CN116956673A - Groundwater flow field model optimization method and system based on multi-parameter probability distribution - Google Patents
Groundwater flow field model optimization method and system based on multi-parameter probability distribution Download PDFInfo
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Abstract
The invention relates to the technical field of underground water flow field simulation, in particular to an underground water flow field model optimization method and system based on multi-parameter probability distribution, which are used for establishing a soil sample hydrogeology multi-parameter distribution model of a target area so as to extract the spatial distribution characteristics of soil sample parameters of the target area; drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation to obtain a multi-parameter combination characteristic value; inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution; and calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and the value range of the confidence interval as the parameter inversion range so as to finally determine the model parameters of the underground water flow field. The invention can provide reference basis for the numerical modeling of the similar underground water flow field, and optimize and improve the numerical simulation effect.
Description
Technical Field
The invention relates to the technical field of underground water flow field simulation, in particular to an underground water flow field model optimization method and system based on multi-parameter probability distribution.
Background
The water resource is basic natural resource and strategic economic resource, is one of the control factors of ecological environment, and is an important component of national comprehensive national force. Aiming at the environmental geology problem in the research area, the in-depth hydrogeology investigation and research is carried out, the scientific means is used for researching the time-space evolution rule of the underground water, the numerical simulation and the scene analysis prediction are carried out on the underground water flow field in the research area, and the method has important significance for realizing the sustainable development of the underground water resource. In recent years, with the rapid development of computer technology, finite difference numerical simulation technology is widely applied to underground water flow field analysis, but aiming at numerical simulation calculation results, multi-parameter analysis is not performed on a soil layer lithology complex region, so that the problems of inaccuracy and incompleteness exist in the underground water flow field simulation calculation process, and the expected simulation effect is further affected.
Disclosure of Invention
Therefore, the invention provides a groundwater flow field model optimization method and system based on multi-parameter probability distribution, which solve the problems of inaccurate and incomplete simulation calculation of the existing groundwater flow field, optimize and improve the numerical simulation effect.
According to the design scheme provided by the invention, the underground water flow field model optimization method based on multi-parameter probability distribution comprises the following steps:
establishing a soil sample hydrogeologic multi-parameter distribution model of a target area so as to extract soil sample parameter space distribution characteristics of the target area, wherein the hydrogeologic multi-parameter comprises a permeability coefficient, a water supply degree and a water storage rate;
drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation to obtain a multi-parameter combination characteristic value;
inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution;
and calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and the value range of the confidence interval as the parameter inversion range so as to finally determine the model parameters of the underground water flow field.
As the optimization method of the underground water flow field model based on the multi-parameter probability distribution, the invention further establishes a hydrogeology multi-parameter distribution model, which comprises the following steps:
firstly, performing field exploration, drilling and layered sampling on cross sections of a target area to obtain a soil sample;
then, carrying out a physical test on the soil sample to obtain various parameter indexes, wherein the physical test at least comprises a particle grading analysis test, a permeability coefficient test, a water supply degree test and a water storage rate test;
and then, determining the property classification of the soil sample according to the grain composition, and establishing a distribution model of the soil body permeability coefficient, the water storage rate and the water supply degree of the target area by combining the soil sample permeability coefficient, the water storage rate and the water supply degree.
As the underground water flow field model optimization method based on multi-parameter probability distribution, in the physical test of the soil sample, a laser particle size analyzer is utilized to assist a rock-soil mineralization analyzer to carry out a particle size analysis test so as to obtain the content percentages of clay particles, particles and sand grains of the soil sample and the mineral composition of the soil sample; and the permeability coefficient of the soil sample is measured by using a constant head permeability test or a variable head permeability test.
As the optimization method of the underground water flow field model based on the multi-parameter probability distribution, the invention further extracts the spatial distribution characteristics of soil sample parameters of a target area, and comprises the following steps:
firstly, determining the partition of each parameter according to a distribution model of three parameters of the permeability coefficient, the water storage rate and the water supply degree;
and then integrating the three parameter distribution models, establishing a probability distribution model, and extracting the spatial distribution characteristics of the soil sample in the target area according to the probability distribution model.
