CN114997524B - Groundwater solute distribution prediction method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a prediction method, a device, equipment and a storage medium for groundwater solute distribution, wherein the method comprises the following steps: collecting the concentration of the groundwater pollutants in a preset time period, and generating solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period; constructing a double-stress stage fractional solute migration model based on solute migration parameters corresponding to the concentration of groundwater pollutants in the preset time period and the duration of the different stress stages; training the dual stress stage fractional solute transport model by utilizing the concentration of the pollutant solute in the preset time period; and collecting the current concentration of the pollutants in the groundwater, and inputting the concentration of the pollutants in the groundwater before training into a trained dual-stress stage fractional order solute transport model to generate groundwater solute distribution. The method realizes the identification of the solute distribution of the groundwater in the target area.
Description
Technical Field
The invention relates to the technical field of solute distribution prediction, in particular to a method, a device, equipment and a storage medium for predicting groundwater solute distribution.
Background
The pollutant solute migration process can be influenced by natural laws and human activities, and in a longer time scale, the severe fluctuation of the groundwater level causes the transient change of hydrodynamic conditions, the groundwater pollutant solute migration laws can correspondingly change, the groundwater system is in a dynamic stable state, and the rate and direction of the pollutant solute migration can change, so that a new equilibrium state is achieved.
Because of the memory of the fractional derivative model, the method is suitable for describing abnormal diffusion phenomenon of pollutant migration under stable flow field conditions, and further, when the original hydraulic balance is broken through by the change of external conditions, how to build a new fractional derivative model to describe groundwater solute distribution becomes a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the defect that the prior art lacks in describing the migration rule of pollutants under the condition of sudden change of groundwater, so as to provide a prediction method, a prediction device, prediction equipment and a storage medium for solute distribution of groundwater.
The embodiment of the invention provides a prediction method for groundwater solute distribution, which comprises the following steps:
Collecting the concentration of the groundwater pollutants in a preset time period, and generating solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period;
constructing a double-stress stage fractional solute migration model based on solute migration parameters corresponding to the concentration of groundwater pollutants in the preset time period and the duration of the different stress stages;
Training the dual stress stage fractional solute transport model by utilizing the concentration of the pollutant solute in the preset time period;
And collecting the current concentration of the pollutants in the groundwater, and inputting the concentration of the pollutants in the groundwater before training into a trained dual-stress stage fractional order solute transport model to generate groundwater solute distribution.
According to the prediction method for the solute distribution of the groundwater, aiming at the groundwater hydrodynamic gradient abrupt change process caused by infiltration of atmospheric precipitation, river water level storm or diversion, the solute migration rules in the two stress stage aquifers are identified through the double-stress stage fractional order solute migration model, the solute distribution of the groundwater in a target area is simulated, and data support is provided for groundwater pollution assessment and restoration.
Optionally, generating solute transport parameters corresponding to different stress stage durations based on the groundwater contaminant concentration within the preset time period includes:
Constructing a solute penetration graph based on groundwater contaminant concentration within the preset time period;
extracting different stress phase durations based on the solute breakthrough graph;
and determining solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period.
According to the method, the concentration of the pollutants in the groundwater in the preset time period is treated, so that the fractional solute migration model in the double stress stage is constructed, and the fractional solute migration model in the double stress stage is more in accordance with the migration rule of the solutes in the groundwater.
Optionally, the extracting the duration of the different stress phases based on the solute penetration graph includes:
Extracting the abrupt time, the initial time and the end time of the concentration of the groundwater pollutants in the solute penetration graph;
Taking the time period from the initial time to the abrupt change time of the concentration of the pollutants in the underground water as a first stress stage duration;
The period of time between the time of the abrupt change of the groundwater contaminant concentration and the end time is taken as the second stress stage duration.
The migration process of the concentration of the groundwater pollutants is clearly and clearly characterized through the solute migration curve chart, and the duration of different stress phases is set more intuitively through the abrupt change time of the concentration of the groundwater pollutants in the solute migration curve chart.
Optionally, the constructing a dual stress stage fractional solute migration model based on solute migration parameters corresponding to the groundwater contaminant concentration and the different stress stage durations in the preset time period includes:
and constructing the double-stress stage fractional solute migration model based on the concentration of the groundwater pollutants in the preset time period, the solute migration parameter of the first stress stage duration and the solute migration parameter of the second stress stage duration.
Optionally, the training the dual stress stage fractional solute transport model using the contaminant solute concentration within the preset time period includes:
Solving the fractional order solute transport model in the double stress stage to generate pollutant solute distribution;
Extracting the pollutant solute concentration at the end time from the groundwater pollutant concentration within the preset time period, and comparing the pollutant solute distribution with the pollutant solute concentration at the end time;
and calibrating solute transport parameters corresponding to the different stress stage durations based on a comparison result, and generating the trained double stress stage fractional solute transport model.
