CN113919141A - Coal mine area storage yard soil heavy metal risk management and control system and migration inversion method - Google Patents
Coal mine area storage yard soil heavy metal risk management and control system and migration inversion method Download PDFInfo
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Abstract
The invention discloses a coal mine area yard soil heavy metal risk management and control system and a migration inversion method, which are suitable for being used for treating mine area soil. The system comprises a hyperspectral remote sensing image inversion soil heavy metal module, a soil heavy metal migration inversion module and a storage yard heavy metal risk management and control module; based on a seepage-concentration field coupling theory, aiming at the soil containing the pollutants, a mathematical model of pollutant migration under the action of a seepage-concentration coupling field is constructed, and the content of heavy metals in the soil is inverted from a hyperspectral remote sensing image by adopting partial least squares regression analysis; the method has simple steps and convenient use, can visualize the heavy metal migration data, and can display the content of the heavy metal in the soil at a certain future moment in the form of different colors in a three-dimensional view under the action of the current condition.
Description
Technical Field
The invention relates to a soil heavy metal risk management and control system and a migration inversion method, in particular to a coal mine area heap soil heavy metal risk management and control system and a migration inversion method which are suitable for being used for mine area soil treatment.
Background
In the aspect of soil heavy metal migration simulation, the release and migration process of chemical components in the solid waste storage yard in the soil is the result of the combined action of a plurality of physical fields. When natural rainfall is replenished to the coal gangue dump, the replenished rainwater or snow water enters from top to bottom to form hydrodynamic action; secondly, in the process of water infiltration, certain components in the coal gangue are dissolved in water and carried away by the water flow, so that mass transfer or transmission action, such as convection dispersion action, is generated; the change of environmental factors such as seasonal change, day and night temperature difference and the like and the thermodynamic effect generated by the radiation heat release of the solid wastes per se; because the variation difference of the surface soil temperature of the storage yard is not large, the research comprehensively considers the influences of hydraulics and mass transfer and combines the research results of predecessors to construct a coupling mathematical model which can quantitatively describe the chemical action of the hydraulic mass transfer in the process of releasing and transferring the multi-component solute of the coal gangue.
The pores in the yard soil in the mine area are the flow channels of water flow, and the pore geometries formed by the pores are not regularly arranged but quite complex, but are basically communicated in all directions, and the flow speed of the water flow in the pores depends on the corresponding soil-water potential and the pore diameter because the yard soil is not uniform. Therefore, when the water flow velocity is researched, the average value of the flow velocity in a soil layer with a certain volume is selected for research. The yard soil can be regarded as a porous medium, and the seepage flow of the pile soil conforms to Darcy's law. Heavy metal elements in the yard soil are released under the action of leaching and the like in the environment, and the concentration of the heavy metal elements is changed by continuously transferring, adsorbing, settling, converting, decomposing and the like in the chemical and physical processes because a large amount of organic or inorganic colloid exists in the yard soil.
The control factors influencing the migration and transformation of pollutants in the porous medium are quite complex and mainly comprise characteristic parameters of the distribution of the pore structure of the medium; a groundwater movement characteristic parameter; convective and hydrodynamic dispersion of contaminants; decay and degradation of contaminants; distributing heat and force in the medium; initial and boundary conditions of contaminant concentration, etc. Pore fluid pressure is a critical factor in controlling the transport process of the contaminants, and its size determines the distribution and containment of contaminant concentrations.
The soil spectrum is the comprehensive reflection of the spectral properties of various substance components in soil, and due to the complexity of the soil components, a mathematical physical model between the content of a certain heavy metal and the characteristic spectral band of the soil is difficult to directly establish. At present, the method for establishing a heavy metal content estimation model based on soil hyperspectrum is generally an empirical statistical method, and mainly comprises Multiple Stepwise Linear Regression (SMLR), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Artificial Neural Network Regression (ANN), Support Vector machine Regression (SVR), Random Forest (RF), Multiple Adaptive Regression (MAR), Adaptive Neural Fuzzy Inference System (ANFIS) diagnostic index and other methods, and the models established by these methods in a specific research area can all reach the standard of quantitative calculation, among them, multivariate stepwise linear regression and partial least squares regression are the most widely used inversion methods at present.
The multiple stepwise linear regression method is a method for screening the optimal spectrum variables participating in model establishment by utilizing a stepwise method to perform multiple linear regression, is suitable for variable modeling of multi-factor influence, and can optimize the regression effect according to the correlation degree among all factors. Firstly, the spectral band which is obviously related to the heavy metal content is found out through correlation analysis to be used as a spectral variable for modeling, then the variable entering the multi-element linear regression is gradually screened according to the obvious level of F statistic, and a coefficient R is determined2And determining the regression model with the maximum error RMSE and the minimum error RMSE as the optimal model for heavy metal element inversion. The partial least squares regression method is a modeling method which integrates the advantages of principal component regression and multiple linear regression, and is suitable for the condition that the number of samples is less or even less than that of variables, and multiple correlations exist between the variablesThe method has the advantages of strong stability, high prediction precision and convenience in quantitative interpretation, and is widely applied to soil heavy metal content inversion research.
The method for estimating the heavy metal content in the soil by the soil spectrum analysis method comprises the steps of utilizing spectrum data of a soil sample actually measured in a laboratory or in the field, carrying out pretreatment such as breakpoint restoration and smoothing, carrying out correlation analysis on an original spectrum and spectrum indexes obtained after conversion such as mathematical conversion and continuum removal of the original spectrum and the measured heavy metal content in the soil, establishing an optimal regression model of the heavy metal content in the soil and a characteristic waveband of the spectrum indexes, and quantitatively inverting the heavy metal content in the soil by utilizing the model.
In the aspect of soil heavy metal risk management and control: in the practical application of soil environment risk control, most scholars usually adopt a single-factor index method to carry out environment risk quantification of a single pollutant and judge the environment risk level of the single pollutant; the environment risk quantification of various pollutants adopts an inner Merle index method and a comprehensive index method, so that the integral level of the soil environment risk under the action of various environment risks can be reflected; and the accumulation index method and the enrichment factor index method are more applied to the identification of the soil environment risk source. In addition, various index evaluation methods are often not used independently, and in the aspect of quantitative evaluation facing various types of environmental risks, several evaluation methods can be selected to be combined according to the research needs and the advantages of the evaluation methods. The research adopts a single-factor index method, and utilizes the characteristic of simple and convenient calculation to carry out risk quantification of a large number of detailed indexes; then, the average value and the maximum value of the single-factor index are fully considered through an inner-Mel exponential method, and the environmental risk under the combined action of various refinement indexes is reflected; and finally, a comprehensive index method is adopted, and the function that the inner Mero index is excessively exaggerated to high-concentration pollutants is avoided by giving weights to the indexes. The method aims to combine the three methods, fully exert the advantages of the various methods and avoid the evaluation result error caused by the limitation of the method.