As the optimization method of the underground water flow field model based on the multi-parameter probability distribution, the invention further draws a hydrogeology multi-parameter probability distribution curve, and further comprises the following steps: selecting a continuous distribution density function of each parameter according to the spatial heterogeneity characteristic of each parameter, wherein the continuous distribution density function comprises: a uniform distribution, an exponential distribution, a normal too-distribution, or a t-distribution.
As the optimization method of the underground water flow field model based on the multi-parameter probability distribution, the invention further utilizes finite difference analysis software to calculate the underground water seepage, and comprises the following steps:
firstly, meshing a target area by using finite difference analysis software, splitting a to-be-solved interval into a limited number of grid nodes, constructing a groundwater numerical model of the target area by using a set of the limited number of grid nodes, and establishing a difference equation set by using a difference quotient of node function values;
and then solving the differential equation set by using the initial condition and the boundary condition to obtain the underground water flow field model of the target area.
As the underground water flow field model optimization method based on multi-parameter probability distribution, in the process of solving the differential equation set by utilizing the initial condition and the boundary condition, the differential equation set is solved by utilizing the initial condition and the boundary condition by combining the simulated hydrogeologic condition and the source-sink item characteristic of the target area based on the parameter partition and the value provided by the parameter space random distribution theoretical model.
Further, the invention also provides an underground water flow field model optimization system based on multi-parameter probability distribution, which comprises the following steps: the device comprises a feature extraction module, a feature combination module, a model construction module and a parameter determination module, wherein,
the characteristic extraction module is used for establishing a soil sample hydrogeological multi-parameter distribution model of the target area so as to extract the soil sample parameter spatial distribution characteristics of the target area, wherein the hydrogeological multi-parameter comprises a permeability coefficient, a water supply degree and a water storage rate;
the characteristic combination module is used for drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation to obtain a multi-parameter combination characteristic value;
the model construction module is used for inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution;
and the parameter determining module is used for calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and taking the confidence interval value range as the parameter inversion range so as to finally determine the model parameters of the underground water flow field.
The invention has the beneficial effects that:
aiming at the problems of inaccuracy, incompleteness and the like in the numerical modeling of the underground water flow field in the prior finite difference numerical simulation technology, the invention can establish parameter probability distribution functions such as permeability coefficient, water supply degree, water storage rate and the like based on field and indoor hydrogeologic parameter test results, analyze random distribution characteristics of hydrogeologic parameters of different aquifers, utilize parameter median and confidence interval value ranges, and adopt finite difference analysis software to construct an underground water numerical model, combine finite difference numerical simulation and probability statistics, effectively make up the defects caused by adopting soil parameter suggestion values only, provide corresponding basis and reference for the numerical modeling of similar underground water flow fields, optimize and improve the numerical simulation effect, and have better application prospect.
Description of the drawings:
FIG. 1 is a schematic flow chart of optimizing an underground water flow field model based on multi-parameter probability distribution in an embodiment;
FIG. 2 is an overview of a numerical model of a preset target area according to an embodiment;
FIG. 3 is a graph showing the distribution rule of permeability coefficients in different directions and the function fitting in the embodiment;
FIG. 4 is a schematic illustration of a distribution rule of water supply and function fitting in the embodiment;
FIG. 5 is a schematic diagram of a water storage rate distribution rule and a function fitting in an embodiment;
FIG. 6 is a numerical model illustration of an groundwater flow field in an embodiment;
fig. 7 is a schematic diagram of fitting the measured water level and the simulated water level at the observation point in the embodiment.
The specific embodiment is as follows:
the present invention will be described in further detail with reference to the drawings and the technical scheme, in order to make the objects, technical schemes and advantages of the present invention more apparent.
Aiming at the problems of the prior numerical simulation described in the background art, the embodiment of the invention provides an underground water flow field model optimization method based on multi-parameter probability distribution, which is shown in fig. 1 and comprises the following steps:
s1, establishing a soil sample hydrogeological multi-parameter distribution model of a target area so as to extract soil sample parameter spatial distribution characteristics of the target area, wherein the hydrogeological multi-parameter comprises a permeability coefficient, a water supply degree and a water storage rate.
