CN112253236B - Method for cleaning data of mine electrical method monitoring data by utilizing correlation analysis - Google Patents
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Abstract
The invention discloses a method for cleaning data of monitoring data of a mine electrical method by utilizing correlation analysis, which aims at the technical problem that the influence of a plurality of continuous abnormal data points in the monitoring data of the mine electrical method is difficult to eliminate by a conventional data cleaning method.
Description
Technical Field
The invention relates to a mine geophysical prospecting data cleaning method, belongs to the field of mine geophysical prospecting, and particularly relates to a method for cleaning mine electrical monitoring data by utilizing correlation analysis.
Background
The method is widely used for monitoring the damage of the top and bottom plates of the coal seam and monitoring water damage in the stoping process of the coal mine working face, and obtains good geological effect. Data acquired by the electrical prospecting of a mine are generally electric potentials or electric potential differences, and the influence of interference factors such as electromagnetic noise, roadway accumulated water, float coal, metal bodies and the like needs to be eliminated for interpreting useful geological information. In addition, in the process of monitoring the mine electricity method, the grounding condition of the monitoring electrode may change along with the time, the monitoring electrode itself may not work normally due to mining damage, all of which need to be considered in the process of data cleaning, otherwise, false abnormality may occur in the interpretation result.
In general, in the mine electrical monitoring, a plurality of monitoring electrodes are arranged around a target area in a coal mine, an artificial electric field is established by using a transmitting electrode to supply power to the underground, and a voltage signal is acquired by using a receiving electrode. When one transmitting electrode is powered, a plurality of receiving electrodes are generally used for signal acquisition. When the mine electrical method monitoring is carried out, a group of monitoring data can be collected at fixed intervals, the mine electrical method monitoring data are continuously generated along with the time, and the data volume is very large. The conventional data cleaning method generally adopts measures such as outlier correction, smooth filtering and the like, and the method can eliminate or correct isolated abnormal data points caused by electromagnetic noise, roadway accumulated water, float coal, metal bodies and the like. However, when a certain transmitting electrode is seriously interfered in the mining process, the signal acquired by the receiving electrode contains more interference information, and a plurality of continuous receiving data corresponding to the transmitting electrode belong to abnormal data. In this case, a plurality of consecutive abnormal data points are formed in the monitoring data, and it is generally difficult to eliminate the influence of such interference information by using the conventional data cleaning method. If a manual identification method is adopted to process the abnormal data, a great deal of manpower and time are consumed, and the processing result is not time-efficient.
Disclosure of Invention
The invention mainly solves the technical problem that the influence of a plurality of continuous abnormal data points in the mine electrical method monitoring data is difficult to eliminate in the prior art, and provides a method for cleaning the mine electrical method monitoring data by utilizing correlation analysis. The method utilizes the characteristic that the mine electrical method monitoring data has time series correlation, carries out correlation analysis on the monitoring data at different times, eliminates the influence of a plurality of continuous abnormal data points by identifying and correcting the data with low correlation degree or irrelevant data, and realizes the data cleaning of the mine electrical method monitoring data.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for data cleaning of electromechanically monitored mine data using correlation analysis, comprising:
grouping data corresponding to the same transmitting electrode and different receiving electrodes in each monitoring period;
carrying out normalization processing on the grouped monitoring data;
calculating a correlation coefficient matrix of monitoring data in two adjacent time periods based on the data after normalization processing;
and cleaning the monitoring data based on the correlation coefficient matrix.
Preferably, the method for performing data cleaning on the monitoring data of the mining electrical method by using correlation analysis, wherein the grouping of the data corresponding to the same transmitting electrode and different receiving electrodes in each monitoring period includes:
recording monitoring data of an ith monitoring period as T (i), wherein i is 0,1, … … and n, wherein T (0) is initial monitoring data;
grouping data corresponding to the same transmitting electrode and different receiving electrodes into a group, and marking the group as D (i, j), wherein j is the serial number of the transmitting electrode;
recording data collected by the same transmitting electrode and the same receiving electrode in the monitoring data T (i) as d (i, j, k), wherein k is the number of the receiving electrode;
wherein, T (i), D (i, j) and D (i, j, k) are as follows:
in the formula, ms is the number of transmitting electrodes, and mr is the number of receiving electrodes.