Disclosure of Invention
Aiming at the technical problems, according to the coal mine area storage yard soil heavy metal risk management and control system and the migration inversion method, the content of the heavy metal in the soil is inverted from the hyperspectral remote sensing image by adopting partial least square regression analysis, and risk management and control are performed by adopting a method combining a single-factor index method, an internal Merlot index method and a comprehensive index method, so that a set of mine area storage yard soil heavy metal inversion system is formed.
In order to achieve the technical purpose, the coal mine area storage yard soil heavy metal risk management and control system comprises a hyperspectral remote sensing image inversion soil heavy metal module, a soil heavy metal migration inversion module and a storage yard heavy metal risk management and control module;
high spectrum remote sensing image inversion soil heavy metal module: the system is used for inverting the content of the heavy metal in the coal mine area storage yard soil to obtain grid data of the content of the heavy metal in the storage yard soil as an initial parameter of a soil heavy metal migration inversion module;
the soil heavy metal migration inversion module: the method is used for inverting the migration process of the heavy metal in the coal mine area storage yard soil, and inverting the migration process of the heavy metal in the soil by inputting initial parameters based on a seepage-concentration field coupling model, so that the content of the heavy metal in the soil in the coal mine area storage yard at a certain time in the future under the action of the current conditions can be obtained and visualized;
the storage yard soil heavy metal risk management and control module: the method is used for carrying out risk assessment on the heavy metals in the coal mine area storage yard soil, carrying out weight proportioning according to the influence degree of different heavy metals on the environment, and evaluating the environmental risk level of the coal mine area storage yard by adopting an internal Mello index method and a comprehensive index method.
A coal mine area storage yard soil heavy metal migration inversion method is based on a seepage-concentration field coupling theory, a mathematical model of pollutant migration under the action of a seepage-concentration coupling field is constructed for soil containing pollutants, and the content of heavy metals in the soil is inverted from a hyperspectral remote sensing image by adopting partial least square regression analysis;
the method comprises the following specific steps:
s1, firstly, performing spectrum preprocessing on hyperspectral data of the storage yard soil of the mining area, then selecting a characteristic waveband with the strongest correlation with the corresponding heavy metal element, inverting the heavy metal content in the soil by utilizing the selection of the quantitative inversion statistical model, judging whether the accuracy of the inverted heavy metal meets the preset requirement, outputting the heavy metal content data in the soil if the accuracy meets the preset requirement, and replacing the characteristic waveband with the strongest correlation with the corresponding heavy metal element to re-invert the heavy metal content in the soil if the accuracy does not meet the preset requirement; obtaining the grid data of the heavy metal content of the storage yard soil as the initial parameters of the soil heavy metal migration inversion module;
s2, acquiring the heavy metal content, the soil volume weight, the saturated water content, the residual water content, the saturated permeability coefficient, the water tightness, the reciprocal alpha of the air inlet value of a van-Genuchten model, the parameter n related to the size of soil pores, the transverse diffusion coefficient, the vertical diffusion coefficient permeability, the average pore flow rate and the parameters of a hysteresis factor in the soil in a solid waste storage yard, determining the initial condition and the boundary condition, then establishing a seepage-concentration coupling field model, predicting and inverting the heavy metal migration process of the soil by using the seepage-concentration coupling field model so as to acquire heavy metal migration inversion result data, visualizing the heavy metal migration data by using a SceneControl control in ArcGIS Engine, and displaying the heavy metal content of the soil at a certain time in the future in a three-dimensional view in different colors under the action of the current condition.
The spectrum preprocessing is carried out by adopting a moving average and median filtering method:
carrying out simple averaging operation on the spectral data in the window spectral band range:
in the formula Ri'，RiRespectively representing the reflectivity values before and after smoothing of the ith point serving as a central point in a specified spectral range, wherein the width of the spectral range is 2k +1 points, averaging is carried out in the specified spectral range, and a smoothing window is shifted backwards point by point, so that high-frequency noise is effectively inhibited, and the signal-to-noise ratio is improved;
suppressing spectral noise by using median filtering; sorting the data values in a window by using a sliding window containing odd data points, and then replacing window center data by using a median value of a sequence, thereby inhibiting spectral noise and enabling a remote sensing image to be smoother;
is provided with a one-dimensional spectral reflectivity data sequence R1,R2,R3,...,RnTaking the window width as 2K +1, performing median filtering on the reflectivity data sequence, and continuously extracting 2K +1 data R from the input data sequencei-k，…，Ri，…，Ri+kThen, the 2K +1 data are sorted according to the magnitude of the numerical value, the data positioned in the middle are taken as the output reflectivity value of the filtering, and the expression is as follows:
Ri'＝Med{Ri-k,···,Ri,···,Ri+k} (2)
in the formula Ri',RiRespectively taking the reflectance values before and after smoothing of the ith point as the central point of the array;
the denoised soil spectrum data is transformed by using a spectrum differential technology and a spectrum logarithm method, so that partial base lines and other background interferences in remote sensing images are eliminated, the spectrum resolution and the sensitivity are improved, and the spectrum resolution and the sensitivity comprise first-order differential and second-order differential, wherein the first-order differential is the slope of each point of an original spectrum, and the change trend of the original spectrum is reflected; the second order differential is the curvature of each point of the original spectrum, is differential processing performed aiming at the first order differential result, and reflects the slope change condition of the first order differential result, and the calculation formula is as follows:
R'(λi)＝[R(λi+1)-R(λi-1)]/2Δλ (3)
wherein, R' (λ)i) And R' (lambda)i) Respectively representing first-order differential values and second-order differential values, representing reflectivity values of the ith waveband, wherein delta lambda is a wavelength interval;
because the reflectivity of the original spectrum in the visible light region is low, the spectrum difference in the visible light region can be enhanced after the logarithmic transformation is carried out on the spectrum reflectivity, and the influence of multiplicative factors caused by the change of illumination conditions can be reduced, so that the 1/log (R) transformation analysis is carried out on the original spectrum.