Specifically, the hydrogeology multiparameter distribution model is established, and can be designed to comprise the following contents:
firstly, performing field exploration, drilling and layered sampling on cross sections of a target area to obtain a soil sample;
then, carrying out a physical test on the soil sample to obtain various parameter indexes, wherein the physical test at least comprises a particle grading analysis test, a permeability coefficient test, a water supply degree test and a water storage rate test;
and then, determining the property classification of the soil sample according to the grain composition, and establishing a distribution model of the soil body permeability coefficient, the water storage rate and the water supply degree of the target area by combining the soil sample permeability coefficient, the water storage rate and the water supply degree.
In the process of sampling the section, a key monitoring section in a target research area can be selected, a drilling machine is used for extracting soil samples from points in the section, the drilling depth can be adjusted according to the soil property condition in the research area, for example, 3*3 is used for extracting the soil samples from the section points in a preset target area, the drilling depth is 12-16 m, and specific numerical values can be adjusted according to the actual sampling condition.
In the physical test of the soil sample, a laser particle size analyzer and an auxiliary rock-soil mineralization analyzer can be utilized to carry out a particle size analysis test so as to obtain the content percentages of clay particles, particles and sand grains of the soil sample and the mineral composition of the soil sample; and the permeability coefficient of the soil sample is measured by using a constant head permeability test or a variable head permeability test.
In order to judge the soil property distribution of the stratum, an MS2000 laser particle size analyzer is utilized to assist an EVO 18 rock-soil mineralization analyzer to develop particle and mineralization analysis tests so as to obtain the content percentages of clay particles, sand grains and the like of the soil sample and the mineral composition of the soil sample. The stratum space distribution can be further divided according to the vertical depth of the elevation and according to the classification standard of the soil in the dike engineering geological survey standard.
The permeability coefficient is an important mechanical index of the soil, and different test methods should be selected for measuring the permeability coefficient of the soil for different soil types. The test types can be classified into a constant head penetration test and a variable head penetration test. The constant water head penetration test is applicable to coarse-grained soil, and the variable water head penetration test is applicable to fine-grained soil. In the embodiment of the scheme, an SLB-1 stress strain control triaxial test penetrometer can be adopted to truly simulate the permeability of soil under a certain confining pressure environment.
Further, extracting the spatial distribution characteristics of the soil sample parameters of the target area can be designed to comprise the following contents:
firstly, determining the partition of each parameter according to a distribution model of three parameters of the permeability coefficient, the water storage rate and the water supply degree;
and then integrating the three parameter distribution models, establishing a probability distribution model, and extracting the spatial distribution characteristics of the soil sample in the target area according to the probability distribution model.
Determining the partition condition of each parameter by determining a distribution model of three parameters of the permeability coefficient, the water supply degree and the water storage rate; data integration is carried out on the distribution models of the three parameters to establish a probability distribution model; and extracting distribution characteristics according to the probability distribution model, integrating the distribution model, and reasonably partitioning the research area to embody the spatial distribution characteristics of soil parameters of the research area.
S2, drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation, and obtaining a multi-parameter combination characteristic value.
Wherein, drawing hydrogeology multiparameter probability distribution curve still contains: selecting a continuous distribution density function of each parameter according to the spatial heterogeneity characteristic of each parameter, wherein the continuous distribution density function comprises: a uniform distribution, an exponential distribution, a normal too-distribution, or a t-distribution.
According to the on-site and indoor test data, respectively carrying out data analysis on parameters such as permeability coefficient, water supply degree, water storage coefficient and the like in a research area, obtaining relevant parameter characteristic values as hydrogeological parameter input values by establishing a probability distribution function, and analyzing the accuracy of a groundwater numerical model based on multi-parameter probability distribution.
Random variables are generally classified into discrete type and continuous type according to their characteristics, and continuous type random variables are mainly used to describe continuously varying geological phenomena such as physical properties parameters of porosity, permeability coefficient, saturation, etc. The soil permeability coefficient, the water supply degree and the water storage rate all have the characteristic of continuous random variables, a corresponding continuous distribution density function can be selected to generate a parameter field, and the common continuous distribution density function has uniform distribution, exponential distribution, normal distribution, t distribution and the like.
