Prediction method of prone areas of soil-cavity karst collapse based on groundwater chemical field monitoring

Abstract

The present invention provides a method for predicting areas prone to soil-hole karst collapse based on groundwater chemical field monitoring, comprising: selecting a working area, arranging observation holes in the working area, collecting groundwater chemical characteristic parameters in each observation hole, building a database, simulating the distribution of specific chemical ion concentrations according to the collected groundwater chemical characteristic parameters, obtaining a simulation map of groundwater specific chemical ion concentrations and a simulation map of total dissolved solid concentrations, and obtaining key areas for karst monitoring, early warning and prevention and control based on the simulation map of groundwater specific chemical ion concentrations and the simulation map of total dissolved solid concentrations. The present invention can more accurately predict areas prone to soil-hole karst collapse based on groundwater chemical field information, saving prevention and control costs.

Description

Technical Field

[0001] The invention relates to the technical field of karst geological disaster prevention and control, and in particular to a method for predicting soil cave type karst collapse prone areas based on groundwater chemical field monitoring. Background Art

[0002] Soil caves are the prototype of karst collapse. Under the influence of human engineering activities, soil caves are often formed and developed into ground collapses very quickly, and some can even be formed within minutes to hours after pumping water. However, under natural conditions, this process may take several years to decades. In the process of studying the formation mechanism of soil caves, groundwater activity is considered to be the main driving force for the formation of soil caves. The rise and fall of groundwater levels and changes in flow velocity and flow rate will produce a variety of physical and mechanical effects and effects such as erosion, vacuum erosion and water hammer, which will cause deformation and damage of the soil, form soil caves, and finally collapse. However, when groundwater interacts with soil, in addition to hydrodynamic mechanical erosion, there are also very complex chemical reactions such as ion exchange, dissolution and corrosion, hydrolysis and hydration, oxidation and reduction, which have a great influence on the strength and deformation of the soil. For example, Ca in the soil 2+ 、Na + , K + and Cl-, HCO 3- The increase in ion concentration has a significant weakening effect on the cohesion of the soil. 2+ ion groundwater flows through the Na-rich + When the soil is clay, the Na + The ions will be Ca 2+ With ion replacement, the porosity and permeability of clay will increase, the strength of the soil will weaken, and it will be easy to deform and destroy to form soil caves. It can be seen that the study of the formation mechanism of soil-cave karst collapse must consider the influence of groundwater chemistry on the basis of hydrodynamic erosion. If water chemical indicators can be added to the evaluation and monitoring of karst collapse susceptibility, the accuracy of karst collapse identification and early warning will be improved.

[0003] Therefore, it is necessary to design a prediction method for soil-cavity karst collapse-prone areas based on groundwater chemical field monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting soil-cavern karst collapse-prone areas based on groundwater chemical field monitoring, which can more accurately predict soil-cavern karst collapse-prone areas based on groundwater chemical field information, thereby saving prevention and control costs.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for predicting soil cave karst collapse prone areas based on groundwater chemical field monitoring includes the following steps:

[0007] Step 1: Select the work area, set up observation holes in the work area, collect the groundwater chemical characteristic parameters in each observation hole, and build a database;

[0008] Step 2: simulate the distribution of specific chemical ion concentrations based on the collected groundwater chemical characteristic parameters to obtain a simulation diagram of groundwater specific chemical ion concentrations and a simulation diagram of total dissolved solid concentrations;

[0009] Step 3: Based on the simulation map of groundwater specific chemical ion concentration and total dissolved solid concentration, key areas for karst monitoring, early warning and prevention are obtained.