The selection of the characteristic wave band is specifically as follows:
selecting a wave band corresponding to each heavy metal element with strongest correlation, and setting a dependent variable matrix Y (Y) of the heavy metal contents of n samplesi)n×lAnd n samples with p characteristic bands, where the independent variable matrix X is (X)ij)n×pThe nature of soil heavy metal content inversion still needs to establish a linear regression model of a dependent variable Y and an independent variable X, only introduces a potential function T in form, T is represented by a linear combination of X, and then the aim of predicting a variable matrix Y by the variable matrix X is fulfilled by establishing a unary linear regression model of Y to T; the formula for the partial least squares regression analysis is shown below:
X＝TP'+E (5)
Y＝TQ+F (6)
wherein X and Y are normalized variable matrices; e and F represent residual matrices of X and Y, respectively; p and Q respectively represent a regression coefficient matrix, namely a weight matrix, of the variable matrix X and the variable matrix Y;
starting from the first band, the function T is represented by a linear combination of the argument matrix X:
T＝Xw (7)
where w is the PLS weight vector, modulo equal to 1;
the method comprises the steps that partial least squares regression analysis is utilized to ensure that the correlation between a variable matrix Y and a potential function T is as large as possible, meanwhile, the variance of the potential function T is also as large as possible, and information in X is contained as much as possible, so that when the covariance S of the variable matrix Y and the potential function T is the maximum, Y' T is the optimal model; thereby extracting soil spectral characteristics;
the method comprises the steps of taking the previously extracted soil spectral features as independent variables, taking spectral data as all spectra, taking the spectral features as spectral data of a plurality of wave bands extracted by a partial least squares regression method, taking the content of each heavy metal element in the soil as a dependent variable, and respectively establishing an inversion model according to a formula (5), a formula (6) and a formula (7) so as to obtain the inversion result of the content of the heavy metals in the soil.
The seepage-concentration coupling field model is composed of a seepage field fundamental equation and a concentration field convection-dispersion equation, wherein the seepage field fundamental equation describes the seepage characteristic of rainwater in the solid waste storage yard, the concentration field convection-dispersion equation describes the influence of convection action, dispersion action and diffusion action on the concentration of heavy metals in the soil of the solid waste storage yard, and the convection-dispersion coupling field model is specifically as follows:
wherein the seepage field fundamental equation consists of a seepage motion equation and a continuity equation; the establishment of the basic equation of the seepage field in the solid waste storage yard needs to meet the following conditions:
1) regarding the solid waste storage yard as a pore medium with variable saturation, wherein the pore space is a gap formed between the solid wastes, and the permeability of the solid wastes is not considered;
2) without considering the presence of the possible bubbles and drops of water in the pores and without considering the evaporation of the water therein:
3) the seepage of water inside the yard follows Darcy's law. Based on the three assumptions, the pore space formed by the solid waste particles is occupied by both liquid phase (water) and gas phase (air) (unsaturated) or is occupied by one phase (saturated), and a seepage field fundamental equation can be derived;
based on the conditions, the pore space of the reclamation material in the landfill is occupied by gas and liquid (unsaturated) or by one of them alone (saturated), and after introducing the concept of characterization unit volume (REV), the liquid continuity equation is derived according to the law of mass conservation:
in the formula:
is a water flow velocity vector; t is time; sw is the saturation of water; Φ is the porosity.
Water velocity according to Darcy's law
The following transformations are made:
in the formula: (k)weffective permeability for water; etawIs the kinetic viscosity of water; pwIs the pressure of the water.
For a porous media, if its intrinsic permeability is k, the relative permeability is (k)r)wThat they are associated with an effective permeability (k)wThe relationship between them is as follows:
by bringing formula (10) into formula (9), the percolation rate, which is characterized by the intrinsic permeability and the relative permeability of the material itself, can be obtained:
the above equation is the equation of motion of the seepage field, and the equation of motion is substituted into the equation of continuity of the liquid in equation (8), so that the final equation of control of the seepage field can be obtained:
the right-hand expression of the above formula can be written as:
water content CwThe definition is as follows:
then:
bringing formula (15) into formula (13) gives:
and the above formula is brought into formula (12), so that a differential equation of a seepage field in a saturated medium and a non-saturated medium in a seepage-concentration coupling field model can be obtained:
wherein (k)r)wIs relative permeability, SwIs saturation, CwWater content, theta water content, relative permeability (k)r)wSaturation SwWater content CwThe water content theta or the pressure water head Hp can be expressed; because heavy metals in soil mainly migrate through seepage of rainwater, the van Genuchten model is suitable for solving the water motion parameters of coal gangue and fly ash backfill reclamation materials, and the van-Genuchten model which defines the variables by adopting van Genuchten has the expression that:
in the above formula, m, n and l are parameters related to the pore size of soil, specifically, the parameters are obtained by fitting laboratory soil data, α is the reciprocal of an air intake value and is obtained by fitting a soil-water characteristic curve, and m is 1-1/n, in the equation, when the fluid pressure is atmospheric pressure (i.e., Hp is 0), saturation is achieved, and when the soil is completely saturated, four parameters reach constant values.
In a concentration field, the concentration of heavy metal elements is mainly influenced by convection action, diffusion action and adsorption action, the diffusion action and the diffusion action are called hydrodynamic diffusion action together, and when only the convection action and the hydrodynamic diffusion action are considered, a convection-dispersion equation (also called hydrodynamic dispersion equation) is provided, wherein the equation expression is as follows:
in the formula: u is the average pore flow rate, C is the concentration of heavy metals in the soil, t is the time, DhIs the hydrodynamic dispersion coefficient;
considering the hydrodynamic dispersion coefficient as a constant that does not change with time-space changes, in the saturation case, the above equation can be further written as:
in the formula: dLIs the lateral diffusion coefficient; dTIs the vertical diffusivity.