The solute migration parameters are calibrated, so that the influence of external environment changes on the distribution of groundwater solutes can be effectively reflected, and the trained dual-stress stage fractional solute migration model is more in accordance with the pollutant migration rule under the condition of abrupt gradient change of the water power.
In a second aspect of the present application, there is also provided a device for predicting groundwater solute distribution, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the concentration of the groundwater pollutants in a preset time period and generating solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period;
The construction module is used for constructing a double-stress stage fractional solute migration model based on solute migration parameters of the concentration of the groundwater pollutants in the preset time period and the duration of the different stress stages;
The training module is used for training the fractional solute migration model in the double stress stage by utilizing the concentration of the pollutant solute in the preset time period;
the generation module is used for collecting the current concentration of the pollutants in the groundwater, inputting the concentration of the pollutants in the groundwater before the training into the trained double-stress stage fractional solute migration model, and generating the solute distribution of the groundwater.
Optionally, the acquisition module includes:
A constructing submodule for constructing a solute penetration graph based on the concentration of the groundwater pollutants within the preset time period;
an extraction sub-module for extracting different stress phase durations based on the solute penetration graph;
and the determining submodule is used for determining solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period.
Optionally, the extracting submodule includes:
The extraction unit is used for extracting the abrupt time, the initial time and the end time of the concentration of the pollutants in the underground water in the solute penetration curve graph;
A first acquisition unit configured to take a time period from the initial time to a time of abrupt change of the groundwater contaminant concentration as a first stress stage duration;
and the second acquisition unit is used for taking the time period from the abrupt change time of the concentration of the groundwater pollutants to the ending time as a second stress stage duration.
Optionally, the building module includes:
and constructing the double-stress stage fractional solute migration model based on the concentration of the groundwater pollutants in the preset time period, the solute migration parameter of the first stress stage duration and the solute migration parameter of the second stress stage duration.
Optionally, the training module includes:
the solving submodule is used for solving the fractional order solute transport model in the double-stress stage to generate pollutant solute distribution;
a comparison submodule, configured to extract a pollutant solute concentration at an end time from a groundwater pollutant concentration within the preset time period, and compare the pollutant solute distribution with the pollutant solute concentration at the end time;
And the rate stator module is used for calibrating solute transport parameters corresponding to the different stress stage duration based on a comparison result, and generating the trained double stress stage fractional solute transport model.
In a third aspect of the application, a computer device is also presented, comprising a processor and a memory, wherein the memory is for storing a computer program, the computer program comprising a program, the processor being configured to invoke the computer program to perform the method of the first aspect described above.
In a fourth aspect of the application, embodiments of the application provide a computer readable storage medium storing a computer program for execution by a processor to implement the method of the first aspect described above.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting groundwater solute distribution in embodiment 1 of the invention;
FIG. 2 is a flowchart of S101 in embodiment 1 of the present invention;
fig. 3 is a flowchart of step S1012 in embodiment 1 of the present invention;
fig. 4 is a flowchart of step S103 in embodiment 1 of the present invention;
FIG. 5 is a graph showing the solute penetration profile of the water gradient from 0.01 to 0.001 at 20 years in example 1 of the present invention;
FIG. 6 is a graph showing the solute penetration of the water gradient from 0.001 to 0.01 at 200 years in example 1 of the present invention;
FIG. 7 is a schematic block diagram of a prediction apparatus for groundwater solute distribution in accordance with an embodiment 2 of the present invention;
fig. 8 is a schematic block diagram of an acquisition module 71 in embodiment 2 of the present invention;
Fig. 9 is a schematic block diagram of an extraction sub-module 712 in embodiment 2 of the present invention;
fig. 10 is a schematic block diagram of the training module 73 in embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Example 1
The embodiment provides a prediction method for groundwater solute distribution, as shown in fig. 1, comprising the following steps:
s101, collecting the concentration of the groundwater pollutants in a preset time period, and generating solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period.
Wherein, aiming at a target area, carrying out a tracer experiment on the underground water; for example, for two adjacent natural rivers with a dam at the upstream, water exchange exists between the two natural rivers, the type of an aquifer and corresponding hydraulic parameters are measured through historical data collection or on-site investigation, when the water flow of the upstream dam is small, a certain amount of inert tracer (such as sodium bromide) is put in a position close to the shore of one river, the solubility of the tracer is monitored at the position close to the shore of the other river, after a period of time of the tracer is monitored, the upstream dam discharges water, the water level of the river is raised, and experimental data (namely the concentration of pollutants in groundwater in a preset time period) of the inert tracer is continuously monitored, collected and tidied.
Or the field experiment environment is complex and difficult to develop smoothly, the heterogeneous simulation technology can be adopted to simulate the pollutant migration process under the condition of groundwater dynamic gradient abrupt change, the type of the aquifer of the area is obtained by developing large-scale detailed geological monitoring on the target area, the corresponding non-uniform medium structure is generated based on the Markov random field approximation theory through a computer program T-PROGS (geostatistical method of transition probability), the hydraulic parameters of each stratum in different directions (horizontal, oblique and vertical) are quantized through the measurement of the transition probability, the three-dimensional random field of the stratum is generated, the simulation is carried out through a classical Fick diffusion law (Fick diffusion law), and the simulation results (namely, the concentration of the groundwater pollutants in the preset time period) of different groundwater dynamic gradient abrupt change conditions are obtained.
