CN113738448A - Mining rock mass seepage and water inrush near-far multisource grading information intelligent monitoring and early warning method - Google Patents
Mining rock mass seepage and water inrush near-far multisource grading information intelligent monitoring and early warning method Download PDFInfo
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Abstract
The invention discloses an intelligent monitoring and early warning method for near-far multisource grading information of water seepage and water inrush of a mined rock mass, and belongs to the field of water-retention mining and remote sensing rock mechanics. In the mining process, a micro-seismic monitoring system, a mining geological radar monitoring system and an infrared radiation monitoring system are jointly used for monitoring and carrying out grading early warning on water seepage and inrush conditions of three spatial positions far away and near a mining rock mass, multi-source information grading early warning indexes and corresponding discrimination thresholds are determined, water seepage and inrush in a monitoring area on the surface of a top plate and a bottom plate are collected and sampled, water seepage and inrush sources are analyzed, and whether water seepage cracks of a top plate and a bottom plate aquifer are communicated or not is judged in an auxiliary mode. And forming a multi-source information real-time grading information intelligent monitoring and early warning system for the mining rock mass seepage and water inrush far and near. The method can improve the monitoring and early warning precision of water seepage and water inrush and coal and rock stability in the excavation process, and lays a foundation for water-retaining mining and safe and efficient mining of mines.
Description
Technical Field
The invention relates to an intelligent monitoring and early warning method for near-far multisource grading information of water seepage and water inrush of a mining rock mass, and belongs to the field of water-retention mining and remote sensing rock mechanics.
Background
Along with the increase of the demand of coal resources, the production scale of coal is continuously enlarged, and the water seepage and inrush disasters of coal mines not only cause serious casualties and economic losses, but also cause underground water pollution and loss and irreversible damage to the environment, thereby becoming an important problem restricting the safe and efficient production of coal mines.
Along with the trend of intensive and large-scale rapid development of coal production, the prevention and control of the coal mine water seepage and inrush disaster not only needs to control deformation and crack development of a stope mining rock mass, but also needs to monitor and early warn the occurrence condition of the stope water seepage and inrush in real time. If the problems of inaccurate early warning of seepage and water inrush of overlying rocks of a stope, misjudgment and the like occur, disasters such as water inrush of a mine and the like can be caused, and immeasurable mischief such as water resource loss of the mine, ecological environment degradation of a mining area and the like can be caused. The research on monitoring and early warning technology of water seepage and water inrush of a stope is enhanced, water inrush disasters of mines are reduced, and the method is one of the problems to be solved urgently in realizing coal safety production. However, at present, domestic coal mine water seepage (inrush) monitoring and early warning mainly depends on workers to detect water by adopting exploration methods of drilling and geophysical prospecting, which not only occupies a large amount of human resources, but also has a lot of human errors, and an accurate, effective and real-time dynamic monitoring system is still lacking. Therefore, the invention provides a remote and near multi-source information grading intelligent monitoring and early warning method for mining rock mass seepage and water inrush by utilizing a micro-seismic monitoring system, a geological radar monitoring system, an infrared radiation monitoring system, seepage and water inrush water quality ion component analysis and the like, and has important significance for monitoring and early warning of stope overlying strata seepage and water inrush.
Disclosure of Invention
Aiming at the defects of the prior art, the mining rock mass water seepage and water inrush far and near multi-source grading information intelligent monitoring and early warning method is provided, the steps are simple, the automation degree is high, the monitoring precision is high, and multi-source information grading intelligent monitoring and early warning of water seepage and water inrush of the top plate and the bottom plate are realized.
