CN108020649B - Method for determining karst spring supply channel and strength - Google Patents
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Abstract
The invention discloses a method for determining a karst spring supply channel and strength, which comprises the following steps: the method comprises the steps of firstly, judging the sensitivity of spring water level to rainfall and rainfall stations, secondly, determining the depth and the distribution of a main runoff zone, thirdly, verifying the depth and the distribution of the main runoff zone determined in the second step through a temperature and conductivity tracing technology, and fourthly, calculating the strength of different runoff replenishing sources. On the basis of the essence of regional hydrogeological conditions, the method accurately identifies the field large-scale karst large spring supply channel through the four factors of the correlation between a rainfall site and the spring water level, the karst fracture rate, the temperature tracing and the water chemical mixing ratio, provides scientific basis for the protection of the flow rate and the water quality of the karst large spring water, and provides decision support for the government related departments to manage underground water resources.
Description
Technical Field
The invention relates to the technical field of hydrology and geology, in particular to a method for determining a karst spring supply channel and strength thereof.
Background
With the change of global climate and environment and the aggravation of human activities, in recent years, a plurality of environmental hydrogeological problems such as spring flow failure, spring water pollution and the like appear in a karst distribution area, so a karst water supply channel becomes a difficult problem for the research of the karst science at home and abroad. Although predecessors try to research the runoff source of the karst water system by methods such as computer numerical simulation inversion, optimization algorithm identification, probability statistical model, indoor experiment and the like, due to the limitation of the technological development level, an effective method for accurately finding the underground supply runoff channel of the karst spring domain is not available under the modern scientific and technical conditions.
At present, the methods for supplying the channel and the strength of the karst spring are all stopped at a hydrogeological condition generalization stage, and have three insurmountable fatal defects: the generalized hydrogeological conditions are too ideal, are separated from natural actual conditions and cannot be used for accurately guiding engineering practice; secondly, due to the restriction of time scale and space scale, especially the scale effect of an indoor experimental method is huge, and the acquired hydrogeological parameters often have errors of several orders of magnitude; and thirdly, the three-dimensional space distribution of the karst channels cannot be accurately obtained in the horizontal direction and the vertical direction.
Disclosure of Invention
Aiming at the problems, the invention provides a method for determining a karst fountain replenishment channel and strength, which is based on the essence of regional hydrogeological conditions, accurately identifies the field large-scale karst fountain replenishment channel through four factors of correlation between rainfall sites and spring water level, karst fracture rate, temperature tracing and water chemical mixing ratio, provides scientific basis for the protection of karst large spring water flow and water quality, and provides decision support for the government related departments to manage underground water resources.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for determining a supply passage and strength of a karst spring comprises the following steps,
firstly, judging the sensitivity of the spring water level to precipitation rainfall sites,
1.1, determining the positions of rainfall stations, the lithology of the stratum where the rainfall stations are located and the distance between the rainfall stations and a spring group in a research area;
1.2 the lag time of the designed water level responding to rainfall is 1d, 2d, 3d, 4d, 5d and 6d respectively;
1.3, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary rainfall scale is 1 d;
1.4, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the delay time of each rainfall station under the secondary rainfall scale is 2 d;
1.5 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary rainfall scale is 3 d;
1.6, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary rainfall scale is 4 d;
1.7, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the delay time of each rainfall station under the secondary rainfall scale is 5 d;
1.8 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the delay time of each rainfall station under the secondary rainfall scale is 6 d;
1.9 taking the maximum correlation coefficient of each rainfall station as the correlation coefficient of the corresponding rainfall station, selecting the correlation coefficient R2The rainfall station with the value of more than 0.75 is taken as a related close rainfall station;
1.10 obtaining control areas of all the closely related rainfall stations by adopting a Thiessen polygon method, and taking the control areas of the closely related rainfall stations as optimal source supplementing areas;
secondly, determining the depth and the distribution of the main runoff zone,
2.1, selecting the drill hole in the optimal source repairing area determined in the first step, counting the development condition of pore fractures of the rock core every 5 meters in the drill hole in the area, and measuring the diameter of the karst cave and the length and width of the fractures;
2.2 calculating the karst fracture rate of different depths in the drill hole according to the measurement result in the step 2.1, and determining the depth and the distribution of the main runoff zone by taking the karst fracture rate more than 5% as a main channel and other channels as secondary channels;
thirdly, verifying the depth and the distribution of the main flow zone determined in the second step by a temperature and conductivity tracing technology,
3.1 selecting a drill hole containing a main channel, observing the temperature and the conductivity in real time according to 1m intervals in the drill hole, and synchronously measuring the temperature and the conductivity of spring water;
and 3.2, analyzing the observation result of the step 3.1 and preliminarily determining the flow rate change rule.