[0010] Optionally, in step 1, a working area is selected, and observation holes are arranged in the working area, and groundwater chemical characteristic parameters in each observation hole are collected to construct a database, specifically:

[0011] The work area was selected, and observation holes were arranged in a plum blossom pattern according to the density requirement of 5 observation holes per 4 square kilometers. The groundwater chemical characteristic parameters in each observation hole were collected by a multi-parameter water quality online monitoring instrument. Among them, the groundwater chemical characteristic parameters include sodium ion Na + Potassium ion K + , calcium ion Ca ++ 、Chloride ion Cl - 、Bicarbonate ion HCO3 - and total dissolved solids TDS, and a database is constructed based on the acquired groundwater chemical characteristic parameters.

[0012] Optionally, in step 2, a specific chemical ion concentration distribution simulation is performed according to the collected groundwater chemical characteristic parameters to obtain a groundwater specific chemical ion concentration simulation diagram and a total dissolved solid concentration simulation diagram, which specifically includes the following steps:

[0013] Step 201: grouping and counting the collected groundwater chemical characteristic parameters, including grouping samples and counting random variables of each group of samples, the random variables including ion concentration, total dissolved solids and ion concentration ratio;

[0014] Step 202: Perform a fitting test on the distribution form of the collected groundwater chemical characteristic parameter samples, determine the statistical distribution form and distribution parameters of each random variable, and generate a statistical distribution model;

[0015] Step 203: Generate random numbers that conform to the distribution law of ion concentration and total dissolved solids according to the statistical distribution model, and generate a simulation diagram of groundwater specific chemical ion concentration and a simulation diagram of total dissolved solids concentration based on the random numbers.

[0016] Optionally, in step 3, the key areas for karst monitoring, early warning and prevention are obtained based on the simulation map of groundwater specific chemical ion concentration and the simulation map of total dissolved solid concentration, specifically:

[0017] Based on the simulation diagram of groundwater specific chemical ion concentration and total dissolved solid concentration, the high-risk areas for karst collapse are selected according to the selection principles and used as the key areas for karst monitoring, early warning and prevention. The selection principles include three:

[0018] Areas where the total dissolved solids (TDS) is 30% or higher than the average value;

[0019] The concentration and ratio of sodium, potassium ions and sodium, potassium, and calcium ions, i.e. (Na + +K + ) / (Na + +K + +Ca ++ ), less than or equal to the average value of more than 30% of the area;

[0020] Chloride ion concentration and the concentration and ratio of chloride ions and bicarbonate ions, i.e. Cl - / (Cl - +HCO3 - ), less than or equal to the average value of more than 30% of the area;

[0021] Areas that meet two or more of the three selection principles are high-incidence areas of karst collapse.

[0022] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the method for predicting the prone area of ​​soil cave karst collapse based on groundwater chemical field monitoring provided by the present invention comprises selecting a working area, arranging observation holes in the working area, collecting groundwater chemical characteristic parameters in each observation hole, constructing a database, simulating the distribution of specific chemical ion concentration according to the collected groundwater chemical characteristic parameters, obtaining a simulation diagram of groundwater specific chemical ion concentration and a simulation diagram of total dissolved solid concentration, and obtaining key areas for karst monitoring, early warning and prevention and control based on the simulation diagram of groundwater specific chemical ion concentration and the simulation diagram of total dissolved solid concentration. The method can obtain quantitative evaluation indicators of karst collapse susceptibility by monitoring groundwater chemical field indicator parameters, and can obtain judgment criteria by establishing a corresponding relationship between the distribution characteristics of groundwater specific ion concentration and the existing karst collapse occurrence location, so that the karst collapse susceptibility evaluation indicator system is richer and more complete, with a higher degree of quantification and more accurate prediction, and can realize quantitative prediction of karst collapse susceptibility, providing a premise and basis for accurately judging the occurrence location of karst collapse in the later stage by combining the comprehensive analysis of karst development, structural properties of the covering layer and triggering factors, thereby saving prevention and control costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic flow chart of a method for predicting soil cave karst collapse prone areas based on groundwater chemical field monitoring provided in an embodiment of the present application;

[0024] Figure 2 It is a layout diagram of observation holes in a specific application engineering example of the present invention;