Further, the different stress phase durations adopt a double stress phase duration, namely a first stress phase duration and a second stress phase duration.
S102, constructing a double-stress stage fractional solute migration model based on solute migration parameters of groundwater pollutant concentration and different stress stage durations in the preset time period.
Specifically, the dual stress stage fractional solute migration model is constructed based on the groundwater contaminant concentration within the preset time period, the solute migration parameter of the first stress stage duration, and the solute migration parameter of the second stress stage duration.
Wherein the solute transport parameters include: flow parameters, diffusion parameters, cut-off coefficients and fractional order.
Further, the expression of the dual stress stage fractional order solute transport model is:
In the above formula, x represents a space, T represents a time, C represents a current groundwater pollutant concentration, T 1 represents a sudden change time of the groundwater pollutant concentration, T 2 represents a total duration, [0, T 1 ] represents a first stress stage duration, [ T 1,T2 ] represents a second stress stage duration, [ lambda ] represents a cutoff coefficient, 0.ltoreq.lambda.ltoreq.0.1, alpha represents a fractional order, 0.6.ltoreq.alpha.ltoreq.1 or 0.7.ltoreq.alpha.ltoreq.1, TC represents a cutoff fractional derivative, v represents a flow parameter, 0.ltoreq.v.ltoreq.0.1, D represents a diffusion parameter, 0.ltoreq.D.ltoreq.0.1, A represents an initial concentration of the groundwater pollutant, Representing a boundary position; the subscripts of the truncated coefficient λ, the fractional order α, the flow parameter v, and the diffusion parameter D represent the first stress stage duration and the second stress stage duration, respectively.
Wherein, the calculation formula of the truncated fractional derivative TC is as follows:
where xi represents the functional integral of time t, For the fractional derivative sign, TC is the truncated fractional derivative, λ represents the truncated coefficient, t represents time (i.e., the time of the current calculation), t e [ t 1,t2], t1 ] represents the time when the fractional derivative starts memorization, typically the initial time of each stage, t 2 is the time when the fractional derivative terminates memorization, typically the end time of each stage, e is a natural constant, its value is about 2.718281828459045, Γ () is a single-parameter Gamma function (Gamma function), where the Gamma function is represented as follows:
In the above formula, γ represents a single parameter variable in the gamma function, s γ-1 represents a polynomial increasing function, and e -s represents an exponential decreasing function.
S103, training the fractional solute transport model in the double-stress stage by utilizing the concentration of the pollutant solute in the preset time period.
S104, collecting the current concentration of the pollutants in the groundwater, and inputting the concentration of the pollutants in the groundwater before training into a trained dual-stress stage fractional solute transport model to generate groundwater solute distribution.
Further, pollution evaluation and restoration are carried out on the groundwater in the target area based on the groundwater solute distribution, when the retention effect of the aquifer on the solute in the groundwater solute distribution is strong, the condition that the underground medium clay occupies more space, the solute is difficult to clear, the solute retention degree is longer, and then the solute in the target area is dredged and the like is treated.
According to the prediction method for the solute distribution of the groundwater, aiming at the groundwater hydrodynamic gradient abrupt change process caused by infiltration of atmospheric precipitation, river water level storm or diversion, the solute migration rules in the two stress stage aquifers are identified through the double-stress stage fractional order solute migration model, the solute distribution of the groundwater in a target area is simulated, and data support is provided for groundwater pollution assessment and restoration.
Preferably, as shown in fig. 2, generating solute transport parameters corresponding to different stress phase durations based on the concentration of groundwater pollutants in the preset time period in step S101 includes:
s1011, constructing a solute penetration curve graph based on the concentration of the groundwater pollutants within the preset time period.
And constructing a solute penetration curve graph by taking the acquisition time as an abscissa and the concentration of the groundwater pollutants as an ordinate.
S1012, extracting different stress phase durations based on the solute penetration curve graph.
S1013, determining solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period.
Specifically, by using artificial experience, the range of solute transport parameters is estimated manually according to the solute penetration graph, and then the numerical value of the solute transport parameters is randomly selected in the range as the input data of the model.
Further, the composition ratio of the aquifer clay can be obtained, the range of the initial solute transport parameter can be manually estimated by using human experience based on the composition ratio of the aquifer clay, and then the numerical value of the solute transport parameter is randomly selected in the range to be used as the input data of the model.
According to the method, the concentration of the pollutants in the groundwater in the preset time period is treated, so that the fractional solute migration model in the double stress stage is constructed, and the fractional solute migration model in the double stress stage is more in accordance with the migration rule of the solutes in the groundwater.