3.3 according to the temperatures of the different depths of the drill hole obtained in the step 3.1, calculating the flow rate of the groundwater through a VFLUX calculation program, and further determining a flow rate change rule.
3.4 observing whether the flow rate change rule of the drill hole containing the main channel obtained in the step 3.3 is identical with the karst fracture development rule obtained in the step two, and if not, checking and calculating the karst fracture rate of the drill hole which is not identical with the drill hole and has different depths;
fourthly, calculating the intensity of different runoff supply sources,
4.1 dividing the optimal source supplementing area determined in the first step into a plurality of subareas according to the groundwater flow field, the type of the aquifer, the supplementing direction and the type of the source supplementing source, taking a water sample A in each subarea, locating a sampling point in the groundwater supplementing runoff area, and determining the water chemical parameters in the water sample A, wherein the number of the subareas of the optimal source supplementing area is more than or equal to the number of the water chemical parameters;
4.2 taking 1 group of spring water samples, and determining water chemical parameters in the spring water samples;
4.3 according to formula Xi*Wi＝YiCalculating the mixing ratio of the spring water supply sources of the optimal supply region by adopting a mechanical mixing method, wherein XiAs a water chemistry parameter, WiRatio of mixing sources to spring water, YiWater chemical parameters corresponding to spring water;
4.4, performing rationality analysis on the mixing ratio obtained in the step 4.3 by adopting PHREEQC numerical simulation inversion.
Further, the statistical time of steps 1.3-1.8 is not less than three years.
Further, drawing a correlation coefficient curve graph of different lag times of each rainfall station under the scale of the secondary rainfall according to the results of the steps 1.3-1.8.
And further, drawing a karst channel distribution diagram according to the calculation result of the step 2.2.
Further, the observation time of step 3.1 is not less than one year, the observation frequency is 1 time per week, and each observation time is not less than 1 min.
Further, a water temperature change curve graph and a conductivity change curve graph along with the depth are respectively drawn according to the observation result of the step 3.1.
Further, the water chemistry parameter in the fourth step includes Ca2+、Mg2+、Na+、K+、HCO3 -、SO4 2-、Cl-Soluble SiO2Conductivity and total dissolved solids.
The invention has the beneficial effects that:
1. factors such as the lithology of the stratum in the area where the rainfall station is located, the relation between rainfall and spring water level, the development degree of karst, the fracture rate of the karst, the spring water level, the chemical components of groundwater and water, the chemical components of spring water and water, the water temperature variation of groundwater and the like are comprehensively considered, and the method has comprehensive index factors, and is scientific and systematic.
2. The area of using rainfall website control is the plane research district region of horizontal direction, and the quality of water index with the karst crack rate of the different degree of depth, the different degree of depth temperature, the different degree of depth in the drilling retrains in the vertical direction, has eliminated the yardstick effect of indoor experiment, has realized the accurate location of the big spring supply runoff passageway space of karst exhibition cloth, has stronger practicality.
3. The method is based on precipitation, water level, water temperature, fracture rate and actual observation data of tested ion components, does not need artificial generalization on geological conditions, does not have calculation errors, and has higher precision and reliability.
4. The method is simple, scientific in principle, free of large investment and other costs, easy to obtain data, convenient to explain a calculation result, free of multiple solutions and high in technical economy.
5. The implementation can accurately find the space distribution position of the karst spring supply runoff channel without investing high engineering exploration cost, find out spring water supply sources in different directions and different depths, and further give out a section suitable for recharging the sources, so that targeted protection measures can be provided, and spring water pollution is prevented.