The normal distribution is also called Gaussian distribution, is a probability distribution which is very important in the fields of mathematics, physics, engineering and the like, and has great influence on a plurality of aspects of statistics. The normal distribution of mathematical expectation and variance is marked as X-N (mu, sigma) 2 ) The probability density function and the cumulative distribution function are respectively as follows:
the t-distribution is used to estimate the mean of the population with normal distribution and unknown variance from the small samples. If the ensemble variance is known (e.g., when the number of samples is sufficiently large), then the ensemble mean should be estimated with a normal distribution. the t-profile shape is related to the degree of freedom n. Compared with a standard normal distribution curve, the smaller the degree of freedom n is, the flatter the t distribution curve is, the lower the middle of the curve is, and the higher the tail parts of the two sides of the curve are tilted; the larger the degree of freedom n, the closer the t distribution curve is to the normal distribution curve, and when the degree of freedom is, the t distribution curve is the standard normal distribution curve. Assuming that X follows a standard normal distribution and Y follows a chi-square distribution, the distribution is called a t-distribution with a degree of freedom n, and is denoted as. Here, if k mutually independent random variables all follow a standard normal distribution, the sum of squares of the k random variables that follow the standard normal distribution constitutes a new random variable X, whose distribution rule is called chi-square distribution, denoted as where n represents the degree of freedom. the distribution density function of the t distribution is:
here Gam (x) is a gamma function.
The probability density function and the cumulative distribution function of the exponential distribution are respectively:
it can be checked by a single sample K-S test whether the sample is from a specific theoretical distribution. The accumulated frequency distribution of the sample data is compared with a specific theoretical distribution, and if the difference between the accumulated frequency distribution and the specific theoretical distribution is small, the sample is deduced to be taken from a specific distribution. The single sample K-S test is judged to be significant, if the significance is greater than 0.05, then the sample can be considered to be taken from the particular profile taken.
In the embodiment of the scheme, the spatial heterogeneity characteristics of the soil layer permeability coefficient, the water supply degree and the water storage rate of the research area can be found according to the hydrogeological conditions of the research area and by combining the hydrogeological data of the research area and indoor and outdoor test results. Therefore, by statistically analyzing the test results, a relevant parameter probability distribution function can be obtained.
S3, inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating the groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution.
Therein Visual MODFLOW Flex is not merely a graphical user interface for a MODFLOW groundwater simulation. Visual MODFLOW Flex is also industry standard specification software that integrates groundwater flow and contaminant migration, basic analysis and calibration tools, and powerful three-dimensional visualization functions into a single, easy-to-use software environment. Using Visual MODFLOW Flex, the water quality, groundwater make-up and water source protection problem in the local area can be solved using tools embedded in the software. Thus, in the present embodiment, visual MODFLOW Flex can be utilized as finite difference analysis software to build the model.
Specifically, the calculation of groundwater seepage by finite difference analysis software can be designed to include the following:
firstly, meshing a target area by using finite difference analysis software, splitting a to-be-solved interval into a limited number of grid nodes, constructing a groundwater numerical model of the target area by using a set of the limited number of grid nodes, and establishing a difference equation set by using a difference quotient of node function values;
and then solving the differential equation set by using the initial condition and the boundary condition to obtain the underground water flow field model of the target area.
The differential equation set can be solved by utilizing initial conditions and boundary conditions based on parameter partition and value provided by a parameter space random distribution theoretical model and combining simulated hydrogeological conditions and source-sink characteristics of a target area.
And S4, calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and taking the value range of the confidence interval as the parameter inversion range so as to finally determine the model parameters of the underground water flow field.
The method comprises the steps of taking a parameter median as an initial model parameter, taking a confidence interval value range as a parameter inversion range, wherein the confidence interval can be set by adopting a confidence coefficient of 95%, calibrating the model parameter by utilizing a Visual MODFLOW Flex software embedded PEST inversion module, and finally determining the model parameter.