Preferably, in the method for performing data cleaning on the monitoring data by the mining electrical method by using the correlation analysis, the initial monitoring data T (0) is acquired under the condition that all monitoring electrodes of the monitoring system work normally.
Preferably, in the method for cleaning the data of the monitoring data of the mining electrical method by using the correlation analysis, outlier correction and smooth filtering are performed on the grouped data corresponding to the same transmitting electrode and different receiving electrodes, so that isolated abnormal point data is eliminated.
Preferably, in the method for performing data cleaning on the monitoring data by the mining electrical method by using correlation analysis, the correlation coefficient matrix of the monitoring data in two adjacent time periods is calculated based on the following formula:
wherein s (i-1, i, j) is the covariance of D (i-1, j) and D (i, j), and c (i-1, j) and c (i, j) are the standard deviation of D (i-1, j) and D (i, j), respectively; r (i-1, i) represents a correlation coefficient matrix of the monitoring data T (i-1) and the monitoring data T (i).
Preferably, in the method for performing data cleaning on the monitoring data of the mining electrical method by using the correlation analysis, the covariance s (i-1, i, j) of D (i-1, j) and D (i, j) is calculated based on the following formula:
preferably, in the method for performing data cleaning on the monitoring data of the mining electrical method by using correlation analysis, the standard deviation c (i-1, j) and c (i, j) are calculated based on the following formula:
preferably, the method for performing data cleaning on the monitoring data of the mining electrical method by using correlation analysis is performed when R (i-1, i) (i)>0) In the presence of a correlation coefficient r (i-1, i, j) 0 ) 0, wherein 1 ≦ j 0 Ms or less, adding j in T (i) 0 Reception data D (i, j) corresponding to each transmission electrode 0 ) The abnormal data that are regarded as irrelevant are marked,it is corrected based on the following formula:
preferably, the method for performing data cleaning on the monitoring data of the mining electrical method by using correlation analysis is performed when R (i-1, i) (i)>0) In the presence of a correlation coefficient of 0<|r(i-1，i，j 1 )|<ε(1≤j 1 Ms) or less, adding j in T (i) 1 Received data D (i, j) corresponding to each transmitting electrode 1 ) Abnormal data labeled as low in correlation, which is corrected based on the following formula:
D(i，j 1 )＝r(i-1，i，j 1 )D(i，j 1 )+(1-r(i-1，i，j 1 ))D(i-1，j 1 ) (i≥1)。
preferably, in the method for performing data cleaning on the monitoring data of the mining electrical method by using the correlation analysis, if all elements of R (i-1, i) satisfy | R (i-1, i, j) | ≧ epsilon (j ═ 1, … …, ms), which indicates that the monitoring data T (i) and T (i-1) have correlation, the monitoring data of the next period are cleaned.
The method has the advantages that the monitoring data in the current time period and the monitoring data in the previous time period are subjected to correlation analysis, abnormal data with low or irrelevant correlation degree are identified according to the given correlation coefficient threshold, the monitoring data can be analyzed and processed in real time in the monitoring process, and the timeliness is high; the abnormal data is corrected by utilizing the monitoring data in the previous period or the previous period instead of being directly deleted, so that the data information which is useful for the later geological interpretation can be kept as much as possible while the influence of a plurality of continuous abnormal data points is eliminated.