[0025] Figure 3 It is a schematic diagram of groundwater hydrochemistry monitoring in a specific application engineering example of the present invention;

[0026] Figure 4 It is a simulation diagram of groundwater ion concentration simulated by Monte Carlo method in a specific application engineering example of the present invention;

[0027] Figure 5 It is a criterion map for karst collapse susceptibility zoning based on groundwater chemical characteristics in a specific application engineering example of the present invention;

[0028] Figure 6 The target area with high susceptibility to karst collapse is obtained by analyzing a specific application engineering example according to the method of the present invention. DETAILED DESCRIPTION

[0029] The purpose of the present invention is to provide a method for predicting soil-cavern karst collapse-prone areas based on groundwater chemical field monitoring, which can more accurately predict soil-cavern karst collapse-prone areas based on groundwater chemical field information, thereby saving prevention and control costs.

[0030] like Figure 1 As shown, the method for predicting soil cave karst collapse prone areas based on groundwater chemical field monitoring provided by the embodiment of the present invention includes the following steps:

[0031] Step 1: Select the work area, set up observation holes in the work area, collect the groundwater chemical characteristic parameters in each observation hole, and build a database;

[0032] Step 2: simulate the distribution of specific chemical ion concentrations based on the collected groundwater chemical characteristic parameters to obtain a simulation diagram of groundwater specific chemical ion concentrations and a simulation diagram of total dissolved solid concentrations;

[0033] Step 3: Based on the simulation map of groundwater specific chemical ion concentration and total dissolved solid concentration, key areas for karst monitoring, early warning and prevention are obtained.

[0034] In step 1, a work area is selected, and observation holes are arranged in the work area. The groundwater chemical characteristic parameters in each observation hole are collected to build a database, specifically:

[0035] The work area was selected, and observation holes were arranged in a plum blossom pattern according to the density requirement of 5 observation holes per 4 square kilometers. The groundwater chemical characteristic parameters in each observation hole were collected by a multi-parameter water quality online monitoring instrument. Among them, the groundwater chemical characteristic parameters include sodium ion Na + Potassium ion K + , calcium ion Ca ++ 、Chloride ion Cl - 、Bicarbonate ion HCO3 - and total dissolved solids TDS, etc., and build a database based on the acquired groundwater chemical characteristic parameters;

[0036] Among them, groundwater outcrops such as civil wells, machine-made wells, springs, and karst ponds can be used as observation holes. The borehole diameter is determined according to the size of the multi-parameter water quality online monitoring instrument. Under normal circumstances, the opening diameter is 30 mm, the final hole diameter is the same as the opening diameter, the drilling depth is the bedrock surface depth, and the borehole inclination is less than or equal to 1 degree. Dry drilling is required for drilling, and water drilling is prohibited to avoid groundwater pollution due to drilling water. A portable backpack drilling rig can be used.

[0037] In step 2, a specific chemical ion concentration distribution simulation is performed based on the collected groundwater chemical characteristic parameters to obtain a groundwater specific chemical ion concentration simulation map and a total dissolved solid concentration simulation map, which specifically includes the following steps:

[0038] Step 201: grouping and counting the collected groundwater chemical characteristic parameters, including grouping samples and counting random variables of each group of samples, the random variables including ion concentration, total dissolved solids and ion concentration ratio;

[0039] Step 202: Perform a fitting test on the distribution form of the collected groundwater chemical characteristic parameter samples, determine the statistical distribution form and distribution parameters of each random variable, and generate a statistical distribution model;

[0040] Step 203: Generate random numbers that conform to the distribution law of ion concentration and total dissolved solids according to the statistical distribution model, and generate a simulation diagram of groundwater specific chemical ion concentration and a simulation diagram of total dissolved solids concentration based on the random numbers.

[0041] The existing conventional random variable simulation method is used to simulate the concentration of specific chemical ions in groundwater, preferably the Monte Carlo method.