Further, based on the above method, the embodiment of the present invention further provides an optimization system for an underground water flow field model based on a multi-parameter probability distribution, which comprises: the device comprises a feature extraction module, a feature combination module, a model construction module and a parameter determination module, wherein,
the characteristic extraction module is used for establishing a soil sample hydrogeological multi-parameter distribution model of the target area so as to extract the soil sample parameter spatial distribution characteristics of the target area, wherein the hydrogeological multi-parameter comprises a permeability coefficient, a water supply degree and a water storage rate;
the characteristic combination module is used for drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation to obtain a multi-parameter combination characteristic value;
the model construction module is used for inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution;
and the parameter determining module is used for calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and taking the confidence interval value range as the parameter inversion range so as to finally determine the model parameters of the underground water flow field.
To verify the validity of this protocol, the following is further explained in connection with experimental data:
the preset target simulation area is arranged to be positioned on a section 16 of a clear water river reach for key monitoring, a numerical model overview is shown in fig. 2, the ground water type in a field area is fourth series loose layer pore diving, and the ground water in the lower fine sand layer has pressure bearing property and is mainly reserved in sandy loam, light powder loam and fine sand layers; the sandy loam and the fine sand have medium water permeability, the light powder loam has weak-medium water permeability, and the heavy powder loam has weak water permeability and is a relative water-resisting layer. The buried depth of groundwater during exploration is generally 3-5 m. Groundwater has dynamic characteristics, and the amplitude is generally 1-3 m. The underground water in the field is mainly subjected to atmospheric precipitation, lateral runoff and local river reach infiltration and replenishment, and is consumed in evaporation, exploitation, lateral runoff and river drainage. The river water and the underground water in the field are mutually supplemented, when the river water is high, the river water is supplemented to the underground water, and when the river water level is low, the underground water is supplemented to the river water.
Taking the first layer of heavy powder loam as an example, the vertical permeability coefficient accords with the index distribution, the horizontal permeability coefficient accords with the t distribution, the water supply degree also accords with the t distribution, and the water storage rate accords with the index distribution. Since the permeability coefficient, water supply and water storage values all span different orders of magnitude, the log is taken and then statistically analyzed, as shown in fig. 3-5.
Table 1 shows the characteristic parameter values of the probability distribution functions of the permeability coefficients of different soil bodies. Through KS test, the fitting degree of the distribution function is higher, and each soil layer related parameter has obvious distribution characteristics.
Table 1 parameter distribution fitting table
The key parameters of the numerical simulation comprise parameters such as a permeability coefficient, a water supply degree, a water storage coefficient and the like of the aquifer, carrying out parameter probability distribution statistics on soil layers according to test results to obtain probability distribution curves of the permeability coefficients, the water supply degree and the water storage coefficient of different soil layers, and calculating the median of the parameters and the value range of a confidence interval (confidence degree 95%) as shown in a table 2.
The model generalization range is not an independent complete hydrogeological unit, so that numerical simulation is convenient, the horizontal generalized boundary is a west clear water river as a constant water head boundary, the east side is generalized to a given water head boundary by a serialized monitoring water well, and the parts (north side and south side) perpendicular to the constant water head boundary are generalized to a water isolation boundary (namely a zero flow boundary).
In the vertical direction, the water-containing layers in the research area are I, II, III, IV and V water-containing layers, the free water surface of the shallow groundwater water-containing layer is a top plate boundary of groundwater numerical simulation, and through the boundary, the shallow groundwater and the outside of the system are subjected to vertical water volume exchange. The clay layer between the shallow water layer and the deep pressure-bearing water layer is used as a water-proof bottom plate of the model.
The dynamic state of groundwater in the model is mainly influenced by the input of sink source items, and the source sink items in the simulation area have 3 elements of points, lines and planes. The punctiform elements are source sink items such as agricultural exploitation; the linear elements represent mainly River (treated by River module) replenishment items; the planar elements are given by the recharging module and mainly represent the replenishment items such as rainfall infiltration, irrigation infiltration and the like. The other source and sink data such as diving evaporation are brought into model calculation through a Well module, and are processed into exploitation or replenishment wells.