Preferably, as shown in fig. 3, step S1012 includes extracting the duration of the different stress phases based on the solute penetration graph, including:
S10121, extracting the abrupt change time, the initial time and the end time of the concentration of the groundwater pollutants in the solute penetration graph.
Specifically, whether the solute penetration curve has double peaks and trailing phenomena is observed, meanwhile, the possible detention process of the solute transport process is roughly judged by combining the clay component proportion of the aquifer, and when the possible detention process of the solute transport process is carried out, the solute penetration curve has double peaks and trailing phenomena, namely the phenomenon that the concentration of the pollutants in the groundwater is suddenly increased or suddenly decreased when the clay of the underground medium is more or is greatly influenced by the outside is caused.
S10122, taking the time period from the initial time to the abrupt change time of the groundwater pollutant concentration as a first stress stage duration.
S10123, regarding a period of time between the abrupt change time of the groundwater pollutant concentration and the ending time as a second stress stage duration.
The migration process of the concentration of the groundwater pollutants is clearly and clearly characterized through the solute migration curve chart, and the duration of different stress phases is set more intuitively through the abrupt change time of the concentration of the groundwater pollutants in the solute migration curve chart.
Preferably, as shown in fig. 4, training the dual stress stage fractional solute transport model using the concentration of the contaminant solute in the preset time period in step S103 includes:
S1031, solving the fractional order solute transport model in the double-stress stage to generate pollutant solute distribution.
Specifically, the time and space in the double-stress stage fractional solute transport model are discretized, and then the double-stress stage fractional solute transport model is solved by using an implicit finite difference method, and the specific calculation process is as follows:
T e [0, T 1 ], the number of points in time is m, the time step is τ=t 1/m, the space step is h, T k+1 represents the k+1st moment, x l represents the l space position,
When k=0:
When k=1:
When k is more than or equal to 1:
in the above equation, j represents a summation function. When k-m=0:
When k-m=1:
when k-m is more than or equal to 1:
s1032, extracting the pollutant solute concentration at the end time from the pollutant concentration of the groundwater within the preset time period, and comparing the pollutant solute distribution with the pollutant solute concentration at the end time.
S1033, calibrating solute transport parameters corresponding to the different stress stage durations based on the comparison result, and generating the trained double stress stage fractional solute transport model.
Specifically, when the contaminant solute distribution is different from the contaminant solute concentration at the end time, calibrating solute migration parameters corresponding to the duration of the different stress phases until the solute concentration distribution is the same as the contaminant solute concentration at the end time, and outputting the trained dual-stress phase fractional solute migration model.
Further, a reasonable range of solute transport parameters corresponding to different stress stage durations of the type of region is analyzed, wherein the solute transport parameters reflect heterogeneity of similar underground aquifers, for example, the effect of the aquifers on the retention of the solutes is strong when the fractional order is low, which indicates that the underground medium clay occupies more space, the solutes are difficult to remove, and the degree of the retention of the solutes is longer.
Further, when the solute concentration distribution is the same as the pollutant solute concentration at the end time, the trained double-stress stage fractional solute migration model is directly output, and prediction simulation is performed on the solute distribution under the groundwater hydrodynamic gradient shock based on the current utilization of the trained double-stress stage fractional solute migration model.
In general, in a longer time scale, the water level of groundwater is affected by changing external environment, so that the water level can be changed drastically, and the transient state of hydrodynamic conditions is caused, and under different hydrodynamic conditions, the retention effect of the aquifer on the solute can be changed, meanwhile, the hydrodynamic shock changes the original balance, the original migration rule of the groundwater pollutant solute can be correspondingly changed, for example, the natural or human activities such as infiltration of atmospheric rainfall, river water level storm or diversion, etc., and the rate and direction of the pollutant solute migration can be changed, so as to reach a new balance state. The fractional derivative model is a black box model, has memory characteristics and is suitable for describing abnormal diffusion phenomenon of pollutant migration under stable flow field conditions. When the original transportation balance is broken through the abrupt change of hydrodynamic conditions caused by the change of external environment, a fractional order model of a new stress stage is required to be established to describe the solute migration process, and the new model is required to be changed in the aspects of flow parameters, diffusion parameters, cutoff coefficients and fractional order, so that the solute migration parameters are required to be calibrated, the influence of the external environment change on the distribution of groundwater solutes can be effectively reflected, and the trained fractional order solute migration model of the dual stress stage is more in accordance with the pollutant migration rule under the condition of the abrupt change of the hydrodynamic gradient.
A method of predicting groundwater solute distribution is described below by way of a specific example.
The data [1,2] of 245 geological monitoring boreholes of an international airport polluted area are selected, the geological area is divided into four categories, clay (volume ratio is 42.4%), silt (17.7%), sand (19.6%), gravels (20.3%), and heterogeneous simulation results are analyzed and simulated for the area to verify the feasibility of the technology.