In order to achieve the technical purpose, the mining rock mass seepage and water inrush far and near multi-source grading information intelligent monitoring and early warning method comprises the steps of firstly analyzing the distribution conditions of a structural key layer and a water-bearing layer of a stope, which are influenced by mining, and further judging the position of a main water-resisting key layer of the stope; in the mining process, a microseismic monitoring system is used for remotely monitoring whether a main water-resisting key layer of a stope top and bottom plate is influenced by mining to generate a water flowing crack channel, an abnormal signal indicates that a crack is generated, and further positioning the abnormal signal, if a positioning point is positioned at the position of the main water-resisting key layer, the water flowing crack is generated, in the mining rock top and bottom plate area, the mining geological radar is used for carrying out mobile continuous back and forth monitoring on the roadway top plate in the area with abnormal signals in a short distance, judging whether a water diversion crack is generated in a short distance range according to the geological radar signal, if an abnormal signal exists, analyzing and positioning water body position parameters generated by the abnormal signal, an infrared radiation fixed monitoring system is arranged on the surface of the top plate area and the bottom plate area corresponding to the positioning points, and if the obtained infrared information mutation exceeds a threshold value, the top plate area is judged to be subjected to water seepage and water inrush; and finally, collecting and sampling seepage and inrush water in the monitoring area, and analyzing ion components of the seepage and inrush water quality by adopting the grey correlation degree so as to analyze a seepage and inrush water source and judge whether water flowing cracks of each aquifer of the top plate are communicated.
The method specifically comprises the following steps:
step 1, judging the distribution conditions of an overlying rock aquifer and a structural key layer of a stope, determining the position of a main water-resisting key layer, and collecting an aquifer water sample;
step 2, analyzing the change condition of the micro-seismic energy density release rate according to the monitoring data acquired by the existing micro-seismic remote monitoring system in the mine, positioning abnormal signals according to the change condition, judging whether the main water-resisting key layer generates water diversion cracks or not by judging whether the micro-seismic indexes exceed a set threshold value or not and positioning points are positioned at the main water-resisting key layer, and executing the next step and carrying out remote forecasting if the micro-seismic indexes exceed the set threshold value;
step 3, carrying out mobile continuous monitoring on the close range of the top and bottom plates by using a mining geological radar, judging whether seepage water reaches the close range after the main waterproof key layer is damaged according to a monitoring result, if the geological radar oscillogram shows that an abnormal signal appears, positioning a seepage and water inrush area according to a geological radar electromagnetic wave speed average amplitude and a monitoring mileage curve chart read by the geological radar, and carrying out close range prediction on the seepage and water inrush of the mining rock mass;
step 4, performing infrared radiation critical fixed monitoring on the surface area of the top and bottom plates vertically corresponding to the positioning area in the step 3, if the infrared radiation monitoring signal is mutated, indicating that the top and bottom plate surfaces of the monitoring area are subjected to water seepage and water bursting, and performing critical distance prediction;
and 5, collecting a water sample of the water seepage and outburst on the surface of the top and bottom plates for detection, analyzing the correlation degree of the aquifer water sample and the water seepage and outburst water sample by adopting gray correlation degree, so as to judge the water source of the surrounding rock water seepage and outburst of the mining face, if the ingredients of the aquifer water sample and the water seepage and outburst water sample are consistent, judging that the water diversion fissure channel of the mining rock body is communicated, and otherwise, water seepage is carried out in other places.
And 2, monitoring the main water-proof key layer by using a microseismic monitoring system. If the energy release rate at the position of the main water-resisting key layer is greater than a threshold value, judging that an abnormal signal is generated, further using a positioning algorithm to position the abnormal signal, and if the positioning point is positioned at the position of the main water-resisting key layer, determining that a water flowing fracture channel is generated; the abnormal signal judging method comprises the following steps:
let the microseismic event time series be { t }1，t2，…，tnIs corresponding to an energy sequence of { E }1，E2，…EnIs then tiThe time-corresponding energy release rate is:
if ε is satisfied>a is judged as an abnormal signal, wherein epsilon represents tiThe energy release rate corresponding to the time, a, represents the energy release rate threshold.