Because of insufficient hydrogeologic data in the research area, the initial flow field cannot be directly obtained. According to the existing elevation data, rainfall data, drainage basin inflow and outflow data and hydrogeological parameter data of each aquifer of a research area, the stable flow in 2022 years is simulated, and a stable flow initial flow field is obtained.
According to the hydrogeologic concept model, hydrogeologic parameters and other boundary conditions are input into the model, and the equation set is solved by utilizing initial conditions and boundary conditions according to the hydrogeologic conditions and source-sink item characteristics of a simulation area and based on a parameter partitioning and value-taking method provided by a soil body parameter space random distribution theoretical model to obtain a numerical model of the underground water flow field of the research area, as shown in fig. 6.
In order to better reflect the advantages of considering the multi-parameter probability distribution model, a groundwater model I and a groundwater model II are respectively built.
The hydrogeologic parameters in the model I adopt a geological investigation recommended value; in the model II, a parameter median is used as an initial model parameter, a confidence interval (confidence coefficient 95%) value range is used as a parameter inversion range, a Visual MODFLOW Flex software embedded PEST inversion module is used for calibrating the model parameter, and the model parameter is finally determined. The processing of the conditions of boundary, source and sink items, time step and the like is unchanged.
And carrying out matching analysis on the simulation results of the model I and the model II and the actual monitoring results of the groundwater under the corresponding working conditions, wherein the corresponding matching correlation fitting degree is shown in figure 7.
Comparing the simulated water level with the observed water level, the maximum difference between the calculated water level and the observed water level of the model I is shown at QSH-3 of 10 months of 2022, the difference is 127cm, the minimum difference is shown at QSH-3 of 24 months of 2023, and the difference is 69cm; model II calculates that the maximum difference between the water level and the observed water level occurs at QSH-3 of 24 days of 10 months of 2022, the difference is 27cm, the difference is reduced by 78.7%, the minimum difference occurs at QSH-2 of 24 days of 12 months of 2022, the difference is 0.9cm, and the difference is reduced by 98.7%.
Standard deviations of the calculated water level of the model I and the observation hole are 1.11, 0.92 and 0.77 respectively, and standard deviations of the corresponding point position of the model II and the actual observation value of the observation hole are 0.96, 0.72 and 0.52 respectively. By considering the multi-parameter random distribution, the standard deviation of the model calculated water level and the actual observation value of the observation hole can be reduced by 0.20 comprehensively.
Therefore, through the data, the scheme can verify that the calculated result of the numerical model distributed randomly is closer to the actual observed value due to the consideration of multiple parameters, the model fitting degree is higher, the expected effect of model simulation can be optimized and perfected, and the scheme has a good application prospect in the field of environmental geological research.
The relative steps, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The elements and method steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or a combination thereof, and the elements and steps of the examples have been generally described in terms of functionality in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Those of ordinary skill in the art may implement the described functionality using different methods for each particular application, but such implementation is not considered to be beyond the scope of the present invention.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the above methods may be performed by a program that instructs associated hardware, and that the program may be stored on a computer readable storage medium, such as: read-only memory, magnetic or optical disk, etc. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits, and accordingly, each module/unit in the above embodiments may be implemented in hardware or may be implemented in a software functional module. The present invention is not limited to any specific form of combination of hardware and software.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The underground water flow field model optimization method based on the multi-parameter probability distribution is characterized by comprising the following steps of:
establishing a soil sample hydrogeologic multi-parameter distribution model of a target area so as to extract soil sample parameter space distribution characteristics of the target area, wherein the hydrogeologic multi-parameter comprises a permeability coefficient, a water supply degree and a water storage rate;
drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation to obtain a multi-parameter combination characteristic value;
inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution;
and calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and the value range of the confidence interval as the parameter inversion range so as to finally determine the model parameters of the underground water flow field.