The solute transport simulation under the sudden change of the groundwater hydrodynamic gradient of the international airport comprises the following steps:
Step 1: aiming at an international airport, generating a corresponding non-uniform medium structure based on a Markov random field approximation theory through a computer program T-PROGS, determining the type of an aquifer through logging data acquired in the field, quantifying hydraulic parameters of each stratum in different directions (horizontal, oblique and vertical) through measuring transition probability, and generating a three-dimensional random field of the stratum; data of 245 geological monitoring boreholes of an international airport contaminated area [1,2] was collected, the geological area was divided into four categories, clay (42.4% by volume), silt (17.7%), sand (19.6%), crushed stone (20.3%). The dimensions of the formation in each direction are derived from data and geologic analysis. The experiment considered the case where the hydraulic gradient was changed from 0.01 to 0.001 at 20 years (as shown in fig. 5) and the hydraulic gradient was changed from 0.001 to 0.01 suddenly at 200 years (as shown in fig. 6), respectively.
Step 2: in the numerical experiment, the hydraulic gradient only changes suddenly once, the clay ratio in the geological region is high, and the retention effect on solute is obvious.
Step 3: according to the analysis result of the step 2, a time pulse cut-off fractional order convection-diffusion equation set (namely a dual-stress stage fractional order solute transport model) applicable to two stress periods of an international airport is established, and is used for describing transient changes of the horizontal hydraulic gradient and describing the migration rule of pollutant solutes, wherein the expression is as follows:
In the above formula, x represents a space, T represents a time, C represents a current groundwater pollutant concentration, T 1 represents a time of abrupt change of the groundwater pollutant concentration, T 2 represents a total duration, [0, T 1 ] represents a first stress stage duration, [ T 1,T2 ] represents a second stress stage duration, [ lambda ] represents a cutoff coefficient, α represents a fractional order, TC represents a fractional derivative of the cutoff, v represents a flow parameter, D represents a diffusion parameter, a represents an initial concentration of the groundwater pollutant, Representing the position along the boundary.
The time-truncated fractional derivative is defined as:
where xi represents the functional integral of time t, For the fractional derivative sign, TC is the truncated fractional derivative, λ represents the truncated coefficient, t represents time (i.e., the time of the current calculation), t e [ t 1,t2], t1 ] represents the time when the fractional derivative starts memorization, typically the initial time of each stage, t 2 is the time when the fractional derivative terminates memorization, typically the end time of each stage, e is a natural constant, its value is about 2.718281828459045, Γ () is a single-parameter Gamma function (Gamma function), where the Gamma function is represented as follows:
In the above formula, γ represents a single parameter variable in the gamma function, s γ-1 represents a polynomial increasing function, and e -s represents an exponential decreasing function.
Step 4: the solute transport parameters were set as shown in the following table:
| penetration curve | α | λ((104day)-1) | v(m/day) | D(m2/day) |
| (a) | [0.7,0.8] | [1.5,0.01] | [3.25,0.2] | [0.18,0.2] |
| (b) | [0.8,0.7] | [0.05,0.12] | [0.2,3.25] | [0.2,0.18] |
As shown in the table above, the hydraulic gradient is 0.01 and 0.7, and the hydraulic gradient is 0.1 and 0.8, which further indicates that the fractional order and the convective diffusion coefficient are directly related to the hydraulic gradient, and the lower the hydraulic gradient, the slower the water flow speed, the slower the diffusion rate of the solute, the retention effect is enhanced, but the retention characteristic attenuation speed is very fast.
Step 5: as can be seen from fig. 5 and 6, the time pulse cut-off fractional derivative model characterizes the mass flux of solute transport (i.e. groundwater contaminant concentration) in two equilibrium states during two stress periods, i.e. sudden increase and sudden decrease of hydraulic gradient level, describing the retention phenomenon of solute transport process in clay-containing mixed component geology for a long time and the process of attenuation with time; by comparing solute transport parameters (as shown in the above table), the increase in the hydraulic gradient accelerates the water flow rate, at which time the tailing of the solute penetration curve is rapidly decayed, while the substantial decrease in the hydraulic gradient in the horizontal or vertical direction slows down the process; the diffusion parameters of solutes are less affected by the flow rate variation, are mainly affected by the direction of diffusion, and are related to the media structure.
Example 2
The embodiment provides a prediction apparatus for groundwater solute distribution, as shown in fig. 7, including:
The collection module 71 is configured to collect the concentration of the groundwater pollutants in a preset period of time, and generate solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset period of time.
Wherein, aiming at a target area, carrying out a tracer experiment on the underground water; for example, for two adjacent natural rivers with a dam at the upstream, water exchange exists between the two natural rivers, the type of an aquifer and corresponding hydraulic parameters are measured through historical data collection or on-site investigation, when the water flow of the upstream dam is small, a certain amount of inert tracer (such as sodium bromide) is put in a position close to the shore of one river, the solubility of the tracer is monitored at the position close to the shore of the other river, after a period of time of the tracer is monitored, the upstream dam discharges water, the water level of the river is raised, and experimental data (namely the concentration of pollutants in groundwater in a preset time period) of the inert tracer is continuously monitored, collected and tidied.