The parameters to be determined by the seepage-concentration coupling model can be determined by the formula, such as the volume weight rho b of the backfill material and the water content theta, which comprise the saturated water content thetas, the residual water content thetar, the saturated permeability coefficient Ks, the water density rho, the reciprocal alpha of the air inlet value of the van-Genuchten model, the fitting parameter n related to the size of the soil pores, the initial release concentration of each element, the release rate and the transverse and longitudinal dispersion degree of each element, and the parameters can be solved by substituting the parameters into the whole seepage-concentration coupling field model formula (17) and the whole seepage-concentration coupling field formula (23) to obtain the heavy metal content (concentration c) of the soil at a certain future time, namely the migration process of the heavy metal in the soil. And outputting the result into a raster data format, and realizing three-dimensional visualization by using a SceneControl control in the arcgis engine.
Reversely performing risk control by combining a single-factor index method, an internal Merlot index method and a comprehensive index method after the heavy metal in the soil migrates;
and calculating the weight and the reference value of the heavy metal migration inversion result data by adopting an internal Merlot index method, evaluating and grading the soil environment risk of the research area, and guiding a decision maker to give a corresponding risk control strategy and scheme according to the environment risk evaluation result.
Evaluating and grading the soil environment risk of the research area by using an inner Metro index method:
normalizing the concentrations of various heavy metals and the risk indexes of various heavy metals to the environment by adopting an internal Meiro index method, and calculating the normalization index of each index; defining a 'decisive index' to judge whether all the indexes reach the standard; calculating a comprehensive evaluation index Ea through the normalization index and the weight, and determining the soil environment risk level of the evaluated area according to the calculated value of Ea;
inner merle index formula:
Pi＝Ci/Si (25)
in the formula, PSynthesis ofIs the inner Metro contamination index; piIs a single contamination index; ciIs the measured value of the pollutants; siThe evaluation criteria are selected according to the requirements;
is the average value of single pollution indexes; pi maxIs the maximum single contamination index.
And the soil environment risk comprehensive evaluation index is calculated by adopting a comprehensive index method, and the normalization indexes of all primary indexes of an index system are summed with the products of corresponding weights of the normalization indexes. Is calculated by the formula
In the formula, EaFor comprehensive evaluation index, WiIs the weight of the i-th index, Pi synthesisInner merlo pollution index as the ith index;
environmental risk is divided into three levels: and (4) relatively low, early warning and management and control, and determining the environmental risk level of the heavy metal in the yard soil according to the calculated value of Ea and the range of the following table.
Inputting the heavy metal content data at a certain moment, the evaluation standard of heavy metal pollutants and the weight of each heavy metal index, calculating by an inner Merlot index and comprehensive index method, and outputting a risk level result.
Has the advantages that:
according to the method, when the initial value of the heavy metal content of the soil is obtained, on-site sampling analysis is not needed, and only local hyperspectral remote sensing images and a small amount of soil samples are needed to measure parameters such as volume weight, permeability coefficient, saturated water content and dispersivity, so that a large amount of manpower and material resources are saved.
Drawings
Fig. 1 is a schematic diagram of a heavy metal risk management and control system for coal mine yard soil.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in fig. 1, the coal mine area storage yard soil heavy metal risk management and control system of the invention comprises a hyperspectral remote sensing image inversion soil heavy metal module, a soil heavy metal migration inversion module and a storage yard heavy metal risk management and control module;
high spectrum remote sensing image inversion soil heavy metal module: the system is used for inverting the content of the heavy metal in the coal mine area storage yard soil to obtain grid data of the content of the heavy metal in the storage yard soil as an initial parameter of a soil heavy metal migration inversion module;
the soil heavy metal migration inversion module: the method is used for inverting the migration process of the heavy metal in the coal mine area storage yard soil, and inverting the migration process of the heavy metal in the soil by inputting initial parameters based on a seepage-concentration field coupling model, so that the content of the heavy metal in the soil in the coal mine area storage yard at a certain time in the future under the action of the current conditions can be obtained and visualized;
the storage yard soil heavy metal risk management and control module: the method is used for carrying out risk assessment on the heavy metals in the coal mine area storage yard soil, carrying out weight proportioning according to the influence degree of different heavy metals on the environment, and evaluating the environmental risk level of the coal mine area storage yard by adopting an internal Mello index method and a comprehensive index method.
Related functions meeting the requirements of the storage yard of the mining area are designed and realized, and a mining area storage yard GIS system with the functions of inverting the heavy metal of the soil, simulating the migration process of the heavy metal in the soil, visualizing, analyzing, managing data and the like based on hyperspectral data is developed in a mode of carrying out secondary development based on an Arcgis Engine in a C # platform.
ArcGIS Engine is a software development Engine of ArcGIS and can enable programmers to create self-defined GIS desktop programs. In order to quickly construct a GIS application, ArcGIS Engine provides some visual controls, such as drawing controls, 3D controls, framework controls and the like, for developers. The ArcGIS control can establish an application program in two ways, wherein the ArcGIS control can be embedded into the existing application program to enhance the drawing function; second, the ArcGIS control can be used to create a new stand-alone application.
Drawing controls, such as MapControl, pagelayout control, where MapControl is mainly used for displaying and analyzing geographic data, and pagelayout control is used for generating a finished map. MapControl encapsulates Map objects, while PageLayoutControl encapsulates PageLayout objects. Both controls implement the IMxContents interface, so that not only can the map document created by ArcMap be read, but also the map content of itself can be written into a new map document.
Three-dimensional controls, such as GlobeControl, SceneControl, have navigation functionality that allows end users to manipulate three-dimensional views without having to use control commands or custom commands. By setting the navigator attribute, the user can operate the three-dimensional view, such as moving forward, backward, leftward, rightward, zooming in and zooming out, and the like.
Framework controls, such as TOCControl, ToolbarControl, need to be used in conjunction with other controls. For example, a TOCControl control property page is associated with MapControl, and when an image layer is deleted from MapControl, the image layer is also deleted from tocconrol.
The mining area yard soil heavy metal migration inversion method is divided into three modules in total, and the three modules are respectively: the system comprises a hyperspectral remote sensing image inversion soil heavy metal module, a soil heavy metal migration inversion module and a storage yard heavy metal risk management and control module. The technical roadmap is shown in the following figure.