Drawings
FIG. 1 is a flow chart of a method of determining karst spring replenishment paths and strengths;
FIG. 2 is a correlation straight line of 1 day lag time of a Xinglong rainfall station;
FIG. 3 is a graph of correlation coefficients for different lag times for each rainfall station at a sub-rainfall scale;
FIG. 4 is a map of a modified Thiessen polygonal rainfall station control area according to terrain;
FIG. 5 is a diagram of a karst fracture development depth distribution in the Jinan spring region;
fig. 6 is a graph of different depth conductivity changes.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly apparent, the technical solutions of the present invention are further described in detail below by taking the spring water of south china as an example.
A method for determining a karst spring supply passage and strength comprises the following steps:
first, the sensitivity of spring water level to precipitation rainfall site is determined
1.1 determining the positions of rainfall stations in the research area, the lithology of the stratum where the rainfall stations are located and the distance between the rainfall stations and the spring group.
1.2 lag time of the design water level in response to rainfall is 1d, 2d, 3d, 4d, 5d, 6d, respectively (where d is an abbreviation for day, representing day).
1.3 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary precipitation scale is 1d (namely 1d after the secondary precipitation), wherein the counting time is not less than three years.
Taking a boom-making rainfall station as an example, the rainfall of each rainfall station in 2009-2011 and the spring water level variation after each rainfall finishes 1d are counted. Then, the rainfall amount of the secondary rainfall is taken as the abscissa, the spring water level amplitude is taken as the ordinate, a discrete point group is drawn according to the rainfall amount of the secondary rainfall of the bloom rainfall station and the corresponding spring water level amplitude, a correlation equation is obtained according to a correlation straight line drawn by the discrete point group, as shown in fig. 2, and finally a correlation coefficient R with the lag time of 1 day of the bloom rainfall station is obtained2。
1.4 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary precipitation scale is 2d (namely 1d after the secondary precipitation), wherein the counting time is not less than three years.
1.5 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary precipitation scale is 3d (namely 1d after the secondary precipitation), wherein the counting time is not less than three years.
1.6 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary precipitation scale is 4d (namely 1d after the secondary precipitation), wherein the counting time is not less than three years.
1.7 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary precipitation scale is 5d (namely 1d after the secondary precipitation), wherein the counting time is not less than three years.
1.8 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary precipitation scale is 6d (namely 1d after the secondary precipitation), wherein the counting time is not less than three years.
And (3) drawing a correlation coefficient curve graph of different lag times of each rainfall station under the scale of the secondary rainfall according to the results of the steps 1.3-1.8.
1.9 according to the drawn correlation coefficient curve chart of different lag times of each rainfall station under the scale of secondary rainfall, taking the maximum correlation coefficient of each rainfall station as the correlation coefficient of the corresponding rainfall station, and selecting a correlation coefficient R2The rainfall station with the value of more than 0.75 is taken as a related close rainfall station.
1.10 obtaining the control area of each closely related rainfall station by adopting a Thiessen polygon method, as shown in figure 4, and taking the control area of the closely related rainfall station as an optimal source supplement area. I.e. the distribution of the karst feeder channels is taken from the horizontal direction.
Second, determining the depth and distribution of the main radial flow zone
2.1, selecting the drill holes in the optimal source repairing area determined in the first step, counting the development conditions of pore fractures of the rock core every 5 meters in the drill holes in the area, and measuring the diameter of the karst cave and the length and width of the fractures.
The borehole described herein is the original borehole in the area, such as a motor-pumped well or the like. As a specific implementation manner, in this embodiment, there are 213 drill holes in the best compensation area determined by the first step in the juana
2.2 calculating the karst fracture rate of the 213 boreholes at different depths according to the measurement result in the step 2.1, and drawing a karst channel distribution diagram by taking the karst fracture rate more than 5% as a main channel and the others as secondary channels.