Or the field experiment environment is complex and difficult to develop smoothly, the heterogeneous simulation technology can be adopted to simulate the pollutant migration process under the condition of groundwater dynamic gradient abrupt change, the type of the aquifer of the area is obtained by developing large-scale detailed geological monitoring on the target area, the corresponding non-uniform medium structure is generated based on the Markov random field approximation theory through a computer program T-PROGS (geostatistical method of transition probability), the hydraulic parameters of each stratum in different directions (horizontal, oblique and vertical) are quantized through the measurement of the transition probability, the three-dimensional random field of the stratum is generated, the simulation is carried out through a classical Fick diffusion law (Fick diffusion law), and the simulation results (namely, the concentration of the groundwater pollutants in the preset time period) of different groundwater dynamic gradient abrupt change conditions are obtained.
Further, the different stress phase durations adopt a double stress phase duration, namely a first stress phase duration and a second stress phase duration.
A construction module 72 is configured to construct a dual stress stage fractional solute migration model based on solute migration parameters corresponding to the groundwater contaminant concentration and the different stress stage durations within the predetermined time period.
Specifically, the dual stress stage fractional solute migration model is constructed based on the groundwater contaminant concentration within the preset time period, the solute migration parameter of the first stress stage duration, and the solute migration parameter of the second stress stage duration.
Wherein the solute transport parameters include: flow parameters, diffusion parameters, cut-off coefficients and fractional order.
Further, the expression of the dual stress stage fractional order solute transport model is:
In the above formula, x represents a space, T represents a time, C represents a current groundwater pollutant concentration, T 1 represents a sudden change time of the groundwater pollutant concentration, T 2 represents a total duration, [0, T 1 ] represents a first stress stage duration, [ T 1,T2 ] represents a second stress stage duration, [ lambda ] represents a cutoff coefficient, 0.ltoreq.lambda.ltoreq.0.1, alpha represents a fractional order, 0.6.ltoreq.alpha.ltoreq.1 or 0.7.ltoreq.alpha.ltoreq.1, TC represents a cutoff fractional derivative, v represents a flow parameter, 0.ltoreq.v.ltoreq.0.1, D represents a diffusion parameter, 0.ltoreq.D.ltoreq.0.1, A represents an initial concentration of the groundwater pollutant, Representing a boundary position; the subscripts of the truncated coefficient λ, the fractional order α, the flow parameter v, and the diffusion parameter D represent the first stress stage duration and the second stress stage duration, respectively.
Wherein, the calculation formula of the truncated fractional derivative TC is as follows:
where xi represents the functional integral of time t, For the fractional derivative sign, TC is the truncated fractional derivative, λ represents the truncated coefficient, t represents time (i.e., the time of the current calculation), t e [ t 1,t2], t1 ] represents the time when the fractional derivative starts memorization, typically the initial time of each stage, t 2 is the time when the fractional derivative terminates memorization, typically the end time of each stage, e is a natural constant, its value is about 2.718281828459045, Γ () is a single-parameter Gamma function (Gamma function), where the Gamma function is represented as follows:
In the above formula, γ represents a single parameter variable in the gamma function, s γ-1 represents a polynomial increasing function, and e -s represents an exponential decreasing function.
A training module 73, configured to train the fractional solute transport model in the dual stress stage by using the concentration of the contaminant solute in the preset time period.
The generating module 74 is configured to collect a current groundwater pollutant concentration, input the previous groundwater pollutant concentration into the trained dual stress stage fractional solute migration model, and generate a groundwater solute distribution.
Further, pollution evaluation and restoration are carried out on the groundwater in the target area based on the groundwater solute distribution, when the retention effect of the aquifer on the solute in the groundwater solute distribution is strong, the condition that the underground medium clay occupies more space, the solute is difficult to clear, the solute retention degree is longer, and then the solute in the target area is dredged and the like is treated.
According to the prediction device for the solute distribution of the groundwater, aiming at the groundwater hydrodynamic gradient abrupt change process caused by infiltration of atmospheric precipitation, river water level storm or diversion, the solute migration rules in the two stress stage aquifers are identified through the double-stress stage fractional order solute migration model, the solute distribution of the groundwater in a target area is simulated, and data support is provided for groundwater pollution assessment and restoration.
Preferably, as shown in fig. 8, the acquisition module 71 includes:
a construction sub-module 711 for constructing a solute penetration graph based on groundwater contaminant concentration within the preset time period.
And constructing a solute penetration curve graph by taking the acquisition time as an abscissa and the concentration of the groundwater pollutants as an ordinate.
An extraction sub-module 712 for extracting different stress phase durations based on the solute penetration graph.
A determining submodule 713, configured to determine solute transport parameters corresponding to different stress phase durations based on the concentration of groundwater contaminants within the preset time period.