And 3, the short-distance monitoring and forecasting is to monitor the top and bottom plate rock stratums in a short-distance range by using a mining geological radar, locate the water seepage and inrush area, detect the abnormal area by using a geological radar oscillogram, and draw a geological radar electromagnetic wave speed average amplitude and monitoring mileage curve graph if the oscillogram shows that the abnormal area occurs so as to locate the position parameter of the abnormal area.
Monitoring and forecasting the critical distance, namely automatically monitoring and forecasting the top plate surface area vertically corresponding to the positioning area by using an infrared radiation monitoring system, and judging that water seepage and water inrush occur on the top plate surface if the infrared radiation difference infrared radiation variance index has mutation exceeding a threshold value; the specific calculation formula of the difference infrared radiation variance index is as follows:
VOIIT(p)>b
VOIIT (p) is the difference infrared radiation variance of the infrared radiation temperature matrix of the p frame; AIRT (p) is the average value of the infrared radiation temperature matrix of the p frame; l isxAnd LyThe maximum row number and the maximum column number of the infrared radiation temperature matrix are respectively; p is the serial number of the infrared radiation temperature matrix; f. ofp(x, y) is the temperature value of the x row and y column in the p frame infrared radiation temperature matrix; b is a discrimination threshold.
The grey correlation degree water chemical ion analysis method comprises the following steps:
taking the ion component sequence of the water sample of the aquifer as a mother sequence X1(k) The surface water seepage and inrush water sample of the monitoring area is regarded as a subsequence Xi(k) Wherein i (i ═ 1,2,3.. imax) K (k is 1,2,3 … k) is the number of subsequences, i.e. the number of positions where the IR signal is mutated in step 4max) For the number of ion components in the water sample, the son sequence and the mother sequence are uniformly merged and written into Xi(k) Wherein i ═ 1 represents the parent sequence, and the remainder are the subsequences. Subjecting both of these sequences toCarrying out value dimensionless treatment to make the new sequence have comparability and equivalence, and recording the expression after the mean value as X for calculation conveniencei(k) The calculation method comprises the following steps:
calculating the absolute difference sequence delta of the sub-sequence1j(k) And determining the series of step differences Delta1j(k)minAnd Δ1j(k)maxThe calculation method comprises the following steps:
wherein j (j ═ 2,3,4 … imax) Is a subsequence number;
calculating the correlation coefficient of the subsequence to the parent sequence, namely:
in the formula, rho is a resolution coefficient and is generally 0.5. Correlation coefficient xi1j(k) Is the correlation coefficient of the subsequence and the parent sequence under each index of water chemical ion components, wherein j (j is 2,3,4 … i)max) Number of subsequences, k (k ═ 1,2,3 … kmax) The number of chemical ion components of water;
the correlation coefficient sequences are subjected to an averaging process, and the number of the degree of correlation between the subsequence and the parent sequence is expressed as a degree of correlation, namely: the grey correlation between the subsequence and the parent sequence is denoted as r1j；
Degree of gray correlation r1jThe size of the sequence can reflect the correlation degree between the parent sequence and the subsequence, the higher the correlation degree is, the closer the relationship is, and the water seepage and inrush source is the aquifer; degree of associationWhen the content is more than or equal to 0.8, the association between the subsequence and the parent sequence is good; when the degree of association is between 0.6 and 0.8, the association is good; when the degree of association is less than 0.5, it indicates that the subsequence is substantially unrelated to the parent sequence.
Has the advantages that:
the invention combines four nondestructive and efficient monitoring methods for near-far monitoring and early warning of the water seepage and water inrush of overlying strata in the stope, has high automation degree, does not need manual guard, only needs to confirm on site and perform sampling analysis after abnormal conditions are alarmed, greatly improves the early warning accuracy of the used multi-source information grading intelligent monitoring and early warning technology for the water seepage and water inrush, and reduces the influence of the water seepage and water inrush disaster on the mining of mines.