The result shows that the Jinan spring water has karst runoff channels with different depths, and the channels can be divided into <40m, 40-105m, 105-150m, 150-210m and 210-250m according to the buried depth. As shown in fig. 5, shallow major runoff channels at 40m were distributed mainly near the four spring flock drainage areas; medium development of karst within the buried depth range of 40-105 m; 105-150m buried depth range, the development stratum is mainly the Aotao three mountain sets and the Ma Jia Shuang Bei Zhuang section; the karst development distribution area is smaller in the burial depth range of 210- & ltSUB & gt, 250 m.
And thirdly, verifying the depth and the distribution of the main flow zone determined in the second step by a temperature and conductivity tracing technology.
When the main reason for the verification step of the third step is set, in the actual operation of the second step, the deepest drill hole is as deep as 300m, the core is counted at intervals of 5m, actually, the sampling rate of the core is often not 100%, and the sampling rate of the core in the pore fracture development section is often only 70%, so that in the actual measurement process, errors may exist between the core and the corresponding depth of the core, and the final result of the second step is finally influenced. For this purpose, the depth of the main flow path zone determined in the second step and its distribution are verified in a third step by means of temperature and conductivity tracing techniques.
3.1 selecting a drill hole containing a main channel, observing the temperature and the conductivity in real time in the drill hole at intervals of 1m, and synchronously measuring the temperature and the conductivity of the spring water, wherein the observation time is not less than one year, the observation frequency is 1 time per week, and the observation time is not less than 1min each time.
3.2 respectively drawing a water temperature change curve graph along with the depth and a conductivity change curve graph along with the depth according to the observation result of the 3.1, and preliminarily determining the flow rate change rule.
For the same depth, if there is a primary path channel, the greater the flow rate, the greater the magnitude of the conductivity (or temperature) change at that depth after precipitation replenishment, which is reflected in a graph of conductivity (or temperature) versus depth: a larger magnitude of conductivity (or temperature) change at a depth indicates a faster flow rate at that depth.
3.3 according to the temperatures of the different depths of the drill hole obtained in the step 3.1, calculating the flow rate of the groundwater through a VFLUX calculation program, and further determining a flow rate change rule.
Taking the Xinglong 204-meter depth observation hole as an example, a conductivity change curve at different depths is drawn as shown in fig. 6, and the flow velocity at different depths in the hole is calculated by using a VFLUX calculation program. Since the number of measurement points in the observation hole is large, the groundwater flow velocities at 10m, 15m, 20m, 25m, 30m and 35m are listed in table 1.
TABLE 1 different depth flow Rate statistics Table under VFLUX calculation
| Depth/m | Hatch phase method (m/s) |
| 12.5 | 7.87×10-4～1.06×10-3 |
| 15 | 7.19×10-4～9.53×10-4 |
| 17.5 | 6.62×10-4～9.17×10-4 |
| 20 | 6.29×10-4～8.87×10-4 |
| 22.5 | 5.99×10-4～8.68×10-4 |
| 25 | 6.64×10-4～8.03×10-4 |
| 27.5 | 6.35×10-4～8.6×10-4 |
| 30 | 6.95×10-4～8.95×10-4 |
| 32.5 | 7.67×10-4～9.49×10-4 |
As can be seen from the analysis of FIG. 6 and Table 1, the variation rule of the sections 6-35m in FIG. 5 is the same as that of Table 1.
And 3.4, observing whether the flow rate change rule of the drill hole containing the main channel obtained in the step 3.3 is identical with the karst fracture development rule obtained in the step two. If the drill holes do not coincide with each other, the karst fracture rates of different depths of the non-coincident drill holes are checked and calculated again.
The flow rate can be increased along with the increase of the karst fracture rate, so that the karst fracture development rule is identical by observing the change rule of the flow rate, and the correctness of the second step can be verified.
Fourthly, calculating the intensity of different runoff supply sources
4.1 dividing the optimal replenishment area determined in the first step into 10 subareas according to the groundwater flow field, the type of the aquifer, the replenishment direction and the replenishment source type, taking a water sample A in each subarea, locating the sampling point in the groundwater replenishment runoff area, and measuring the water chemical parameters including Ca in the water sample A2+、Mg2+、Na+、K+、HCO3 -、SO4 2-、Cl-Soluble SiO2Conductivity and total dissolved solids.