Specifically, by using artificial experience, the range of solute transport parameters is estimated manually according to the solute penetration graph, and then the numerical value of the solute transport parameters is randomly selected in the range as the input data of the model.
Further, the composition ratio of the aquifer clay can be obtained, the range of the initial solute transport parameter can be manually estimated by using human experience based on the composition ratio of the aquifer clay, and then the numerical value of the solute transport parameter is randomly selected in the range to be used as the input data of the model.
Preferably, as shown in fig. 9, the extracting sub-module 712 includes:
an extracting unit 7121 for extracting a sudden change time, an initial time, and an end time of the concentration of the groundwater contaminant in the solute penetration graph.
Specifically, whether the solute penetration curve has double peaks and trailing phenomena is observed, meanwhile, the possible detention process of the solute transport process is roughly judged by combining the clay component proportion of the aquifer, and when the possible detention process of the solute transport process is carried out, the solute penetration curve has double peaks and trailing phenomena, namely the phenomenon that the concentration of the pollutants in the groundwater is suddenly increased or suddenly decreased when the clay of the underground medium is more or is greatly influenced by the outside is caused.
The first acquiring unit 7122 is configured to take a time period between the initial time and the time of abrupt change of the groundwater contaminant concentration as a first stress stage duration.
A second acquisition unit 7123 for taking a time period between the time of the abrupt change of the groundwater contaminant concentration and the end time as a second stress stage duration.
Preferably, as shown in fig. 10, the training module 73 includes:
the solving submodule 731 is configured to solve the fractional solute transport model in the dual stress stage, and generate a contaminant solute distribution.
Specifically, the time and space in the double-stress stage fractional solute transport model are discretized, and then the double-stress stage fractional solute transport model is solved by using an implicit finite difference method, and the specific calculation process is as follows:
T e [0, T 1 ], the number of points in time is m, the time step is τ=t 1/m, the space step is h, T k+1 represents the k+1st moment, x l represents the l space position,
When k=0:
When k=1:
When k is more than or equal to 1:
in the above equation, j represents a summation function.
When k-m=0:
When k-m=1:
when k-m is more than or equal to 1:
and the comparison submodule 732 is used for extracting the pollutant solute concentration at the end time from the pollutant concentration of the underground water within the preset time period and comparing the pollutant solute distribution with the pollutant solute concentration at the end time.
And the rate stator module 733 is configured to rate solute transport parameters corresponding to the different stress stage durations based on a comparison result, and generate the trained dual stress stage fractional solute transport model.
Specifically, when the contaminant solute distribution is different from the contaminant solute concentration at the end time, calibrating solute migration parameters corresponding to the duration of the different stress phases until the solute concentration distribution is the same as the contaminant solute concentration at the end time, and outputting the trained dual-stress phase fractional solute migration model.
Further, a reasonable range of solute transport parameters corresponding to different stress stage durations of the type of region is analyzed, wherein the solute transport parameters reflect heterogeneity of similar underground aquifers, for example, the effect of the aquifers on the retention of the solutes is strong when the fractional order is low, which indicates that the underground medium clay occupies more space, the solutes are difficult to remove, and the degree of the retention of the solutes is longer.
Further, when the solute concentration distribution is the same as the pollutant solute concentration at the end time, the trained double-stress stage fractional solute migration model is directly output, and prediction simulation is performed on the solute distribution under the groundwater hydrodynamic gradient shock based on the current utilization of the trained double-stress stage fractional solute migration model.
Example 3
The embodiment provides a computer device, which comprises a memory and a processor, wherein the processor is used for reading instructions stored in the memory to execute a prediction method of groundwater solute distribution in any of the above method embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Example 4
The present embodiment provides a computer-readable storage medium storing computer-executable instructions that can perform a method for predicting groundwater solute distribution in any of the above-described method embodiments. Wherein the storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a hard disk (HARD DISK DRIVE, abbreviated as HDD), a Solid state disk (Solid-state-STATE DRIVE, SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (10)
1. The method for predicting the solute distribution of the groundwater is characterized by comprising the following steps:
Collecting the concentration of the groundwater pollutants in a preset time period, and generating solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period;
Constructing a double-stress stage fractional solute migration model based on solute migration parameters corresponding to the concentration of groundwater pollutants in the preset time period and the duration of the different stress stages; the expression of the dual stress stage fractional solute transport model is as follows:
In the above-mentioned method, the step of, The space is represented by a representation of the space,The time is represented by the time period of the day,Indicating the current concentration of groundwater contaminants,Indicating the time of the sudden change in the concentration of the groundwater contaminants,The total duration of time is indicated and,Indicating the duration of the first stress phase,Indicating the duration of the second stress phase,Represents the coefficient of truncation,，The order of the fraction is represented and,Or (b)，Representing the truncated fractional derivative of the value,The flow parameter is indicated as such,，The diffusion parameter is indicated as such,，Indicating the initial concentration of groundwater contaminants,Representing a boundary position; coefficient of cut-offFractional orderFlow parametersAnd diffusion parameterThe subscripts of (a) respectively represent a first stress stage duration and a second stress stage duration;
Training the dual stress stage fractional solute transport model by utilizing the concentration of the pollutant solute in the preset time period;
And collecting the current concentration of the pollutants in the groundwater, and inputting the concentration of the pollutants in the groundwater before training into a trained dual-stress stage fractional order solute transport model to generate groundwater solute distribution.