Drawings
FIG. 1 is a schematic flow chart of a mining rock mass seepage water near-far multisource grading information intelligent monitoring and early warning method.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in figure 1, the mining rock mass seepage and water inrush far-near multi-source grading information intelligent monitoring and early warning method is characterized in that: firstly, analyzing the distribution conditions of a structural key layer and a water-bearing layer of a stope, which are influenced by mining, and further judging the position of a main water-resisting key layer of the stope; in the mining process, a microseismic monitoring system is used for remotely monitoring whether a main water-resisting key layer of a stope top and bottom plate is influenced by mining to generate a water flowing crack channel, an abnormal signal indicates that a crack is generated, and further positioning the abnormal signal, if a positioning point is positioned at the position of the main water-resisting key layer, the water flowing crack is generated, in the mining rock top and bottom plate area, the mining geological radar is used for carrying out mobile continuous back and forth monitoring on the roadway top plate in the area with abnormal signals in a short distance, judging whether a water diversion crack is generated in a short distance range according to the geological radar signal, if an abnormal signal exists, analyzing and positioning water body position parameters generated by the abnormal signal, arranging an infrared radiation fixed monitoring system on the surface of the top plate area corresponding to the positioning point, and if the obtained infrared information mutation exceeds a threshold value, judging that the top plate area has water seepage and water burst; and finally, collecting and sampling seepage and inrush water in the monitoring area, and analyzing ion components of the seepage and inrush water quality by adopting the grey correlation degree so as to analyze a seepage and inrush water source and judge whether water flowing cracks of each aquifer of the top plate are communicated.
The method specifically comprises the following steps:
step 1, judging the distribution conditions of an overlying rock aquifer and a structural key layer of a stope, determining the position of a main water-resisting key layer, and collecting an aquifer water sample;
and 2, analyzing the change condition of the micro-seismic energy density release rate according to the monitoring data acquired by the existing micro-seismic remote monitoring system in the mine, positioning abnormal signals according to the change condition, judging whether the main water-resisting key layer generates water diversion cracks or not by judging whether the micro-seismic indexes exceed a set threshold value or not and positioning points are positioned at the main water-resisting key layer, and executing the next step and carrying out remote forecasting if the water diversion cracks are judged to be abnormal.
The remote monitoring method is used for monitoring the main water-proof key layer by using a microseismic monitoring system. If the energy release rate at the position of the main water-resisting key layer is greater than a threshold value, judging that an abnormal signal is generated, further using a positioning algorithm to position the abnormal signal, and if the positioning point is positioned at the position of the main water-resisting key layer, determining that a water flowing fracture channel is generated; the abnormal signal judging method comprises the following steps:
let the microseismic event time series be { t }1,t2,…,tnIs corresponding to an energy sequence of { E }1,E2,…EnIs then tiThe time-corresponding energy release rate is:
if epsilon > a is satisfied, judging the signal as an abnormal signal, wherein epsilon represents tiThe energy release rate corresponding to the moment, a represents the threshold value of the energy release rate;
step 3, carrying out mobile continuous monitoring on the close range of the top and bottom plates by using a mining geological radar, judging whether seepage water reaches the close range after the main waterproof key layer is damaged according to a monitoring result, if the geological radar oscillogram shows that an abnormal signal appears, positioning a seepage and water inrush area according to a geological radar electromagnetic wave speed average amplitude and a monitoring mileage curve chart read by the geological radar, and carrying out close range prediction on the seepage and water inrush of the mining rock mass;
the short-distance monitoring and forecasting is to monitor the top and bottom plate rock stratums in a short-distance range by using a mining geological radar, locate the water seepage and inrush area, detect the abnormal area by using a geological radar oscillogram, and draw a geological radar electromagnetic wave speed average amplitude and monitoring mileage curve graph if the oscillogram shows that the abnormal area occurs so as to locate the position parameter of the abnormal area.