4.2 taking 1 group of spring water samples and determining spring waterWater chemical parameters in water samples, including Ca2+、Mg2+、Na+、K+、HCO3 -、SO4 2-、Cl-Soluble SiO2Conductivity and total dissolved solids.
4.3 according to formula Xi*Wi＝YiCalculating the mixing ratio of the spring water supply sources of the optimal supply region by adopting a mechanical mixing method, wherein XiAs a water chemistry parameter, WiRatio of mixing sources to spring water, YiThe water chemical parameters corresponding to spring water.
4.4, performing rationality analysis on the mixing ratio obtained in the step 4.3 by adopting PHREEQC numerical simulation inversion.
As a specific implementation manner, in this embodiment, the content of the water chemical component of the spring water is used as a calculation target value, the spring water supply sources are classified according to the groundwater flow field, the water-containing layer type, the supply source orientation and the supply source type, and the water chemical test data is divided into 11 subareas. Setting a 1-11 partition as a complementary water source of Baotu spring, wherein the index participating in calculation comprises Ca2+、Mg2+、Na+、K+、HCO3 -、SO4 2-、Cl-Soluble SiO2Total solids of conductivity and solubility 10, listed as matrix X ═ X1,X2,X3,X4,X5,X6,X7,X8,X9,X10]TThe contribution ratio of 11 replenishment sources to the spring water is w ═ w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,w11]TBaotu spring as example Y ═ Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10]TNeglecting other make-up water sources, ∑ Wi1, then, X × W ═ Y.
And obtaining the replenishment mixing proportion of the Baotu spring through trial calculation and inspection. The mixing ratio of Baotu spring make-up water sources respectively accounts for 25.0% of Zhang summer group aquifer, 40.0% of Bakatu Fengshan group-Otaotai aquifer replenishment, 10.0% of Suanyang lake recharge water, 6.0% of Xinji river recharge water, 8.0% of Yuzhu river recharge water and 8% of city district recharge water.
And (3) rationality analysis: and verifying the reliability of the calculated result of the spring water mixing ratio by adopting a hydrological geochemistry simulation method. Ca2+Is the main cation in the Jinan karst water, and the PREEQC simulated karst water is saturated by calcite, so Ca is used2+And comparing the calculated Baotu spring chemical index value with an actually measured value as a basis for equation set balancing, wherein the allowable error is that the absolute value of the relative error is less than 10%, and the calculation abnormality is that the absolute value of the relative error is more than 10% (positive value is positive abnormality, and negative value is negative abnormality). The chemical indicator of normal and abnormal Baotu spring water quality is SO4 2-Chemical indicators of negative abnormalities are Na+。SO4 2-The positive abnormality of the ions indicates that the spring water is slightly polluted, the pollution is from a sewage pipe network at the upstream of the spring opening, but SO4 2-The content is low, and the water quality type of spring water is not changed far, although Na+Negative abnormality but Na in karst water+The content is extremely low, and the spring water quality is almost not influenced, so the obtained mixing ratio W is reasonable.