[0042] The specific implementation method of drawing the isodensity map based on the generated groundwater specific chemical ion simulation map is the same as the prior art. The denser the contour lines in the isodensity map, the more specific chemical ions there are in the groundwater in the area and the greater the concentration.

[0043] In step 3, the key areas for karst monitoring, early warning and prevention are obtained based on the simulation map of groundwater specific chemical ion concentration and the simulation map of total dissolved solid concentration, specifically:

[0044] Based on the simulation diagram of groundwater specific chemical ion concentration and total dissolved solid concentration, the high-risk areas for karst collapse are selected according to the selection principles and used as the key areas for karst monitoring, early warning and prevention. The selection principles include three:

[0045] Areas where the total dissolved solids (TDS) is 30% or higher than the average value;

[0046] The concentration and ratio of sodium, potassium ions and sodium, potassium, and calcium ions, i.e. (Na + +K + ) / (Na + +K + +Ca ++ ), less than or equal to the average value of more than 30% of the area;

[0047] Chloride ion concentration and the concentration and ratio of chloride ions and bicarbonate ions, i.e. Cl - / (Cl - +HCO3 - ), less than or equal to the average value of more than 30% of the area;

[0048] Areas that meet two or more of the three selection principles are high-risk areas for karst collapse;

[0049] The value of 30% is determined based on the average value of ion concentration or the statistical value of the karst collapse points that have occurred. The total dissolved solid (TDS) concentration is usually greater than or equal to 200 mg / l, preferably 200-300 mg / l. The sodium and potassium ion concentrations and the ratio of the sodium, potassium and calcium ion concentrations (Na + +K + ) / (Na + +K + +Ca ++ ) is less than or equal to 0.25, preferably 0.20-0.25, the concentration of chloride ions and the concentration and ratio of chloride ions and bicarbonate ions Cl - / (Cl - +HCO3 - ) is less than or equal to 0.1, preferably 0.08-0.1.

[0050] This method uses a multi-parameter water quality online analyzer to obtain specific groundwater hydrochemistry indicators in real time as the susceptibility zoning standard, which makes up for the previous zoning indicators that only perform spatial superposition without considering the interaction between rock, soil and water, making the indicators more quantitative and the prediction more accurate; secondly, it uses groundwater chemical field simulation technology to expand the prediction area through specific ion concentration simulation, and at the same time, it can also use wells, springs and other groundwater outcrops to carry out monitoring, saving drilling costs; thirdly, by establishing a corresponding relationship between the specific ion concentration characteristics of groundwater and the location of karst collapse, it gives a judgment principle for predicting the susceptibility of karst collapse, so that the karst collapse susceptibility zoning based on this method has a basis.

[0051] The present invention provides a specific embodiment: the selected engineering example is the Jinshazhou karst collapse area in Baiyun District, Guangzhou, where many karst collapses have occurred. The site is rectangular, 2 kilometers long, 1 kilometer wide, and 2 square kilometers in area. Based on groundwater chemical field monitoring and simulation, the method recorded in the present invention is used to evaluate the susceptibility of karst collapse and predict the prone area, and compare with the actual karst collapse location to verify its precision and accuracy;

[0052] The specific process is:

[0053] 1. Observation hole layout and drilling. Observation holes were laid out according to the density requirement of 5 observation holes per 1 square kilometer, and a total of 23 observation holes (including available wells and springs) were arranged (such as Figure 2 As shown), the monitoring hole opening diameter is 30mm, the final hole diameter is 30mm, the observation hole is located at the 4 vertices and the center point of the square area, the depth is 10-15m, and the drilling adopts waterless drilling. After the hole is drilled, the PVC casing is first used to support the hole wall, and the PVC flowering tube at the monitoring position is then placed on the PVC flowering tube for real-time monitoring (as shown). Figure 3 (as shown), obtain the groundwater hydrochemical characteristics and establish a database, including sodium, potassium ion concentration, chloride ion concentration, bicarbonate ion concentration and total dissolved solids concentration (TDS);

