CN112068221B - Method for analyzing water-rich property of coal seam - Google Patents
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Abstract
A method of analyzing water-rich properties of a coal seam, comprising: s1, acquiring data of a coal seam to be tested; s2, extracting relevant attribute data of the data acquired in the S1; s3, normalizing the related attribute data of the data acquired in the S2; s4, carrying out correlation analysis among attributes on the attribute data standardized in the S3, and judging whether the PCA dimension reduction processing is suitable; s5, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S4; s6, extracting main components according to the characteristic values and the characteristic vectors obtained in the S5; and S7, analyzing the principal component by using a weighted Euclidean distance according to the principal component extracted in the S6. The Euclidean distance or the weighted Euclidean distance is utilized to make various anomalies more prominent in data processing, and the difference of stratum water enrichment can be reflected more.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a coal seam water-rich analysis method.
Background
The water contained in the sandstone fracture type aquifer is a water hazard commonly existing in most coal fields in China, and most of the water is still water, and the sandstone fracture type aquifer is generally characterized by quicker arrival and departure, relatively smaller hazard, insufficient importance and less development of related research work. However, in recent years, more and more examples of such aquifers with large water inflow and long duration are provided, and if a drainage measure is adopted in advance for sandstone water on the top and bottom plates of the coal seam, water damage caused by such aquifers is significantly improved.
The goaf ponding mainly refers to old kiln ponding with unknown mining range for a long time, small disordered kiln ponding lacking accurate drawing data around a mine or waste roadway old kiln water self-driven by the mine. The water is stored in goafs or coal rock or rock roadways connected with the goafs, the geometric shape of the water body is extremely irregular, the spatial relationship between the production mine mining engineering and the water body is complicated, and the analysis and the judgment are difficult. The water body is concentrated, the pressure transmission is rapid, and the flow of the water body is communicated with the surface water flow, which is different from the leakage of the underground water in the aquifer. Once the mining engineering approaches, the mining engineering can be crashed out, and water-permeable accidents occur. Even if only a few cubic meters of water is accumulated, personal casualties often occur and the well engineering through which the collapsed water flows is destroyed once the water is accidentally approached or collapsed, so that huge economic losses are caused.
The characteristic of the common geophysical prospecting abnormality, the numerical value is assumed to be abnormal at the average measuring point, for example, the resistivity is high or the resistivity is low, and the resistivity rapid change zone is abnormal, which usually needs to be manually judged, and the interpretation difficulty is increased.
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 method for analyzing water-rich properties of coal seam.
The invention solves the problems by the following technical means:
a method of analyzing water-rich properties of a coal seam, comprising:
s1, acquiring data of a coal seam to be tested;
s2, extracting relevant attribute data of the data acquired in the S1;
s3, normalizing the related attribute data of the data acquired in the S2;
s4, carrying out correlation analysis among attributes on the attribute data standardized in the S3, and judging whether the PCA dimension reduction processing is suitable;
s5, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S4;
s6, extracting main components according to the characteristic values and the characteristic vectors obtained in the S5;
and S7, analyzing the principal component by using a weighted Euclidean distance according to the principal component extracted in the S6.
Further, the data acquired in the step S1 are transient electromagnetic data of the coal seam to be detected.
Further, the data acquired in the step S1 are three-dimensional seismic data and transient electromagnetic data of the coal seam to be detected.
Further, the relevant attribute data of the data extracted in S2 is:
transient electromagnetic resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient, and resistivity level gradient.
Further, the step S2 includes:
s21, converting transient electromagnetic data according to a three-dimensional seismic data coordinate format;
s22, uniformly extracting three-dimensional seismic attribute data and transient electromagnetic attribute data converted in the S31.
Further, the relevant attribute data of the data extracted in S2 is:
the three-dimensional seismic attribute data includes:
coherent, variance, curvature, ant, amplitude, frequency, density, impedance, porosity, and seismic resistivity;
transient electromagnetic attribute data includes:
transient electromagnetic 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 S3 is as follows:
wherein sigma is the standard deviation of attribute data,is the average value of the attribute data.
Further, the S4 employs KMO test statistics.
Further, the eigenvalue and eigenvector in S5 are obtained by a correlation matrix method or a singular value decomposition method.