Step 4, performing infrared radiation critical fixed monitoring on the surface area of the top and bottom plates vertically corresponding to the positioning area in the step 3, if the infrared radiation monitoring signal is mutated, indicating that the top and bottom plate surfaces of the monitoring area are subjected to water seepage and water bursting, and performing critical distance prediction;
the critical distance monitoring and forecasting is that an infrared radiation monitoring system is used for automatically monitoring and forecasting the top plate surface area vertically corresponding to the positioning area, and if the infrared radiation difference infrared radiation variance index changes suddenly and exceeds a threshold value, the top plate surface is judged to have water seepage and water inrush; the specific calculation formula of the difference infrared radiation variance index is as follows:
VOIIT(p)＞b
VOIIT (p) is the difference infrared radiation variance of the infrared radiation temperature matrix of the p frame; AIRT (p) is the average value of the infrared radiation temperature matrix of the p frame; l isxAnd LyThe maximum row number and the maximum column number of the infrared radiation temperature matrix are respectively; p is the serial number of the infrared radiation temperature matrix; f. ofp(x,y) is the temperature value of the x row and y column in the p frame infrared radiation temperature matrix; b is a discrimination threshold.
And 5, collecting a water sample of the water seepage and outburst on the surface of the top and bottom plates for detection, analyzing the correlation degree of the aquifer water sample and the water seepage and outburst water sample by adopting gray correlation degree, so as to judge the water source of the surrounding rock water seepage and outburst of the mining face, if the ingredients of the aquifer water sample and the water seepage and outburst water sample are consistent, judging that the water diversion fissure channel of the mining rock body is communicated, and otherwise, water seepage is carried out in other places.
The grey correlation degree water chemical ion analysis method comprises the following steps:
taking the ion component sequence of the water sample of the aquifer as a mother sequence X1(k) The surface water seepage and inrush water sample of the monitoring area is regarded as a subsequence Xi(k) Wherein i (i ═ 1,2,3.. imax) K (k is 1,2,3 … k) is the number of subsequences, i.e. the number of positions where the IR signal is mutated in step 4max) For the number of ion components in the water sample, the son sequence and the mother sequence are uniformly merged and written into Xi(k) Wherein i ═ 1 represents the parent sequence, and the remainder are the subsequences. Carrying out mean value dimensionless treatment on the sequences to ensure that the new sequences have comparability and equivalence, and recording the expression after the mean value as X for the convenience of calculationi(k) The calculation method comprises the following steps:
calculating the absolute difference sequence delta of the sub-sequence1j(k) And determining the series of step differences Delta1j(k)minAnd Δ1j(k)maxThe calculation method comprises the following steps:
wherein j (j ═ 2,3,4 … imax) Is a subsequence number;
calculating the correlation coefficient of the subsequence to the parent sequence, namely:
in the formula, rho is a resolution coefficient and is generally 0.5. Correlation coefficient xi1j(k) Is the correlation coefficient of the subsequence and the parent sequence under each index of water chemical ion components, wherein j (j is 2,3,4 … i)max) Number of subsequences, k (k ═ 1,2,3 … kmax) The number of chemical ion components of water;
the correlation coefficient sequences are subjected to an averaging process, and the number of the degree of correlation between the subsequence and the parent sequence is expressed as a degree of correlation, namely: the grey correlation between the subsequence and the parent sequence is denoted as r1j；
Degree of gray correlation r1jThe size of the sequence can reflect the correlation degree between the parent sequence and the subsequence, the higher the correlation degree is, the closer the relationship is, and the water seepage and inrush source is the aquifer; when the relevance is more than or equal to 0.8, the relevance of the subsequence and the parent sequence is good; when the degree of association is between 0.6 and 0.8, the association is good; when the degree of association is less than 0.5, it indicates that the subsequence is substantially unrelated to the parent sequence.