[0054] 2. Monte Carlo method is used to simulate the distribution of specific ion concentrations in the groundwater chemical field for the groundwater chemical characteristic samples in each observation well in the database, and the concentration map of each specific ion in the groundwater is obtained (such as Figure 4 shown);

[0055] 3. By analyzing the specific ion concentration and ratio of the groundwater hydrochemical field at the karst collapse points (or the locations where the soil caves have been found) in the work area (or the areas with similar geological conditions to the work area), the corresponding relationship between the collapse and the specific ion concentration and ratio is established, and the susceptibility zoning criteria map (such as Figure 5shown);

[0056] 4. Create density maps of specific ion concentrations and different ion concentration ratios for the ion concentration simulation map obtained through simulation (such as Figure 6 As shown in the figure, the susceptibility zoning is then carried out according to the criteria, and the most susceptible area is the target area for karst collapse prediction. The specific principle is: the area with total dissolved solid concentration (TDS) higher than the critical value, (Na + +K + ) / (Na + +K + +Ca ++ ) is below the critical value, Cl- / (Cl-+HCO3 - ) is higher than the critical value. In the specific application engineering examples of the present invention, the critical values ​​are 173.17, 0.25, and 0.09 respectively (such as Figure 5 shown);

[0057] 5. Delineate the target area for karst collapse development according to the principle of karst collapse susceptibility zoning based on groundwater hydrochemical characteristics (e.g. Figure 6 shown).

[0058] In summary, when groundwater interacts with soil, in addition to hydrodynamic mechanical erosion, there are also very complex chemical reactions such as ion exchange, dissolution, hydrolysis and hydration, oxidation and reduction, which have a great influence on the strength and deformation of the soil and are one of the important reasons for the formation of soil holes and eventually leading to collapse. For example, Ca in the soil 2+ 、Na + , K + and Cl - 、HCO3 - The increase in ion concentration has a significant weakening effect on the cohesion of the soil. When groundwater rich in Ca2+ ions flows through groundwater rich in Na + When the soil is clay, the Na + The ions will be Ca 2+Ion replacement will increase the porosity and permeability of clay, weaken the strength of the soil, and make it easy to deform and destroy to form soil caves. It can be seen that the study of the mechanism of soil-cave karst collapse must consider the influence of groundwater chemistry on the basis of hydrodynamic erosion. If water chemical indicators can be added during the evaluation and monitoring of karst collapse susceptibility, the accuracy of karst collapse identification and early warning will be improved. The method of the present invention can simulate the distribution of groundwater specific ion concentrations in a large area by using a groundwater chemical field simulation method based on real-time acquisition of groundwater specific ion physicochemical parameter information in boreholes, wells, and springs. Then, the susceptibility zoning criterion is determined through the specific ion concentration map and the specific ion concentration ratio map of the existing collapse point. Finally, the specific ion concentration map and the specific ion concentration ratio map simulated by on-site monitoring are analyzed according to the criterion to delineate the target area with high karst collapse susceptibility. Through practical verification, more than 90% of the karst collapse points in the working area are located in the target area with high karst collapse risk. This method can reduce the blindness in the process of karst collapse prevention and control and achieve targeted results. At the same time, it can greatly save the drilling workload in the process of karst collapse prediction by utilizing natural observation points such as wells and springs. It also has high prediction accuracy and high promotion and application value.