Further, the step S7 further includes analyzing the principal component by using euclidean distance.
Compared with the prior art, the invention has the beneficial effects that at least:
according to the invention, through attribute data standardization, attribute correlation analysis, attribute characteristic value and characteristic vector calculation, principal component extraction and analysis, prediction exploration of water damage in a coal mine area is further enriched, and 16 attribute data in transient electromagnetic data and three-dimensional seismic data are extracted to perform Principal Component Analysis (PCA) so as to evaluate the water-rich property of the geologic body. The method solves the problem that the anomaly obtained by different devices is unified by the technology, 5 attributes except the resistivity are added to solve the problem of spatial variation of the resistivity caused by the aqueous geologic body, the related attributes causing the electrical anomaly caused by the aqueous geologic body are enriched, the analysis of the electrical anomaly caused by the aqueous geologic body is more accurate, and various anomalies can be highlighted in the data processing by utilizing Euclidean distance or weighted Euclidean distance, so that the difference of the water-rich property of the stratum can be reflected.
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 method for analyzing water-rich properties of a coal seam;
FIG. 2 is a graph of the integrated results of the PCA of the top Otto interface in example 1 provided by the present invention;
FIG. 3 is a graph showing the comparison of the Euclidean distance before and after rotation of the principal component in example 1 provided by the present invention;
fig. 4 is a comprehensive result chart of the top-boundary vibration electric property PCA of the olan in example 2 provided by 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
A method of analyzing water-rich properties of a coal seam, comprising:
s1, acquiring data of a coal seam to be tested; preferably, the data acquired in S1 is transient electromagnetic data of the coal seam to be measured. It should be noted that the three-dimensional seismic data and transient electromagnetic data can be used together as analysis data of the coal seam to be detected, that is, the data obtained in the step S1 are the three-dimensional seismic data and the transient electromagnetic data of the coal seam to be detected.
S2, extracting the relevant attribute data of the data acquired in the S1.
When the data adopted in S1 is transient electromagnetic data, preferably, the relevant attribute data of the data extracted in S2 is: 6 attribute data including transient electromagnetic resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient and resistivity horizontal gradient.
When the three-dimensional seismic data and the transient electrical data are adopted as analysis data of the coal seam to be tested in the step S1, preferably, the step S2 includes:
s21, converting transient electromagnetic data according to a three-dimensional seismic data coordinate format; preferably, in S1, the transient electromagnetic data is converted into SEG-Y format based on a seismic SEG-Y data format.
S22, uniformly extracting three-dimensional seismic attribute data and transient electromagnetic attribute data converted in the S31. It should be noted that, in order to realize fusion of different data attributes, firstly, data is unified into a unified coordinate system, and the transient electromagnetic data is preferably converted according to a three-dimensional seismic data coordinate format. The three-dimensional seismic data has the data format unified in industry, transient electromagnetic data formats are more, on the basis of analyzing the SEG-Y data format of seismic exploration, the transient electromagnetic data are preferably converted into the SEG-Y format, the three-dimensional seismic data and the transient electromagnetic data are managed on a unified platform, and a certain range of data attributes are extracted.
Preferably, the relevant attribute data of the data extracted in S2 may be:
the three-dimensional seismic attribute data includes: coherent, variance, curvature, ant, amplitude, frequency, density, impedance, porosity, and seismic resistivity; transient electromagnetic attribute data includes: transient electromagnetic resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient, and resistivity level gradient; i.e. a total of 16 attribute data.
S3, normalizing the related attribute data of the data acquired in the S2; preferably, the mathematical formula used for normalization in S3 is:
wherein sigma is the standard deviation of 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.
S4, carrying out correlation analysis among attributes on the attribute data standardized in the S3, and judging whether the PCA dimension reduction processing is suitable; preferably, the S4 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 S3 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.
S5, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S4; preferably, the eigenvalues and eigenvectors in S5 are obtained by a correlation matrix method or a singular value decomposition method.
After the judgment of the S4 is performed, after the attribute suitable for the PCA dimension reduction processing is performed, calculating a correlation matrix or a covariance matrix of the attribute variable, and solving a characteristic value of the matrix and a corresponding unit characteristic vector, wherein the covariance between the two attributes X, Y is expressed by the following formula:
it should be noted that, when the data adopted in S1 is transient electromagnetic data, for the transient electromagnetic data with 6 attributes, a 6×6 covariance matrix S is formed, and the eigenvalue λ in the equation |s- λi|=0 is solved, where 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).