2. The method for optimizing a groundwater flow field model based on multi-parameter probability distribution according to claim 1, wherein establishing a hydrogeological multi-parameter distribution model comprises:
firstly, performing field exploration, drilling and layered sampling on cross sections of a target area to obtain a soil sample;
then, carrying out a physical test on the soil sample to obtain various parameter indexes, wherein the physical test at least comprises a particle grading analysis test, a permeability coefficient test, a water supply degree test and a water storage rate test;
and then, determining the property classification of the soil sample according to the grain composition, and establishing a distribution model of the soil body permeability coefficient, the water storage rate and the water supply degree of the target area by combining the soil sample permeability coefficient, the water storage rate and the water supply degree.
3. The method for optimizing the groundwater flow field model based on the multi-parameter probability distribution according to claim 2, wherein in the physical test of the soil sample, a laser particle size analyzer is utilized to assist a rock-soil mineralization analyzer to carry out a particle grading analysis test so as to obtain the content percentages of clay particles, particles and sand grains of the soil sample and the mineral composition of the soil sample; and the permeability coefficient of the soil sample is measured by using a constant head permeability test or a variable head permeability test.
4. The groundwater flow field model optimization method based on multi-parameter probability distribution according to claim 1 or 2, wherein extracting the spatial distribution characteristics of the soil sample parameters of the target area comprises:
firstly, determining the partition of each parameter according to a distribution model of three parameters of the permeability coefficient, the water storage rate and the water supply degree;
and then integrating the three parameter distribution models, establishing a probability distribution model, and extracting the spatial distribution characteristics of the soil sample in the target area according to the probability distribution model.
5. The method for optimizing a groundwater flow field model based on multi-parameter probability distribution according to claim 1, wherein drawing a hydrogeological multi-parameter probability distribution curve further comprises: selecting a continuous distribution density function of each parameter according to the spatial heterogeneity characteristic of each parameter, wherein the continuous distribution density function comprises: a uniform distribution, an exponential distribution, a normal too-distribution, or a t-distribution.
6. The method for optimizing a groundwater flow field model based on a multi-parameter probability distribution according to claim 1, wherein calculating groundwater seepage by finite difference analysis software comprises:
firstly, meshing a target area by using finite difference analysis software, splitting a to-be-solved interval into a limited number of grid nodes, constructing a groundwater numerical model of the target area by using a set of the limited number of grid nodes, and establishing a difference equation set by using a difference quotient of node function values;
and then solving the differential equation set by using the initial condition and the boundary condition to obtain the underground water flow field model of the target area.
7. The method for optimizing a groundwater flow field model based on multi-parameter probability distribution according to claim 6, wherein in solving the differential equation set by using the initial condition and the boundary condition, the differential equation set is solved by using the initial condition and the boundary condition by combining the simulated hydrogeological condition and the source-sink item characteristic of the target area based on the parameter partition and the value provided by the parameter space random distribution theoretical model.
8. An underground water flow field model optimization system based on multi-parameter probability distribution, comprising: the device comprises a feature extraction module, a feature combination module, a model construction module and a parameter determination module, wherein,
the characteristic extraction module is used for establishing a soil sample hydrogeological multi-parameter distribution model of the target area so as to extract the soil sample parameter spatial distribution characteristics of the target area, wherein the hydrogeological multi-parameter comprises a permeability coefficient, a water supply degree and a water storage rate;
the characteristic combination module is used for drawing a hydrogeology multi-parameter probability distribution curve, extracting and combining a related parameter probability distribution function, a parameter median confidence interval value range and standard deviation to obtain a multi-parameter combination characteristic value;
the model construction module is used for inputting the multi-parameter combination characteristic value as finite difference analysis software, and calculating groundwater seepage by using the finite difference analysis software to obtain a groundwater flow field model under multi-parameter probability distribution;
and the parameter determining module is used for calibrating the model parameters by taking the median of the parameters as the initial parameters of the model and taking the confidence interval value range as the parameter inversion range so as to finally determine the model parameters of the underground water flow field.
9. An electronic device comprising a memory and a processor, said processor and said memory completing communication with each other via a bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1-7.
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| CN117330941B (en) * | 2023-11-29 | 2024-02-02 | 珠海智锐科技有限公司 | Performance test method and related device for new energy BMS printed circuit board |
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