Claims (6)
1. The mining rock mass water seepage and water inrush far-near multisource grading information intelligent monitoring and early warning method is characterized by comprising the following steps of: firstly, analyzing the distribution conditions of a structural key layer and a water-bearing layer of a stope, which are influenced by mining, and further judging the position of a main water-resisting key layer of the stope; in the mining process, a microseismic monitoring system is used for remotely monitoring whether a main water-resisting key layer of a stope top and bottom plate is influenced by mining to generate a water flowing crack channel, an abnormal signal indicates that a crack is generated, and further positioning the abnormal signal, if a positioning point is positioned at the position of the main water-resisting key layer, the water flowing crack is generated, in the mining rock top and bottom plate area, the mining geological radar is used for carrying out mobile continuous back and forth monitoring on the roadway top plate in the area with abnormal signals in a short distance, judging whether a water diversion crack is generated in a short distance range according to the geological radar signal, if an abnormal signal exists, analyzing and positioning water body position parameters generated by the abnormal signal, arranging an infrared radiation fixed monitoring system on the surface of the top plate area corresponding to the positioning point, and if the obtained infrared information mutation exceeds a threshold value, judging that the top plate area has water seepage and water burst; and finally, collecting and sampling seepage and inrush water in the monitoring area, and analyzing ion components of the seepage and inrush water quality by adopting the grey correlation degree so as to analyze a seepage and inrush water source and judge whether water flowing cracks of each aquifer of the top plate are communicated.
2. The mining rock mass water seepage and water inrush far-near multisource grading information intelligent monitoring and early warning method according to claim 1, which is characterized by comprising the following steps:
step 1, judging the distribution conditions of an overlying rock aquifer and a structural key layer of a stope, determining the position of a main water-resisting key layer, and collecting an aquifer water sample;
step 2, analyzing the change condition of the microseismic energy density release rate by utilizing monitoring data acquired by the existing microseismic remote monitoring system in the mine, positioning abnormal signals according to the change condition, judging whether the main water-resisting key layer generates water diversion cracks or not by judging whether the microseismic indexes exceed a set threshold value or not and positioning points are positioned at the main water-resisting key layer, and executing the next step and carrying out remote forecasting if the microseismic indexes exceed the set threshold value;
step 3, carrying out mobile continuous monitoring on the close range of the top and bottom plates by using a mining geological radar, judging whether seepage water reaches the close range after the main waterproof key layer is damaged according to a monitoring result, if the geological radar oscillogram shows that an abnormal signal appears, positioning a seepage and water inrush area according to a geological radar electromagnetic wave speed average amplitude and a monitoring mileage curve chart read by the geological radar, and carrying out close range prediction on the seepage and water inrush of the mining rock mass;
step 4, performing infrared radiation critical fixed monitoring on the surface area of the top and bottom plates vertically corresponding to the positioning area, if the infrared radiation monitoring signal is mutated, indicating that the top and bottom plate surfaces of the monitoring area are subjected to water seepage and water inrush, and performing critical distance prediction;
and 5, collecting a water sample of the water seepage and outburst on the surface of the top and bottom plates for detection, analyzing the correlation degree of the aquifer water sample and the water seepage and outburst water sample by adopting gray correlation degree, so as to judge the water source of the surrounding rock water seepage and outburst of the mining face, if the ingredients of the aquifer water sample and the water seepage and outburst water sample are consistent, judging that the water diversion fissure channel of the mining rock body is communicated, and otherwise, water seepage is carried out in other places.
3. The mining rock mass water seepage and water inrush far-near multisource grading information intelligent monitoring and early warning method as claimed in claim 2, characterized in that the remote monitoring method in step 2 is used for monitoring a water-proof main key layer by using a micro-seismic remote monitoring system. If the energy release rate at the position of the main water-resisting key layer is greater than a threshold value, judging that an abnormal signal is generated, further using a positioning algorithm to position the abnormal signal, and if the positioning point is positioned at the position of the main water-resisting key layer, determining that a water flowing fracture channel is generated; the abnormal signal judging method comprises the following steps:
let the microseismic event time series be { t }1,t2,…,tnIs corresponding to an energy sequence of { E }1,E2,…EnIs then tiThe time-corresponding energy release rate is:
if epsilon > a is satisfied, judging the signal as an abnormal signal, wherein epsilon represents tiThe energy release rate corresponding to the time, a, represents the energy release rate threshold.