When the three-dimensional seismic data and the transient electromagnetic data are adopted as analysis data of the coal seam to be detected together in the S1, a covariance matrix S of 16 multiplied by 16 is formed for the transient electromagnetic data of 16 attributes, and a characteristic value lambda in a formula of a characteristic equation |s-lambda I|=0 is solved, wherein I is a characteristic unit vector. The 16 attributes have 16 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).
S6, extracting main components according to the characteristic values and the characteristic vectors obtained in the S5; preferably, the feature value and the feature vector obtained in S5 are selected as the main component, and the feature value is greater than 1 or the cumulative ratio is greater than 80%.
S7, utilizing a weighted Euclidean distance (the mathematical formula is; the principal component is analyzed. Preferably, the step S7 further comprises using Euclidean distance (formula:. About.: about>The principal component is analyzed.
In order to make the technical effects of the present invention easier to understand, two specific examples are introduced for explanation:
example 1
When the data employed in S1 is transient electromagnetic data:
table 1: otton top boundary PCA component characteristic value table
| Composition of the components | Eigenvalues | Percentage variance (%) | Accumulation (%) |
| 1 | 3.053 | 50.888 | 50.888 |
| 2 | 1.061 | 17.681 | 68.57 |
| 3 | 0.999 | 16.656 | 85.226 |
| 4 | 0.606 | 10.106 | 95.331 |
| 5 | 0.254 | 4.228 | 99.559 |
| 6 | 0.026 | 0.441 | 100 |
Table 1 shows the characteristic values of 6 components obtained by the Austrian top-boundary electrical method attribute PCA, wherein the principal components are selected according to the principle that the characteristic values are larger than 1, 2 principal components can be selected, and the variance accumulation ratio is 68.57%; the principal components are selected according to the principle that the cumulative variance ratio is larger than 80%, 3 principal components can be selected, and the cumulative ratio is 85.266%. As shown in fig. 1, the three-dimensional seismic exploration result and the downhole exposure analysis are combined, the matching of the several plane diagrams is not good, and the weighted euclidean distance is relatively closer.
As shown in fig. 2, on the basis of PCA in a general sense, the rotation of the principal component is searched, and the obtained euclidean distance is compared with the weighted euclidean distance, and it is found that the abnormal euclidean distance (high) is located on the western side of the F2 fault after the rotation of the principal component, and is consistent with the geological condition of the investigation region.
Example 2
When the three-dimensional seismic data and transient electrical data are adopted as analysis data of the coal seam to be tested in the S1:
table 2: PCA component characteristic value table for Otton top seismoelectric property
Table 2 is a characteristic value table obtained by PCA of 10 attributes such as amplitude, coherence, variance, frequency, curvature, ant body, wave impedance, density, porosity, resistivity, etc. of the top boundary of the ohmmeter and 6 attributes such as resistivity, X-direction derivative of resistivity, Y-direction derivative of resistivity, Z-direction derivative of resistivity, horizontal gradient of resistivity, total gradient of resistivity, etc. of the transient electromagnetic, wherein the main component is selected according to the characteristic value of more than 1, 5 main components can be selected, and the variance accumulation ratio is 72.588%; and selecting main components according to the variance accumulation ratio of more than 80%, wherein 7 main components can be selected, and the minimum characteristic value is 0.9.
Table 3 is a matrix of seismoelectric property coefficients for 6 principal components whose most dominant of the 6 principal components by composition property are seismic inversion porosity, transient electromagnetic resistivity horizontal gradient, seismic variance, seismic inversion resistivity, resistivity X-direction derivative, and resistivity Z-direction derivative, respectively. In the distribution of transient electromagnetic (electrical method) and seismic attribute in the coefficient matrix, the 1 st and 2 nd main components are mainly based on the seismic and electrical method attribute respectively, and the main and secondary seismic and electrical method attributes in the other main components are not obvious.