2. The method according to claim 1, wherein generating solute transport parameters corresponding to different stress phase durations based on groundwater contaminant concentrations within the predetermined time period comprises:
Constructing a solute penetration graph based on groundwater contaminant concentration within the preset time period;
extracting different stress phase durations based on the solute breakthrough graph;
and determining solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period.
3. A method of predicting a groundwater solute distribution according to claim 2 in which the extracting the duration of the different stress phases based on the solute penetration graph comprises:
Extracting the abrupt time, the initial time and the end time of the concentration of the groundwater pollutants in the solute penetration graph;
Taking the time period from the initial time to the abrupt change time of the concentration of the pollutants in the underground water as a first stress stage duration;
The period of time between the time of the abrupt change of the groundwater contaminant concentration and the end time is taken as the second stress stage duration.
4. A method for predicting groundwater solute distribution according to claim 3, wherein said constructing a dual stress stage fractional solute migration model based on solute migration parameters corresponding to the different stress stage durations for groundwater contaminant concentrations within said predetermined time period comprises:
and constructing the double-stress stage fractional solute migration model based on the concentration of the groundwater pollutants in the preset time period, the solute migration parameter of the first stress stage duration and the solute migration parameter of the second stress stage duration.
5. The method of claim 1, wherein training the dual stress stage fractional solute transport model using contaminant solute concentrations over the predetermined time period comprises:
Solving the fractional order solute transport model in the double stress stage to generate pollutant solute distribution;
Extracting the pollutant solute concentration at the end time from the groundwater pollutant concentration within the preset time period, and comparing the pollutant solute distribution with the pollutant solute concentration at the end time;
and calibrating solute transport parameters corresponding to the different stress stage durations based on a comparison result, and generating the trained double stress stage fractional solute transport model.
6. A groundwater solute distribution prediction device, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the concentration of the groundwater pollutants in a preset time period and generating solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period;
The construction module is used for constructing a double-stress stage fractional solute migration model based on solute migration parameters of the concentration of the groundwater pollutants in the preset time period and the duration of the different stress stages; the expression of the dual stress stage fractional solute transport model is as follows:
In the above-mentioned method, the step of, The space is represented by a representation of the space,The time is represented by the time period of the day,Indicating the current concentration of groundwater contaminants,Indicating the time of the sudden change in the concentration of the groundwater contaminants,The total duration of time is indicated and,Indicating the duration of the first stress phase,Indicating the duration of the second stress phase,Represents the coefficient of truncation,，The order of the fraction is represented and,Or (b)，Representing the truncated fractional derivative of the value,The flow parameter is indicated as such,，The diffusion parameter is indicated as such,，Indicating the initial concentration of groundwater contaminants,Representing a boundary position; coefficient of cut-offFractional orderFlow parametersAnd diffusion parameterThe subscripts of (a) respectively represent a first stress stage duration and a second stress stage duration;
The training module is used for training the fractional solute migration model in the double stress stage by utilizing the concentration of the pollutant solute in the preset time period;
the generation module is used for collecting the current concentration of the pollutants in the groundwater, inputting the concentration of the pollutants in the groundwater before the training into the trained double-stress stage fractional solute migration model, and generating the solute distribution of the groundwater.
7. The apparatus according to claim 6, wherein the collection module comprises:
A constructing submodule for constructing a solute penetration graph based on the concentration of the groundwater pollutants within the preset time period;
an extraction sub-module for extracting different stress phase durations based on the solute penetration graph;
and the determining submodule is used for determining solute transport parameters corresponding to different stress stage durations based on the concentration of the groundwater pollutants in the preset time period.
8. The apparatus for predicting groundwater solute distribution of claim 7, wherein said extraction sub-module comprises:
The extraction unit is used for extracting the abrupt time, the initial time and the end time of the concentration of the pollutants in the underground water in the solute penetration curve graph;
A first acquisition unit configured to take a time period from the initial time to a time of abrupt change of the groundwater contaminant concentration as a first stress stage duration;
and the second acquisition unit is used for taking the time period from the abrupt change time of the concentration of the groundwater pollutants to the ending time as a second stress stage duration.
9. A computer device comprising a processor and a memory, wherein the memory is for storing a computer program, the processor being configured to invoke the computer program to perform the steps of the method of any of claims 1-5.
10. A computer readable storage medium having stored thereon computer instructions which when executed by a processor perform the steps of the method according to any of claims 1-5.
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