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FIG. 1 is a schematic diagram of an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
In the process of monitoring by the mining electrical method, the grounding condition of the monitoring electrode may change along with the time, and the monitoring electrode cannot normally work due to mining damage. When a certain transmitting electrode is seriously interfered in the mining process, a plurality of continuous abnormal data points can be formed in the monitoring data, and the influence of interference information is generally difficult to eliminate by adopting a conventional data cleaning method. If a manual identification method is adopted to process the abnormal data, a large amount of labor and time are consumed, and the processing result is not time-efficient.
The invention provides a method for cleaning monitoring data of a mine electrical method by utilizing correlation analysis, aiming at the technical problem that the influence of a plurality of continuous abnormal data points in the monitoring data of the mine electrical method is difficult to eliminate in the prior art. The method utilizes the characteristic that the mine electrical method monitoring data has time series correlation, carries out correlation analysis on the monitoring data at different times, eliminates the influence of a plurality of continuous abnormal data points by identifying and correcting the data with low correlation degree or irrelevant data, and realizes the data cleaning of the mine electrical method monitoring data. The specific implementation mode is as follows:
each monitoring time interval has ms transmitting electrodes and mr receiving electrodes to carry out data acquisition, when one transmitting electrode carries out signal transmission, mr receiving electrodes carry out signal acquisition in sequence, and when the ms transmitting electrodes finish one-time signal transmission and corresponding data acquisition, a group of monitoring data acquisition is finished. For the sake of distinction, the monitored data of the ith monitoring period is denoted as T (i) (0,1, … …, n), where T (0) is the initial monitored data and T (n) is the last set of monitored data.
The embodiment comprises the following steps:
(1) recording monitoring data of the ith monitoring period as T (i) (i ═ 0,1, … …, n), wherein T (0) is initial monitoring data, and T (n) is a last group of monitoring data; grouping monitoring data T (i) according to different transmitting electrodes, grouping data corresponding to the same transmitting electrode and different receiving electrodes into a group, and recording the group as D (i, j) (j is 1, … … and ms, and ms is the number of the transmitting electrodes); recording data collected by the same transmitting electrode and the same receiving electrode in the monitoring data t (i) as d (i, j, k) (k is 1, … …, mr is the number of receiving electrodes); t (i), D (i, j) and D (i, j, k) are as follows:
(2) the initial monitoring data T (0) needs to be collected under the condition that all monitoring electrodes of the monitoring system are working normally.
(3) And (3) performing outlier correction and smooth filtering on the monitoring data T (i) (i is 0,1, … … and n) and the elements D (i, j) (j is 1, … … and ms) respectively to eliminate isolated abnormal data points.
(4) The average values of the elements D (i, j) (j is 1, … …, ms) of the monitoring data t (i) (i is 0,1, … …, n) are calculated, and D (i, j) is normalized by the average values, as shown in the following formula:
(5) performing correlation analysis on the monitoring data T (i-1) and T (i) (i >0), and calculating correlation coefficients of two groups of data D (i-1, j) and D (i, j) (j is 1, … … and ms) respectively, wherein the formula is as follows:
where s (i-1, i, j) is the covariance of D (i-1, j) and D (i, j), and c (i-1, j) and c (i, j) are the standard deviations of D (i-1, j) and D (i, j), respectively, in the following specific forms:
(6) the threshold value epsilon of the correlation coefficient is given, the value range of the threshold value epsilon is more than or equal to 0 and less than or equal to 0.5, and the specific value can be selected according to the actual situation.