High spectrum remote sensing inversion soil heavy metal module: firstly, the hyperspectral data is subjected to spectrum preprocessing. The spectrum preprocessing comprises spectrum curve smoothing and spectrum transformation. The invention mainly adopts the methods of moving average and median filtering to carry out spectrum pretreatment. The moving average smoothing method is the most common method in the smoothing processing of the spectrum signals, and the principle is simple, namely, the spectrum data in a window with a specified width is simply subjected to average value calculation, and the formula is as follows:
in the formula Ri'，RiThe reflectance values before and after smoothing of the ith point (center point) are respectively. The window width is 2k +1 points, averaging is performed in the window, and the smoothing window is moved backward point by point. The smoothing method can effectively inhibit high-frequency noise and improve the signal-to-noise ratio.
Median filtering is a typical nonlinear signal processing technique based on a ranking statistical theory, and can effectively suppress noise. The basic principle of median filtering: the value of a point in a digital image or sequence of numbers is replaced by the median of the values of the points in a neighborhood of the point, thereby eliminating isolated noise points. The experiment adopts a one-dimensional median filter, and the implementation method comprises the following steps: the data values in the window are sorted by the size of a sliding window containing an odd number of data points, and the window center data is then replaced by the median of the sequence.
Assuming a one-dimensional spectral reflectance data sequence R1,R2,R3,...,RnTaking the window width as 2K +1, performing median filtering on the reflectivity data sequence, and continuously extracting 2K +1 data (R) from the input data sequencei-k，…，Ri，…，Ri+k) Then, the 2K +1 data are sorted according to the magnitude of the value, and the data located in the middle are taken as the output of the filtering. The expression is as follows:
R'i＝Med{Ri-k,···,Ri,···,Ri+k} (2)
r 'in the formula'i,RiThe reflectance values before and after smoothing of the ith point (the center point of the array) are respectively.
The spectral curve of the soil changes more smoothly in the whole spectral interval, the characteristics are not obvious, the characteristics of the soil spectrum can be enhanced by carrying out corresponding data conversion on the spectral data, and the subsequent inversion research is facilitated. The invention adopts the common spectral differentiation technology and the method of logarithm of the spectrum to transform the denoised soil spectral data.
The spectral differentiation technology can eliminate partial base line and other background interference, improve spectral resolution and sensitivity, and is widely applied to spectral analysis. First order differentiation (FD) and Second order differentiation (SD) are commonly used. The first order differential is the slope of each point of the original spectrum, and reflects the change trend of the original spectrum; the second order differential is the curvature of each point of the original spectrum, is differential processing performed aiming at the first order differential result, and reflects the slope change condition of the first order differential result, and the calculation formula is as follows:
R'(λi)＝[R(λi+1)-R(λi-1)]/2Δλ (3)
wherein, R' (λ)i) And R' (lambda)i) Respectively representing first order differential values and second order differential values, representing reflectivity values of the ith waveband, and delta lambda is a wavelength interval.
Since the reflectivity of the original spectrum in the visible light region is low, the spectrum difference in the visible light region can be enhanced and the influence of multiplicative factors caused by the change of illumination conditions can be reduced after the logarithmic transformation is performed on the spectrum reflectivity, so that the 1/log (R) transformation analysis needs to be performed on the original spectrum.
And selecting a characteristic wave band, wherein the characteristic wave band is selected according to the searched documents, the wave band with the strongest correlation with the corresponding heavy metal elements is selected as an input independent variable of a subsequent estimation model, and the system can design an interactive interface for a user to select the inverted heavy metal element types and select the wave band with the strongest correlation.
The basic mathematical principle of partial least squares regression analysis is: the dependent variable matrix Y (Y) provided with the heavy metal contents of n samplesi)n×lAnd n samples with p characteristic bands, where the independent variable matrix X is (X)ij)n×p. The essence of the method is to establish a linear regression model of a dependent variable Y and an independent variable X, only introduce a latent function T in form, wherein T is represented by a linear combination of X, and then realize the purpose of predicting Y by X by establishing a univariate linear regression model of Y to T. The formula for the partial least squares analysis is as follows:
X＝TP'+E (5)
Y＝TQ+F (6)
wherein X and Y are normalized variable matrices; e and F represent residual matrices of X and Y, respectively; p and Q respectively represent regression coefficient matrixes of X and Y, namely weight matrixes.
Starting from the first component, T can be represented by a linear combination of X:
T＝Xw (7)
where w is called the PLS weight vector, its modulus equals 1;
the starting point of the partial least squares regression analysis is to ensure that the correlation between Y and T is as large as possible, and also to make the variance of T as large as possible, so that as much information in X is included as possible. Therefore, the model is the optimal model when the covariance S of Y and T is maximum.
And respectively establishing models by taking the previously extracted soil spectral characteristics as independent variables and the content of each soil heavy metal element as dependent variables to obtain soil heavy metal content inversion results.
The method is programmed by using Matlab, and after the programming is finished, the Matlab Runtime (MCR) is used for packaging to generate a corresponding DLL (delay locked loop) library, and finally the C # language is used for calling in the system.
The soil heavy metal migration inversion module: inverting the soil heavy metal migration process based on the seepage-concentration coupling field model; the seepage field fundamental equation is a general name of seepage motion equation and continuity equation. The establishment of the basic equation of the seepage field in the solid waste storage yard needs to meet the following assumed conditions: 1) regarding the solid waste storage yard as a pore medium with variable saturation, wherein the pore space is a gap formed between the solid wastes, and the permeability of the solid wastes is not considered; 2) without regard to the presence of gas and water droplets in the pores, and without regard to the evaporation of water therefrom; 3) the seepage of water inside the yard follows Darcy's law. Based on the above three assumptions, the pore space formed by solid waste particles should be occupied by both liquid phase (water) and gas phase (air) (unsaturated) or one of the phases (saturated), and the basic equation of the seepage field can be derived.
After introducing the concept of characterizing elementary volumes (REV), the liquid continuity equation is derived according to the law of conservation of mass, with the pore space of the solid waste dump occupied by gas and liquid (unsaturated) or by one of them alone (saturated):
in the formula:
is a water flow velocity vector; t is time; sw is the saturation of water; Φ is the porosity.
Water velocity according to Darcy's law
The following transformations may be made:
in the formula: (k) w is the effective permeability of water; etawIs the kinetic viscosity of water; pw is the pressure of the water.