4. The mining rock mass water-inrush far-near multisource grading information intelligent monitoring and early warning method as claimed in claim 2, characterized in that the short-distance monitoring and forecasting of step 3 is that a mining geological radar is used for monitoring a top floor rock layer in a short-distance range, positioning of a water-inrush region is carried out, abnormal region detection is carried out through a geological radar oscillogram, if an abnormal region is displayed on the oscillogram, a geological radar electromagnetic wave speed average amplitude and monitoring mileage curve graph is drawn, and therefore abnormal region position parameters are positioned.
5. The mining rock mass water seepage and water inrush far and near multisource grading information intelligent monitoring and early warning method as claimed in claim 2, characterized in that the critical distance monitoring and forecasting shown in step 4 is implemented by utilizing an infrared radiation monitoring system to automatically monitor and forecast the top plate surface area vertically corresponding to the positioning area, and if the infrared radiation difference infrared radiation variance index is suddenly changed and exceeds a threshold value, the top plate surface can be judged to have water seepage and water inrush; the specific calculation formula of the difference infrared radiation variance index is as follows:
VOIIT(p)＞b
VOIIT (p) is the difference infrared radiation variance of the infrared radiation temperature matrix of the p frame; AIRT (p) is the average value of the infrared radiation temperature matrix of the p frame; l isxAnd LyThe maximum row number and the maximum column number of the infrared radiation temperature matrix are respectively; p is the serial number of the infrared radiation temperature matrix; f. ofp(x, y) is the temperature value of the x row and y column in the p frame infrared radiation temperature matrix; b is a discrimination threshold.
6. The mining rock mass seepage and water inrush near-far multisource grading information intelligent monitoring and early warning method according to claim 2, characterized in that the grey correlation degree water chemical ion analysis method in step 5 is as follows:
taking the ion component sequence of the water sample of the aquifer as a mother sequence X1(k) The surface water seepage and inrush water sample of the monitoring area is regarded as a subsequence Xi(k) Wherein i (i ═ 1,2,3.. imax) K (k is 1,2,3 … k) is the number of subsequences, i.e. the number of positions where the IR signal is mutated in step 4max) Is the ionic component of water sampleNumber, uniformly merging and writing son and mother sequences into Xi(k) Wherein i ═ 1 represents the parent sequence, and the remainder are the subsequences. Carrying out mean value dimensionless treatment on the sequences to ensure that the new sequences have comparability and equivalence, and recording the expression after the mean value as X for the convenience of calculationi(k) The calculation method comprises the following steps:
calculating the absolute difference sequence delta of the sub-sequence1j(k) And determining the series of step differences Delta1j(k)minAnd Δ1j(k)maxThe calculation method comprises the following steps:
wherein j (j ═ 2,3,4 … imax) Is a subsequence number;
calculating the correlation coefficient of the subsequence to the parent sequence, namely:
in the formula, rho is a resolution coefficient and is generally 0.5. Correlation coefficient xi1j(k) Is the correlation coefficient of the subsequence and the parent sequence under each index of water chemical ion components, wherein j (j is 2,3,4 … i)max) Number of subsequences, k (k ═ 1,2,3 … kmax) The number of chemical ion components of water;
the correlation coefficient sequences are subjected to an averaging process, and the number of the degree of correlation between the subsequence and the parent sequence is expressed as a degree of correlation, namely: the grey correlation between the subsequence and the parent sequence is denoted as r1j；
Degree of gray correlation r1jThe size of the sequence can reflect the correlation degree between the parent sequence and the subsequence, the higher the correlation degree is, the closer the relationship is, and the water seepage and inrush source is the aquifer; when the relevance is more than or equal to 0.8, the relevance of the subsequence and the parent sequence is good; when the degree of association is between 0.6 and 0.8, the association is good; when the degree of association is less than 0.5, it indicates that the subsequence is substantially unrelated to the parent sequence.
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