(7) If R (i-1, i) (i)>0) In the presence of a correlation coefficient r (i-1, i, j) 0 )＝0(1≤j 0 Ms) indicates the j-th element in T (i) 0 Received data D (i, j) corresponding to each transmitting electrode 0 ) With the data D (i-1, j) of the previous period 0 ) If the data is completely irrelevant, the data can be regarded as irrelevant abnormal data to be marked, and the abnormal data is corrected by utilizing the early monitoring data, and the formula is as follows:
(8) if R (i-1, i) (i)>0) In the presence of a correlation coefficient of 0<|r(i-1,i,j 1 )|<ε(1≤j 1 Ms) indicates the j-th element in T (i) 1 Reception data D (i, j) corresponding to each transmission electrode 1 ) With the data D (i-1, j) of the previous period 1 ) The partial data can be regarded as abnormal data with low correlation degree to be marked, and the abnormal data with low correlation degree can be corrected by utilizing the monitoring data of the previous time interval, and the formula is as follows:
D(i,j 1 )＝r(i-1,i,j 1 )D(i,j 1 )+(1-r(i-1,i,j 1 ))D(i-1,j 1 )(i≥1)
(9) if all elements of R (i-1, i) (i >0) satisfy | R (i-1, i, j) | ≧ epsilon (j ═ 1, … …, ms), it indicates that there is a correlation between the monitored data T (i) and T (i-1), and the cleaning of the monitored data in the next period can be continued without abnormal data marking.
As shown in fig. 1, after the monitoring starts, let i be 0, and the initial monitoring data T (0) needs to be collected when all monitoring electrodes of the monitoring system are working normally. In order to ensure higher data quality, multiple times of acquisition can be carried out under the condition that the monitoring target area is not influenced by mining, and the average value of the multiple acquisition results is taken as initial monitoring data T (0).
As shown in fig. 1, initial monitor data T (0) is read, and the monitor data T (0) is grouped according to the difference of the transmitting electrodes, and data corresponding to the same transmitting electrode and the different receiving electrodes are grouped into one group, which is denoted as D (0, j) (j is 1, … …, ms). Data collected by the same transmitting electrode and the same receiving electrode in the monitoring data T (0) are denoted as d (0, j, k) (k is 1, … …, mr). T (0), D (0, j) and D (0, j, k) are as follows:
as shown in fig. 1, outlier correction and smooth filtering are performed on D (0, j) (j is 1, … …, ms), and this step can eliminate or correct isolated abnormal data points caused by electromagnetic noise, roadway water, float coal, metal bodies, etc., so as to avoid affecting the subsequent correlation analysis. Next, an average value of D (0, j) (j is 1, … …, ms) is calculated and normalized by the average value, and the formula is as follows:
the influence of the amplitude change of the transmitting current and the receiving voltage on the subsequent correlation analysis can be eliminated through the normalization processing.
As shown in fig. 1, after the initial monitoring data T (0) is preprocessed, i is set to 1, and the monitoring data T (1) in the next period is read. D (1, j) (j is 1, … …, ms) is obtained by grouping T (1) into data packets, and has the following form:
outlier correction and smooth filtering were performed on D (1, j) (j ═ 1, … …, ms), respectively, to eliminate isolated outlier data points. Further, D (1, j) (j is 1, … …, ms) is normalized by the average value, and the formula is as follows:
as shown in fig. 1, correlation analysis is performed on the monitoring data T (0) and T (1), and correlation coefficients of two sets of data D (0, j) and D (1, j) (j ═ 1, … …, ms) are calculated, respectively, according to the following formula:
where s (0,1, j) is the covariance of D (0, j) and D (1, j), and c (0, j) and c (1, j) are the standard deviations of D (0, j) and D (1, j), respectively, and are as follows:
the threshold value epsilon of the correlation coefficient is given, the value range of the threshold value epsilon is more than or equal to 0 and less than or equal to 0.5, and the specific value can be selected according to the actual situation. As shown in FIG. 1, if there is a correlation coefficient r (0,1, j) 0 )＝0(1≤j 0 Ms is less than or equal to) is determined, the j-th time in T (1) is indicated 0 Reception data D (1, j) corresponding to each transmission electrode 0 ) And D (0, j) in the initial monitoring data 0 ) If the data is totally irrelevant, the data can be regarded as irrelevant abnormal data to be marked, and the data is corrected by using the initial monitoring data, and the formula is as follows:
D(1,j 0 )＝D(0,j 0 )
if there is a correlation coefficient of 0<|r(0,1,j 1 )|<ε(1≤j 1 Ms ≦ m), it indicates the jth of T (1) 1 Reception data D (1, j) corresponding to each transmission electrode 1 ) And data D (0, j) in the initial monitoring data 1 ) If the correlation is low, the partial data can be regarded as abnormal data with low correlation to be marked, and the abnormal data is corrected by using the initial monitoring data, and the formula is as follows:
D(1，j 1 )＝r(0,1,j 1 )D(1,j 1 )+(1-r(0,1,j 1 ))D(0,j 1 )
if all elements of R (0, 1) satisfy | R (0,1, j) | ≧ epsilon (j ═ 1, … …, ms), it indicates that there is a correlation between the monitored data T (1) and T (0), and no abnormal data marking is needed, so that i ═ 2 can be made, and data cleaning continues to be performed on the monitored data T (2) in the next period.