For porous media, if their intrinsic permeability is k and the relative permeability is (kr) w, then their relationship to the effective permeability (k) w is as follows:
carrying formula 10 into formula 9, the seepage velocity can be obtained as characterized by the intrinsic permeability and the relative permeability of the material itself:
the above equation is the equation of motion of the seepage field, and the equation of motion is substituted into the equation of continuity of the liquid in equation (8), so that the final equation of control of the seepage field can be obtained:
the right-hand expression of the above formula can be written as:
the water capacity Cw is defined as follows:
then:
bringing formula (15) into formula (13) gives:
and bringing the above formula into formula (12), it is possible to obtain:
the above formula is a basic equation of a seepage field in a saturated medium and a non-saturated medium, and is one of differential equations of a convection-concentration coupling field model, and in the equation, the relative permeability (kr) w, the saturation Sw and the water capacity Cw can be expressed by the water content theta or the pressure head Hp. The van-Genuchten model is applicable to the calculation of the moisture motion parameters of coal gangue and fly ash backfill reclamation materials, heavy metals move and migrate in soil through moisture, and the van-Genuchten model is defined by adopting van Genuchten for the variables, and the expression is as follows:
in the above formula, m, n, l and α are obtained by fitting a soil-water characteristic curve, and m is 1-1/n, in the equation, when the fluid pressure is atmospheric pressure (i.e., Hp is 0), saturation is achieved, and when the soil is completely saturated, four parameters reach constant values.
In the concentration field, the concentration of heavy metal elements is mainly influenced by convection, diffusion and adsorption, and the diffusion and diffusion are collectively called hydrodynamic diffusion. According to previous researches, when only the convection action and the hydrodynamic dispersion action are considered, a convection-dispersion equation (also called hydrodynamic dispersion equation) is provided, and the equation expression is as follows:
in the formula: u is the average pore flow rate.
Considering the hydrodynamic dispersion coefficient as a constant that does not change with time-space changes, in the saturation case, the above equation can be further written as:
in the formula: DL is the transverse diffusion coefficient; DT is the vertical diffusivity.
The backfill reclamation material is a porous medium material, solute in the solution can generate physical and chemical actions such as adsorption, desorption or ion exchange and the like on the surface of the material, and when the adsorption-desorption actions are considered, the convection-dispersion equation of the formula is changed into a convection-dispersion-adsorption equation, and the expression is as follows:
the parameters to be determined by the seepage-concentration coupling model can be determined by the formula, including the volume weight rho b of the backfill material, the water content theta (including the saturated water content theta s and the residual water content theta r), the saturated permeability coefficient Ks, the water density rho w, the parameters alpha and n of the van-Genuchten model, the initial release concentration of each element, the release rate and the transverse and longitudinal dispersion degree of each element, the parameters are substituted into the models (formula (17) and formula (24) to be solved, the soil heavy metal content at a certain future moment, namely the migration process of the soil heavy metal is obtained, the concentration of the heavy metal in the soil is c, the result is output into a grid data format, and the SceneControl control in the arcgis engine is used for realizing three-dimensional visualization.
The risk management and control module: the module designs an evaluation method of a soil heavy metal risk management and control index system, evaluates and grades the soil environment risk of a research area by using an internal Mero index method, and guides a decision maker to give corresponding risk management and control countermeasures and schemes according to an environment risk evaluation result.
Normalizing the index values by adopting an inner Mero index method, and calculating the normalized index of each index; defining 'decisive index' to judge whether all the indexes reach the standard; and calculating a comprehensive evaluation index Ea through the normalization index and the weight, and determining the soil environment risk level of the evaluated area according to the calculated value of Ea.
The calculation method of the inner Meiro index is shown in the formula (25) and the formula (26):
Pi＝Ci/Si (25)
in the formula, PSynthesis ofIs the inner Metro contamination index; piIs a single contamination index; ciIs the measured value of the pollutants; siThe evaluation criteria are selected according to the requirements;
is the average value of single pollution indexes; pi maxIs the maximum single contamination index.
And the soil environment risk comprehensive evaluation index is calculated by adopting a comprehensive index method, and the normalization indexes of all primary indexes of an index system are summed with the products of corresponding weights of the normalization indexes. Is calculated by the formula
In the formula, EaFor comprehensive evaluation index, WiIs the weight of the i-th index, Pi synthesisInner merlo pollution index for the ith index.
Environmental risk is divided into three levels: low, early warning and management and control. Determining the environmental risk level of the heavy metals in the yard soil according to the range of the table 1 according to the calculated value of Ea.
TABLE 1 soil environment Risk level comparison Table
Inputting the heavy metal content data at a certain moment, the evaluation standard of heavy metal pollutants and the weight of each heavy metal index, calculating by an inner Merlot index and comprehensive index method, and outputting a risk level result.
Claims (7)
1. The utility model provides a coal mine district store yard soil heavy metal risk management and control system which characterized in that: the system comprises a hyperspectral remote sensing image inversion soil heavy metal module, a soil heavy metal migration inversion module and a storage yard heavy metal risk management and control module;
high spectrum remote sensing image inversion soil heavy metal module: the system is used for inverting the content of the heavy metal in the coal mine area storage yard soil to obtain grid data of the content of the heavy metal in the storage yard soil, and the grid data are used as initial parameters of a soil heavy metal migration inversion module;
the soil heavy metal migration inversion module: the method is used for inverting the migration process of the heavy metal in the coal mine area storage yard soil, and inverting the migration process of the heavy metal in the soil by inputting initial parameters based on a seepage-concentration field coupling model, so that the content of the heavy metal in the soil in the coal mine area storage yard at a certain time in the future under the action of the current conditions can be obtained and visualized;
the storage yard soil heavy metal risk management and control module: the method is used for carrying out risk assessment on the heavy metals in the coal mine area storage yard soil, carrying out weight proportioning according to the influence degree of different heavy metals on the environment, and evaluating the environmental risk level of the coal mine area storage yard by adopting an internal Mello index method and a comprehensive index method.