[0059] The present invention provides a method for predicting areas prone to soil-hole karst collapse based on groundwater chemical field monitoring. The method comprises selecting a working area, arranging observation holes in the working area, collecting groundwater chemical characteristic parameters in each observation hole, constructing a database, simulating the distribution of specific chemical ion concentrations according to the collected groundwater chemical characteristic parameters, obtaining a simulation diagram of groundwater specific chemical ion concentrations and a simulation diagram of total dissolved solid concentrations, and obtaining key areas for karst monitoring, early warning and prevention and control based on the simulation diagram of groundwater specific chemical ion concentrations and the simulation diagram of total dissolved solid concentrations. The method can obtain quantitative evaluation indicators of karst collapse susceptibility by monitoring groundwater chemical field indicator parameters, and can obtain judgment criteria by establishing a corresponding relationship between groundwater specific ion concentration distribution characteristics and existing karst collapse locations, so that the karst collapse susceptibility evaluation indicator system is richer and more complete, with a higher degree of quantification and more accurate prediction, and can achieve quantitative prediction of karst collapse susceptibility, providing a premise and basis for accurately judging the karst collapse location in the later stage by combining karst development conditions, structural property characteristics of the covering layer and comprehensive analysis of triggering factors, thereby saving prevention and control costs.

Claims

1. A method for predicting soil cave karst collapse prone areas based on groundwater chemical field monitoring, characterized in that: The steps include: Step 1: Select the work area, set up observation holes in the work area, collect groundwater chemical characteristic parameters in each observation hole, and build a database; Step 2: simulate the distribution of specific chemical ion concentrations based on the collected groundwater chemical characteristic parameters to obtain a simulation diagram of groundwater specific chemical ion concentrations and a simulation diagram of total dissolved solid concentrations; Step 3: Based on the simulated map of groundwater specific chemical ion concentration and total dissolved solid concentration, key areas for karst monitoring, early warning and prevention are obtained; Among them, in step 1, a working area is selected, and observation holes are arranged in the working area, and the groundwater chemical characteristic parameters in each observation hole are collected to build a database, specifically: The work area was selected, and observation holes were arranged in a plum blossom pattern according to the density requirement of 5 observation holes per 4 square kilometers. The groundwater chemical characteristic parameters in each observation hole were collected by a multi-parameter water quality online monitoring instrument. Among them, the groundwater chemical characteristic parameters include sodium ion Na + Potassium ion K + , calcium ion Ca ++ 、Chloride ion Cl - 、Bicarbonate ion HCO3 - and total dissolved solids TDS, and construct a database based on the acquired groundwater chemical characteristic parameters; In step 3, the key areas for karst monitoring, early warning and prevention are obtained based on the simulation map of groundwater specific chemical ion concentration and the simulation map of total dissolved solid concentration, specifically: Based on the simulation diagram of groundwater specific chemical ion concentration and total dissolved solid concentration, the high-risk areas for karst collapse are selected according to the selection principles and used as the key areas for karst monitoring, early warning and prevention. The selection principles include three: Areas where the total dissolved solids (TDS) is 30% or higher than the average value; The concentration and ratio of sodium, potassium ions and sodium, potassium, and calcium ions, i.e. (Na + +K + ) / (Na + +K + +Ca ++ ), less than or equal to the average value of more than 30% of the area; Chloride ion concentration and the concentration and ratio of chloride ions and bicarbonate ions, i.e. Cl - / (Cl - +HCO3 - ), less than or equal to the average value of more than 30% of the area; Areas that meet two or more of the three selection principles are high-incidence areas of karst collapse.

2. The method for predicting soil cave karst collapse prone areas based on groundwater chemical field monitoring according to claim 1 is characterized in that: In step 2, a specific chemical ion concentration distribution simulation is performed based on the collected groundwater chemical characteristic parameters to obtain a groundwater specific chemical ion concentration simulation map and a total dissolved solid concentration simulation map, which specifically includes the following steps: Step 201: grouping and counting the collected groundwater chemical characteristic parameters, including grouping samples and counting random variables of each group of samples, the random variables including ion concentration, total dissolved solids and ion concentration ratio; Step 202: Perform a fitting test on the distribution form of the collected groundwater chemical characteristic parameter samples, determine the statistical distribution form and distribution parameters of each random variable, and generate a statistical distribution model; Step 203: Generate random numbers that conform to the distribution law of ion concentration and total dissolved solids according to the statistical distribution model, and generate a simulation diagram of groundwater specific chemical ion concentration and a simulation diagram of total dissolved solids concentration based on the random numbers.

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