After all the abnormal data in the monitoring data T (1) are corrected, i may be set to 2, and the data cleaning may be continued on the monitoring data T (2) in the next period.
As shown in fig. 1, when data cleaning is performed on monitoring data t (i) (i >1) in any subsequent time period i, the monitoring data t (i) are grouped according to different transmitting electrodes, and data corresponding to the same transmitting electrode and different receiving electrodes are grouped into one group, which is denoted as D (i, j) (j is 1, … …, ms); recording data collected by the same transmitting electrode and the same receiving electrode in the monitoring data T (i) as d (i, j, k) (k is 1, … … and mr); t (i), D (i, j) and D (i, j, k) are as follows:
outlier correction and smooth filtering were performed on D (i, j) (j ═ 1, … …, ms), respectively, to eliminate isolated outlier data points. The average values of D (i, j) (j is 1, … …, ms) are calculated, and D (i, j) is normalized by the average values, and the formula is as follows:
performing correlation analysis on the monitoring data T (i-1) and T (i), and calculating correlation coefficients of two groups of data D (i-1, j) and D (i, j) (j is 1, … …, ms) respectively, wherein the formula is as follows:
wherein s (i-1, i, j) is the covariance of D (i-1, j) and D (i, j), and c (i-1, j) and c (i, j) are the standard deviation of D (i-1, j) and D (i, j), respectively, and the specific form is as follows:
the threshold value epsilon of the correlation coefficient is given, the value range of the threshold value epsilon is more than or equal to 0 and less than or equal to 0.5, and the specific value can be selected according to the actual situation. If the correlation coefficient r (i-1, i, j) exists 0 )＝0(1≤j 0 Ms) indicates the j-th element in T (i) 0 Reception data D (i, j) corresponding to each transmission electrode 0 ) With the data D (i-1, j) of the previous period 0 ) If the data is completely irrelevant, the data can be regarded as irrelevant abnormal data to be marked, and the abnormal data is corrected by utilizing the early monitoring data, and the formula is as follows:
if there is a correlation coefficient of 0<|r(i-1,i,j 1 )|<ε(1≤j 1 Ms) indicates the j-th element in T (i) 1 Reception data D (i, j) corresponding to each transmission electrode 1 ) And the data D (i-1, j) of the previous time interval 1 ) The degree of correlation is low, and the part of data can be regarded as low degree of correlationThe abnormal data of (2) is marked and corrected by using the monitoring data of the previous time interval, and the formula is as follows:
D(i，j 1 )＝r(i-1,i,j 1 )D(i,j 1 )+(1-r(i-1,i,j 1 ))D(i-1,j 1 )(i＞1)
if all elements of R (i-1, i) (i >0) satisfy | R (i-1, i, j) | ≧ epsilon (j ═ 1, … …, ms), which indicates that there is a correlation between the monitored data T (i) and T (i-1), and the cleaning of the monitored data in the next period can be continued without marking abnormal data. After the correction of all abnormal data in the monitoring data of the current time period is completed, the cleaning of the monitoring data of the next time period can be continued.