2. The coal mine area yard soil heavy metal migration inversion method using the coal mine area yard soil heavy metal risk management and control system of claim 1 is characterized by comprising the following steps: based on a seepage-concentration field coupling theory, aiming at the soil containing the pollutants, a mathematical model of pollutant migration under the action of a seepage-concentration coupling field is constructed, and the content of heavy metals in the soil is inverted from a hyperspectral remote sensing image by adopting partial least squares regression analysis;
the method comprises the following specific steps:
s1, firstly, performing spectrum preprocessing on hyperspectral data of the storage yard soil of the mining area, then selecting a characteristic wave band with the strongest correlation with corresponding heavy metal elements, inverting the heavy metal content in the soil by utilizing the selection of a quantitative inversion statistical model, judging whether the accuracy of the inverted heavy metal meets the preset requirement, if so, outputting the heavy metal content data in the soil, and if not, replacing the characteristic wave band with the strongest correlation with the corresponding heavy metal elements to re-invert the heavy metal content in the soil; obtaining the grid data of the heavy metal content of the storage yard soil as the initial parameters of the soil heavy metal migration inversion module;
s2, acquiring the heavy metal content, the soil volume weight, the saturated water content, the residual water content, the saturated permeability coefficient, the water density, the reciprocal alpha of the air inlet value of the van-Genuchten model parameter, the fitting parameter related to the pore size, the transverse diffusion coefficient, the vertical diffusion coefficient permeability, the average pore flow velocity and the parameter of the hysteresis factor in the soil in the solid waste storage yard, determining the initial condition and the boundary condition, then establishing a seepage-concentration coupling field model, predicting and inverting the heavy metal migration process of the soil by using the seepage-concentration coupling field model so as to obtain the heavy metal migration inversion result data, visualizing the heavy metal migration data by using a scene control in ArcGIS Engine, and displaying the heavy metal content of the soil at a certain time in the future in a three-dimensional view in different colors under the action of the current condition.
3. The coal mine area yard soil heavy metal migration inversion method of claim 2, characterized in that the spectrum preprocessing is performed by a moving average and median filtering method:
carrying out simple averaging operation on the spectral data in the window spectral band range:
r 'in the formula'i，RiRespectively representing the reflectivity values before and after smoothing of the ith point serving as a central point in a specified spectral range, wherein the width of the spectral range is 2k +1 points, averaging is carried out in the specified spectral range, and a smoothing window is shifted backwards point by point, so that high-frequency noise is effectively inhibited, and the signal-to-noise ratio is improved;
suppressing spectral noise by using median filtering; sorting the data values in a window by using a sliding window containing odd data points, and then replacing window center data by using a median value of a sequence, thereby inhibiting spectral noise and enabling a remote sensing image to be smoother;
is provided with a one-dimensional spectral reflectivity data sequence R1,R2,R3,...,RnTaking the window width as 2K +1, performing median filtering on the reflectivity data sequence, and continuously extracting 2K +1 data R from the input data sequencei-k，…，Ri，…，Ri+kThen, the 2K +1 data are sorted according to the magnitude of the value, the data positioned in the middle are taken as the output reflectivity value of the filtering, and the expression is as follows:
R'i＝Med{Ri-k,…,Ri,…,Ri+k} (2)
r 'in the formula'i,RiRespectively taking the reflectance values before and after smoothing of the ith point as the central point of the array;
the spectral differentiation technology and the spectrum logarithm method are used for transforming the denoised soil spectral data, so that part of base lines and other background interference in the remote sensing image are eliminated, the spectral resolution and the sensitivity are improved, the spectral resolution and the sensitivity comprise first-order differentiation and second-order differentiation, the first-order differentiation is the slope of each point of the original spectrum, and the change trend of the original spectrum is reflected; the second order differential is the curvature of each point of the original spectrum, is differential processing performed aiming at the first order differential result, and reflects the slope change condition of the first order differential result, and the calculation formula is as follows:
R'(λi)＝[R(λi+1)-R(λi-1)]/2Δλ (3)
wherein, R' (λ)i) And R' (lambda)i) Respectively representing first-order differential values and second-order differential values, representing reflectivity values of the ith waveband, wherein delta lambda is a wavelength interval;
because the reflectivity of the original spectrum in the visible light region is low, the spectrum difference in the visible light region can be enhanced after the logarithmic transformation is carried out on the spectrum reflectivity, and the influence of multiplicative factors caused by the change of illumination conditions can be reduced, so that the 1/log (R) transformation analysis is carried out on the original spectrum.
4. The coal mine area yard soil heavy metal migration inversion method according to claim 2, characterized in that the selection of the characteristic wave band specifically is:
selecting a wave band corresponding to each heavy metal element with strongest correlation, and setting a dependent variable matrix Y (Y) of the heavy metal contents of n samplesi)n×lAnd n samples with p characteristic bands, where the independent variable matrix X is (X)ij)n×pThe nature of inversion of heavy metal content in soil still needs to establish dependent variable Y and selfThe linear regression model of the variable X only introduces a potential function T in form, wherein T is represented by a linear combination of X, and then the purpose of predicting the variable matrix Y by the variable matrix X is realized by establishing a unary linear regression model of Y to T; the formula for the partial least squares regression analysis is as follows:
X＝TP'+E (5)
Y＝TQ+F (6)
wherein X and Y are normalized variable matrices; e and F represent residual matrices of X and Y, respectively; p and Q respectively represent a regression coefficient matrix, namely a weight matrix, of the variable matrix X and the variable matrix Y;
starting from the first band, the function T is represented by a linear combination of the argument matrix X:
T＝Xw (7)
where w is the PLS weight vector, modulo equal to 1;
the method comprises the steps that partial least squares regression analysis is utilized to ensure that the correlation between a variable matrix Y and a potential function T is as large as possible, meanwhile, the variance of the potential function T is also as large as possible, and information in X is contained as much as possible, so that when the covariance S of the variable matrix Y and the potential function T is the maximum Y' T, a model is the optimal model, and soil spectral characteristics are extracted;
and respectively establishing an inversion model according to a formula (5), a formula (6) and a formula (7) by taking the previously extracted soil spectral characteristics as independent variables and the content of each heavy metal element in the soil as dependent variables, thereby obtaining the inversion result of the content of the heavy metal in the soil.
