (experimental feedback is shown in fig. 3) in the resistivity X direction (along a measuring point), a derivative RDy (experimental feedback is shown in fig. 4) in the resistivity Y direction (along a measuring line), a derivative RDz (experimental feedback is shown in fig. 5), a total gradient RGxy (experimental feedback is shown in fig. 6) of the resistivity level, a total gradient RGxyz (experimental feedback is shown in fig. 7) of the resistivity level and the like are extracted. And the relevant data shown in the following table are obtained through calculation and analysis:
as in table 1: the correlation coefficient matrixes of the 6 attributes of the experimental coal seam (nearby) are shown in the correlation coefficient matrixes of the different attributes of the transient electromagnetic data of the experimental coal seam, and the correlation between the resistivity R and the resistivity Y-direction derivative RDy and the correlation between the resistivity R and the resistivity depth-direction derivative RDz can be intuitively seen.
Table 1: correlation coefficient matrix of transient electromagnetic data of experimental coal seam with different attributes
| Correlation coefficient | R | RDx | RDy | RDz | | RGxyz |
| R | ||||||
| 1 | 0.021 | 0.631 | 0.524 | 0.432 | -0.204 | |
| RDx | 0.021 | 1 | 0.055 | -0.053 | 0.007 | 0.035 |
| RDy | 0.631 | 0.055 | 1 | 0.213 | 0.728 | 0.203 |
| RDz | 0.524 | -0.053 | 0.213 | 1 | 0.132 | -0.812 |
| RGxy | 0.432 | 0.007 | 0.728 | 0.132 | 1 | 0.4 |
| RGxyz | -0.204 | 0.035 | 0.203 | -0.812 | 0.4 | 1 |
Table 2: PCA component characteristic value table of experimental coal seam
| Composition of the components | Eigenvalues | Percentage variance (%) | Accumulation (%) |
| 1 | 2.39 | 39.829 | 39.829 |
| 2 | 1.951 | 32.523 | 72.351 |
| 3 | 0.998 | 16.641 | 88.993 |
| 4 | 0.399 | 6.648 | 95.641 |
| 5 | 0.226 | 3.77 | 99.411 |
| 6 | 0.035 | 0.589 | 100 |
As in table 2: the principal components are selected according to the principle that the characteristic value is larger than 1, and 2 principal components can be selected as shown in the PCA component characteristic value table of the experimental coal seam; according to the characteristic value variance accumulation duty ratio greater than 80%, 3 main components can be selected.
As shown in FIG. 8, the experimental coalbed PCA comprehensive result diagram is that 3 main components and original resistivity are obtained by experimental coalbed electrical property PCA, and the main component 2 is considered to be closer to the actual geological condition by combining the analysis of known geological data. And selecting a main component 2 result diagram to explain the water-rich property of the experimental coal seam.
Compared with the prior art, the invention has the beneficial effects that at least:
the invention further enriches the prediction exploration of transient electromagnetic water damage in a coal mine area by carrying out Principal Component Analysis (PCA) on 6 attributes such as resistivity R, resistivity X-direction (along measuring points) derivative RDx, resistivity Y-direction (along measuring lines) derivative RDy, resistivity Z-direction (depth) derivative RDz, resistivity horizontal total gradient RGxy, resistivity total gradient RGxyz and the like through data attribute standardization, attribute correlation analysis, calculation of attribute characteristic values and characteristic vectors and principal component extraction and analysis. The method has the advantages that the problem that the anomaly obtained by different devices is unified due to the fact that the interpretation methods of the results obtained by exploration are different due to the fact that transient electromagnetic exploration devices are different, the problem that the anomaly is high in value and the anomaly is low in value is solved, the problem of spatial variation of the resistivity caused by the aqueous geologic body is solved due to the fact that other 5 attributes except the resistivity are added, the relevant attributes of the electrical anomaly caused by the aqueous geologic body are enriched, and the analysis of the electrical anomaly caused by the aqueous geologic body is more accurate.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (6)
1. A transient electromagnetic data multi-attribute fusion technique, comprising:
s1, normalizing transient electromagnetic data attributes;
s2, carrying out correlation analysis among attributes on the standardized data attributes in the S1, and judging whether the PCA dimension reduction processing is suitable;
s3, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S2;
s4, extracting main components according to the characteristic values and the characteristic vectors obtained in the S3;
the transient electromagnetic data attribute in S1 includes:
resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient, and resistivity level gradient.
2. The transient electromagnetic data multi-attribute fusion technique according to claim 1, wherein the mathematical formula adopted for normalization in S1 is:
wherein sigma is the standard deviation of attribute data,
is the average value of the attribute data.
3. The transient electromagnetic data multi-attribute fusion technique of claim 1 wherein S2 employs KMO test statistics.
4. A transient electromagnetic data multi-attribute fusion technique according to claim 3, wherein the KMO has a value between 0 and 1, and the determination criteria for performing PCA dimension reduction processing are:
0.9 or more indicates perfect fit, 0.8 indicates fit, 0.7 indicates general, 0.6 indicates less fit, and 0.5 or less indicates extreme unfitness.
5. The transient electromagnetic data multi-attribute fusion technique according to claim 1, wherein the eigenvalue and eigenvector in S3 are obtained by a correlation matrix method or a singular value decomposition method.
6. The transient electromagnetic data multi-attribute fusion technique according to claim 1, wherein a eigenvalue greater than 1 or a cumulative ratio greater than 80% is selected as a principal component based on the eigenvalue and eigenvector calculated in S3.
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| CN110361792A (en) * | 2019-06-24 | 2019-10-22 | 山东大学 | A kind of fusion of geophysical data and imaging method, medium and equipment |
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| US4745550A (en) * | 1985-08-16 | 1988-05-17 | Schlumberger Technology Corporation | Processing of oriented patterns |
| CN109343134A (en) * | 2018-11-27 | 2019-02-15 | 中煤科工集团西安研究院有限公司 | A kind of Transient Electromagnetic Method in Mine data analysis interpretation method and system |
| CN110361792A (en) * | 2019-06-24 | 2019-10-22 | 山东大学 | A kind of fusion of geophysical data and imaging method, medium and equipment |
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