Table 3: principal component coefficient matrix of Oscillating-gray top seismoelectric attribute PCA
As shown in fig. 3, the euclidean distance plan is obtained by the top boundary seismoelectric property PCA of the aogray, the principal component composite score and the principal component weighting. Because the Otto limestone geological data collected in the research area is less, the overall fitness of 6 main components is not high by combining the three-dimensional seismic interpretation results and the comprehensive analysis of the coal seam geological data to be tested in the embodiment 2, and the weighted Euclidean distance is closer to the hydrogeological data.
From the two embodiments, the weighted Euclidean distance is closer to the actual hydrogeologic data, and the interpretation is simple and can be used for detecting and interpreting the water-rich area. The karst in the research area generally develops, the water-rich property is strong, the karst water approximately flows from north east to south west, the water-containing abnormality of the karst limestone is interpreted by using the weighted Euclidean distance to conform to the rule, firstly, the abnormal band is in north west direction of faults, and secondly, the abnormal band is in north east radial direction. In comparison, the weighted euclidean distance after the principal component rotation is more suitable for explaining the water-rich anomaly.
Compared with the prior art, the invention has the beneficial effects that at least:
according to the invention, through attribute data standardization, attribute correlation analysis, attribute characteristic value and characteristic vector calculation, principal component extraction and analysis, prediction exploration of water damage in a coal mine area is further enriched, and 16 attribute data in transient electromagnetic data and three-dimensional seismic data are extracted to perform Principal Component Analysis (PCA) so as to evaluate the water-rich property of the geologic body. The method has the advantages that the problem that the results obtained by exploration are different due to different transient electromagnetic exploration devices is avoided, the problem can be solved through the technology, the anomalies obtained by different devices are unified, the problem of spatial variation of resistivity caused by the aqueous geologic body is solved by adding 5 other attributes besides resistivity, the related attributes causing electrical anomalies caused by the aqueous geologic body are enriched, the analysis of the electrical anomalies caused by the aqueous geologic body is more accurate, and the damage to goaf ponding can be judged more accurately. The Euclidean distance or the weighted Euclidean distance is utilized to make various anomalies more prominent in data processing, and the difference of stratum water enrichment can be reflected more.
Claims (5)
1. A method for analyzing the water-rich nature of a coal seam, comprising the steps of:
s1, acquiring data of a coal bed to be detected, wherein the acquired data are three-dimensional seismic data and transient electromagnetic data of the coal bed to be detected;
s2, extracting relevant attribute data of the data acquired in the S1, wherein the relevant attribute data comprises;
transient electromagnetic resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient, and resistivity horizontal gradient of the transient electromagnetic data;
coherent volume, variance volume, curvature volume, ant volume, amplitude, frequency, density, impedance, porosity and seismic resistivity of the three-dimensional seismic attribute data; transient electromagnetic attribute data includes: transient electromagnetic resistivity, resistivity X-direction derivative, resistivity Y-direction derivative, resistivity Z-direction derivative, total resistivity gradient, and resistivity level gradient;
wherein the step of extracting the correlated attribute data of the three-dimensional seismic data and the transient electromagnetic data comprises:
s21, converting transient electromagnetic data into an SEG-Y format based on the seismic exploration SEG-Y data format;
s22, uniformly extracting three-dimensional seismic attribute data and format-converted transient electromagnetic attribute data;
s3, normalizing the related attribute data of the data acquired in the S2;
s4, carrying out correlation analysis among attributes on the attribute data standardized in the S3, and judging whether the PCA dimension reduction processing is suitable;
s5, obtaining the characteristic value and the characteristic vector of the attribute suitable for PCA dimension reduction processing in the S4;
s6, extracting main components according to the characteristic values and the characteristic vectors obtained in the S5;
and S7, analyzing the principal component by using a weighted Euclidean distance according to the principal component extracted in the S6.
2. The method for analyzing the water-rich property of the coal seam according to claim 1, wherein the mathematical formula adopted for the standardization in the step S3 is as follows:
wherein sigma is the standard deviation of the attribute data and is the average value of the attribute data.
3. The method of claim 1, wherein S4 employs KMO test statistics.
4. The method for analyzing the water-rich property of the coal seam according to claim 1, wherein the characteristic value and the characteristic vector in the step S5 are obtained by adopting a correlation matrix method or a singular value decomposition method.
5. The method of claim 1, wherein S7 further comprises analyzing the principal component using euclidean distance.
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