5. The coal mine area yard soil heavy metal migration inversion method of claim 2, characterized in that: the seepage-concentration coupling field model is composed of a seepage field fundamental equation and a concentration field convection-dispersion equation, wherein the seepage field fundamental equation describes the seepage characteristic of rainwater in the solid waste storage yard, the concentration field convection-dispersion equation describes the influence of convection action, dispersion action and diffusion action on the concentration of heavy metals in the soil of the solid waste storage yard, and the convection-dispersion coupling field model is specifically as follows:
wherein the seepage field fundamental equation consists of a seepage motion equation and a continuity equation; the establishment of the basic equation of the seepage field in the solid waste storage yard needs to meet the following conditions:
1) regarding the solid waste storage yard as a pore medium with variable saturation, wherein the pore space is a gap formed between the solid wastes, and the permeability of the solid wastes is not considered;
2) without considering the presence of the possible bubbles and drops of water in the pores and without considering the evaporation of the water therein:
3) the seepage of the moisture in the storage yard conforms to Darcy's law; based on the three assumptions, the pore space formed by the solid waste particles is occupied by both liquid phase (water) and gas phase (air) (unsaturated) or is occupied by one phase (saturated), and a seepage field fundamental equation can be derived;
based on the conditions, the pore space of the reclamation material in the solid waste dump is occupied by gas and liquid (unsaturated) or by either one alone (saturated), and after introducing the concept of characterization unit volume (REV), the liquid continuity equation is derived according to the law of mass conservation:
in the formula:
is a water flow velocity vector; t is time; sw is the saturation of water; phi is the porosity;
water velocity according to Darcy's law
The following transformations are made:
in the formula: (k)weffective permeability for water; etawIs a dynamic adhesive of waterDegree; pwIs the pressure of the water;
for a porous media, if its intrinsic permeability is k, the relative permeability is (k)r)wThat they are associated with an effective permeability (k)wThe relationship between them is as follows:
by bringing formula (10) into formula (9), the percolation rate, which is characterized by the intrinsic permeability and the relative permeability of the material itself, can be obtained:
the above equation is the equation of motion of the seepage field, and the equation of motion is substituted into the equation of continuity of the liquid in equation (8), so that the final equation of control of the seepage field can be obtained:
the right-hand expression of the above formula can be written as:
water content CwThe definition is as follows:
then:
bringing formula (15) into formula (13) gives:
and the above formula is brought into formula (12), so that a differential equation of a seepage field in a saturated medium and a non-saturated medium in a seepage-concentration coupling field model can be obtained:
wherein (k)r)wIs relative permeability, SwIs saturation, CwWater content, theta water content, relative permeability (k)r)wSaturation SwWater content CwThe water content theta or the pressure water head Hp can be expressed; because heavy metals in soil mainly migrate through seepage of rainwater, the van Genuchten model is suitable for solving the water motion parameters of coal gangue and fly ash backfill reclamation materials, and the van-Genuchten model which defines the variables by adopting van Genuchten has the expression that:
in the above formula, the fitting parameters m, n and l related to the pore size of the soil and the reciprocal α of the air inlet value are obtained by fitting a soil-water characteristic curve, and m is 1-1/n, in the equation, when the fluid pressure is atmospheric pressure (i.e. Hp is 0), saturation is achieved, and when the soil is completely saturated, the four parameters reach constant values;
in a concentration field, the concentration of heavy metal elements is mainly influenced by convection action, diffusion action and adsorption action, the diffusion action and the diffusion action are collectively called hydrodynamic diffusion action, and when only the convection action and the hydrodynamic diffusion action are considered, a convection-dispersion equation (also called hydrodynamic dispersion equation) is provided, and the equation expression is as follows:
in the formula: u is the average pore flow rate, C is the concentration of heavy metals in the soil, t is the time, DhIs the hydrodynamic dispersion coefficient;
considering the hydrodynamic dispersion coefficient as a constant that does not change with time-space changes, in the saturation case, the above equation can be further written as:
in the formula: dLIs the lateral diffusion coefficient; dTIs the vertical diffusion coefficient;
the parameters to be determined by the seepage-concentration coupling model can be determined by the formula, such as the volume weight rho b of a backfill material and the water content theta, and comprise the saturated water content thetas, the residual water content thetar, the saturated permeability coefficient Ks, the water density rho, the reciprocal alpha of the parameter air intake value of the van-Genuchten model, a fitting parameter n related to the size of soil pores, the initial release concentration of each element, the release rate and the transverse and longitudinal dispersion degree of each element, and the parameters can be solved by substituting the parameters into the whole seepage-concentration coupling field model formula (17) and the whole seepage-concentration coupling field formula (23) to obtain the heavy metal content (concentration c) of the soil at a certain future time, namely the migration process of the heavy metal in the soil; and outputting the result into a raster data format, and realizing three-dimensional visualization by using a SceneControl control in the arcgis engine.
6. The coal mine area yard soil heavy metal migration inversion method of claim 2, characterized in that after inversion, risk management and control are performed by using a method combining a single-factor index method, an inner-merlo-index method and a comprehensive index method after soil heavy metal migration;
and calculating the weight and the reference value of the heavy metal migration inversion result data by adopting an internal Mero index method, evaluating and grading the soil environment risk of the research area, and guiding a decision maker to give a corresponding risk control strategy and scheme according to the environment risk evaluation result.
7. The coal mine area yard soil heavy metal migration inversion method of claim 6, characterized in that the risk of the soil environment of the research area is evaluated and graded by an inner-Mello index method:
normalizing the concentrations of various heavy metals and the risk indexes of various heavy metals to the environment by adopting an internal Meiro index method, and calculating the normalized indexes of various indexes; defining a 'decisive index' to judge whether all the indexes reach the standard; calculating a comprehensive evaluation index Ea through the normalization index and the weight, and determining the soil environment risk level of the evaluated area according to the calculated value of Ea;
inner merle index formula:
Pi＝Ci/Si (25)
in the formula, PSynthesis ofIs the inner Metro contamination index; piIs a single contamination index; ciIs the measured value of the pollutants; siThe evaluation criteria are selected according to the requirements;
is the average value of single pollution indexes; pimaxIs the maximum single contamination index;
the soil environment risk comprehensive evaluation index is calculated by adopting a comprehensive index method, and the normalization indexes of all primary indexes of an index system are summed with the products of corresponding weights of the normalization indexes; is calculated by the formula
In the formula, EaFor comprehensive evaluation index, WiIs the weight of the i-th index, Pi synthesisInner merlo pollution index as the ith index;
environmental risk is divided into three levels: the method is low, early warning and management control are carried out, and the environmental risk level of the heavy metal in the yard soil is determined according to the calculated value of Ea and the range of the following table;
inputting the heavy metal content data at a certain moment, the evaluation standard of heavy metal pollutants and the weight of each heavy metal index, calculating by an inner Merlot index and comprehensive index method, and outputting a risk level result.
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