Claims (7)
1. A method for determining a karst spring supply channel and strength is characterized in that: comprises the following steps of (a) carrying out,
firstly, judging the sensitivity of the spring water level to precipitation rainfall sites,
1.1, determining the positions of rainfall stations, the lithology of the stratum where the rainfall stations are located and the distance between the rainfall stations and a spring group in a research area;
1.2 the lag time of the designed water level responding to rainfall is 1d, 2d, 3d, 4d, 5d and 6d respectively;
1.3, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary rainfall scale is 1 d;
1.4, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the delay time of each rainfall station under the secondary rainfall scale is 2 d;
1.5 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary rainfall scale is 3 d;
1.6, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the lag time of each rainfall station under the secondary rainfall scale is 4 d;
1.7, respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the delay time of each rainfall station under the secondary rainfall scale is 5 d;
1.8 respectively counting the correlation coefficient between the rainfall amount and the spring water level amplitude when the delay time of each rainfall station under the secondary rainfall scale is 6 d;
1.9 taking the maximum correlation coefficient of each rainfall station as the correlation coefficient of the corresponding rainfall station, selecting the correlation coefficient R2The rainfall station with the value of more than 0.75 is taken as a related close rainfall station;
1.10 obtaining control areas of all the closely related rainfall stations by adopting a Thiessen polygon method, and taking the control areas of the closely related rainfall stations as optimal source supplementing areas;
secondly, determining the depth and the distribution of the main runoff zone,
2.1, selecting the drill hole in the optimal source repairing area determined in the first step, counting the development condition of pore fractures of the rock core every 5 meters in the drill hole in the area, and measuring the diameter of the karst cave and the length and width of the fractures;
2.2 calculating the karst fracture rate of different depths in the drill hole according to the measurement result in the step 2.1, and determining the depth and the distribution of the main runoff zone by taking the karst fracture rate more than 5% as a main channel and other channels as secondary channels;
thirdly, verifying the depth and the distribution of the main flow zone determined in the second step by a temperature and conductivity tracing technology,
3.1 selecting a drill hole containing a main channel, observing the temperature and the conductivity in real time according to 1m intervals in the drill hole, and synchronously measuring the temperature and the conductivity of spring water;
3.2 analyzing the observation result of 3.1 and preliminarily determining the change rule of the flow rate;
3.3, calculating the flow rate of the groundwater through a VFLUX calculation program according to the temperatures of the drill holes with different depths obtained in the step 3.1, and further determining a flow rate change rule;
3.4 observing whether the flow rate change rule of the drill hole containing the main channel obtained in the step 3.3 is identical with the karst fracture development rule obtained in the step two, and if not, checking and calculating the karst fracture rate of the drill hole which is not identical with the drill hole and has different depths;
fourthly, calculating the intensity of different runoff supply sources,
4.1 dividing the optimal source supplementing area determined in the first step into a plurality of subareas according to the groundwater flow field, the type of the aquifer, the supplementing direction and the type of the source supplementing source, taking a water sample A in each subarea, locating a sampling point in the groundwater supplementing runoff area, and determining the water chemical parameters in the water sample A, wherein the number of the subareas of the optimal source supplementing area is more than or equal to the number of the water chemical parameters;
4.2 taking 1 group of spring water samples, and determining water chemical parameters in the spring water samples;
4.3 according to formula Xi*Wi＝YiCalculating the mixing ratio of the spring water supply sources of the optimal supply region by adopting a mechanical mixing method, wherein XiAs a water chemistry parameter, WiRatio of mixing sources to spring water, YiWater chemical parameters corresponding to spring water;
4.4, performing rationality analysis on the mixing ratio obtained in the step 4.3 by adopting PHREEQC numerical simulation inversion.
2. The method of claim 1, wherein the method further comprises the steps of: the statistical time of the steps 1.3-1.8 is not less than three years.
3. The method of claim 1, wherein the method further comprises the steps of: and (4) drawing a correlation coefficient curve graph of different delay times of all rainfall stations under the scale of secondary rainfall according to the results of the steps 1.3-1.8.
4. The method of claim 1, wherein the method further comprises the steps of: and (3) drawing a karst channel distribution diagram according to the calculation result of the step 2.2.
5. The method of claim 1, wherein the method further comprises the steps of: the observation time of the step 3.1 is not less than one year, the observation frequency is 1 time per week, and the observation time is not less than 1min each time.
6. The method of claim 1, wherein the method further comprises the steps of: and (4) respectively drawing a water temperature change curve graph along with the depth and a conductivity change curve graph along with the depth according to the observation result of the step 3.1.
7. The method of claim 1, wherein the method further comprises the steps of: the water chemistry parameter in the fourth step comprises Ca2+、Mg2+、Na+、K+、HCO3 -、SO4 2-、Cl-Soluble SiO2Conductivity and total dissolved solids.
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| CN108845111A (en) * | 2018-07-04 | 2018-11-20 | 河南理工大学 | The analytical equipment and analysis method that water flow loses solvable karst under a kind of open system |
| CN109030775A (en) * | 2018-07-04 | 2018-12-18 | 河南理工大学 | The analytical equipment and analysis method that water flow loses solvable karst under a kind of closed system |
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