Claims (5)
1. A method for data cleaning of borehole electrical monitoring data using correlation analysis, comprising:
grouping data corresponding to the same transmitting electrode and different receiving electrodes in each monitoring period;
carrying out normalization processing on the grouped monitoring data;
calculating a correlation coefficient matrix of monitoring data in two adjacent time periods based on the data after normalization processing;
cleaning monitoring data based on the correlation coefficient matrix;
wherein, the grouping of the data corresponding to the same transmitting electrode and different receiving electrodes in each monitoring period comprises:
recording monitoring data of an ith monitoring period as T (i), wherein i is 0,1, … … and n, wherein T (0) is initial monitoring data;
grouping data corresponding to the same transmitting electrode and different receiving electrodes into a group, and recording the group as D (i, j), wherein j is the serial number of the transmitting electrode;
recording data collected by the same transmitting electrode and the same receiving electrode in the monitoring data T (i) as d (i, j, k), wherein k is the number of the receiving electrode;
wherein, T (i), D (i, j) and D (i, j, k) are as follows:
in the formula, ms is the number of transmitting electrodes, and mr is the number of receiving electrodes;
when calculating the correlation coefficient matrix of the monitoring data in two adjacent time periods based on the data after the normalization processing, calculating the correlation coefficient matrix of the monitoring data in two adjacent time periods based on the following formula:
wherein s (i-1, i, j) is the covariance of D (i-1, j) and D (i, j), and c (i-1, j) and c (i, j) are the standard deviation of D (i-1, j) and D (i, j), respectively; r (i-1, i) represents a correlation coefficient matrix of the monitoring data T (i-1) and the monitoring data T (i);
wherein cleaning the monitoring data based on the correlation coefficient matrix comprises: if R (i-1, i) (i)>0) In the presence of a correlation coefficient r (i-1, i, j) 0 ) 0, wherein 1 is not more than j 0 Ms or less, adding j in T (i) 0 Received data D (i, j) corresponding to each transmitting electrode 0 ) Anomalous data, considered irrelevant, are labeled and corrected based on:
if R (i-1, i) (i)>0) In the presence of a correlation coefficient of 0<|r(i-1,i,j 1 )|<ε(1≤j 1 Ms) or less, adding j in T (i) 1 Reception data D (i, j) corresponding to each transmission electrode 1 ) Abnormal data labeled as low in correlation, which is corrected based on the following formula:
D(i,j 1 )＝r(i-1,i,j 1 )D(i,j 1 )+(1-r(i-1,i,j 1 ))D(i-1,j 1 )(i≥1)
and if all elements of R (i-1, i) meet | R (i-1, i, j) | ≧ epsilon (j ═ 1, … …, ms), which indicates that the monitoring data T (i) and T (i-1) have correlation, cleaning the monitoring data in the next period, and giving a threshold value epsilon of the correlation coefficient, wherein the value range is more than or equal to 0 and less than or equal to 0.5.
2. The method for data cleansing of borehole electrical monitoring data using correlation analysis as claimed in claim 1 wherein the initial monitoring data T (0) is collected in case all monitoring electrodes of the monitoring system are working properly.
3. The method for performing data cleaning on the monitoring data of the mining electrical method by utilizing correlation analysis as claimed in claim 1, wherein outlier correction and smooth filtering are performed on the grouped data corresponding to the same transmitting electrode and different receiving electrodes, so as to eliminate isolated abnormal point data.
4. The method of claim 1, wherein the covariance s (i-1, i, j) of D (i-1, j) and D (i, j) is calculated based on the following equation:
5. the method for data cleansing of electrodeionization data using correlation analysis of claim 1, wherein the standard deviations c (i-1, j) and c (i, j) are calculated based on the formula:
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