A fault soil radon concentration background value modeling and correction method and system

Abstract

The application relates to the technical field of soil radon migration, and particularly discloses a fault soil radon concentration background value modeling and correction method and system. The method comprises the following steps: constructing a water-gas two-phase migration model, dividing a cover layer into a gas-phase dominated unsaturated zone and a water-phase dominated saturated zone, and respectively establishing control equations; establishing a gas-liquid interface transmission flux formula to form mass conservation and thermodynamic equilibrium boundary conditions; obtaining real-time water table dynamic data, dynamically adjusting the vertical ranges of the gas-phase zone and the liquid-phase zone, and calculating dynamic soil radon concentration background values changing with time; constructing a target function, optimizing model parameters to be inverted by using the difference between synchronously obtained soil radon concentration measured data and model calculation values, and obtaining optimal parameter combinations. The background value after dynamic correction can effectively filter out radon concentration fluctuations caused by water level changes, so that abnormal signals caused by real tectonic activities or earthquake precursors are more prominent.

Description

Technical Field

[0001] This invention relates to the field of soil radon migration technology, specifically a method and system for modeling and correcting background values ​​of radon concentration in fault-line soil. Background Technology

[0002] Fault-induced soil gas (including radon) observation is an important means of detecting active faults and monitoring earthquake precursors. Its basic principle is that deep gases migrate upwards along weak channels such as fault zones, forming geochemical anomalies on the surface. The concentration distribution of soil radon is controlled by its migration patterns, which are heavily influenced by medium properties (porosity, permeability) and environmental factors (gas pressure, temperature, humidity). In existing technologies, numerical simulations of soil radon migration are mostly based on classical diffusion-convection theory. The most representative is the physical model proposed by Abdoh and Pilkington (1989), which treats the overburden as a homogeneous, isotropic porous medium, considering only diffusion and convection in the gas phase, and derives the corresponding two-dimensional concentration equation. Subsequent studies have largely improved upon this model; for example, Liu Jinghua (2006) and Wu Jianbo et al. (2014) introduced layered modeling of secondary fractures and inhomogeneous media to explain more complex anomaly morphologies.

[0003] However, all these existing models share a fundamental limitation: they fail to consider the significant impact of the groundwater level (i.e., the saturation zone interface of groundwater) and its fluctuations on radon migration. In areas with high groundwater levels, the lower part of the overburden is saturated with water, and the radon migration environment changes from a single gaseous phase to a two-phase coexistence of water and gas. Radon not only diffuses and convections in the gaseous phase but also migrates in the aqueous phase and exchanges with the gas-liquid interface. This process follows physical laws completely different from those of a single gaseous phase. Existing models ignore this process, directly simplifying it to a purely gaseous phase problem, which inevitably introduces huge errors in areas of groundwater level variation. This results in calculated background values ​​that fail to accurately reflect geological structural information, instead containing a large amount of environmental noise, thus depriving subsequent anomaly identification and seismic analysis of a reliable data foundation.

[0004] The shortcomings of existing technologies are mainly reflected in the incompleteness of their models and the limitations of their applicable scenarios. Specifically:

[0005] Model assumption flaws: Existing models (such as the Abdoh model) assume that the medium is completely filled with gas, or simply regard the water immersion zone as a migration barrier, which does not conform to the actual physical scenario where the pores below the water table are filled with water and the above is a coexistence of gas and water.

[0006] Key physical processes are ignored: the dissolution, diffusion, and convection of radon in the aqueous phase, as well as the most crucial mass exchange between the gas and liquid phases, are completely overlooked. This exchange process is the bridge connecting radon in the aqueous phase and radon in the gas phase, and its flux directly affects the radon concentration measured at the Earth's surface.

[0007] Static background value: The background value calculated by existing methods is a static value and cannot respond to dynamic changes in groundwater level (such as seasonal fluctuations and the impact of rainfall infiltration). Water level changes directly change the migration path and efficiency of radon, causing the background value to drift and rendering anomaly detection methods based on fixed thresholds ineffective. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for modeling and correcting background values ​​of radon concentration in fault soil, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for modeling and correcting background values ​​of radon concentration in fault-prone soils, the method comprising:

[0011] A water-gas two-phase migration model was constructed, with the dynamically changing water level interface as the boundary, and the overburden was vertically divided into a gas-dominated unsaturated zone and a water-dominated saturated zone. Control equations describing the migration behavior of radon in the gas and liquid phases were established respectively.

[0012] A formula for the transport flux at the gas-liquid interface is established, and based on this flux, the governing equations of the gas phase region and the liquid phase region are coupled to form the mass conservation and thermodynamic equilibrium boundary conditions at the gas-liquid interface.

[0013] Real-time dynamic data of groundwater level is obtained and used as model input. The vertical range of the gas phase zone and liquid phase zone is dynamically adjusted. The dynamic background value of soil radon concentration over time is calculated by numerically solving the coupled control equations.

[0014] An objective function is constructed, and the model parameters to be inverted are optimized by utilizing the difference between the synchronously acquired measured soil radon concentration data and the model calculation values ​​to obtain the optimal parameter combination.

[0015] As a further embodiment of the present invention, the governing equations for both the gas phase region and the liquid phase region are one-dimensional diffusion-convection-decay equations in the vertical direction, wherein the governing equation for the gas phase region is: ;

[0016] Where: C g D represents the radon concentration in the pores. g v is the effective diffusion coefficient of radon in the porous gas phase; g λ is the convection velocity in the gas phase; λ is the decay constant of radon; A g The ejection rate of the medium in the gas phase region;

[0017] The governing equations for the liquid phase region are:

[0018] ;

[0019] Among them, C w D represents the concentration of dissolved radon gas in pore water. w v is the effective diffusion coefficient of radon in water; w A represents the seepage velocity in the aqueous phase. w The ejection rate of the medium in the liquid phase region.

[0020] As a further embodiment of the present invention, the gas-liquid interface transport flux is expressed as:

[0021] ;

[0022] At the gas-liquid interface y=L(t), the coupled boundary condition is:

[0023] ;

[0024] ;

[0025] in: denoted as radon transport flux at the gas-liquid interface; k is the interfacial transport rate; α is the equilibrium distribution coefficient of radon in the aqueous and gas phases; C w / α represents the concentration C in water. w The equilibrium hypothetical gas phase concentration, L(t) is the dynamic water level.

[0026] As a further aspect of the present invention, the step of dynamically adjusting the vertical range between the gas phase region and the liquid phase region specifically includes:

[0027] Time-series groundwater level data and corresponding measured values ​​of soil radon concentration were obtained through monitoring wells;

[0028] For each point in time, the current diving level is used as the interface to re-divide the gas phase region and the liquid phase region;

[0029] The finite difference method was used to numerically solve the governing equations and interface conditions, and the gas concentration at the surface was obtained as the background value for model calculation at that moment.

[0030] The calculation results at all times are combined to obtain the dynamic background value curve, and the corrected residual sequence is calculated.

[0031] As a further aspect of the present invention, the specific method for parameter inversion and optimization is as follows:

[0032] Construct the objective function in least squares form:

[0033] ;

[0034] Among them, C calc(t i ;P) represents the surface radon concentration calculated by the model under a given parameter vector P; C means (t i ) is at time t i Soil radon concentration measured by field instruments;

[0035] The Levenberg-Marquardt algorithm is used to iteratively optimize the objective function. The optimal parameter combination is obtained through steps such as forward model calculation, Jacobian matrix construction, incremental equation solving, parameter updating and convergence judgment.

[0036] As a further embodiment of the present invention, the iterative process of the Levenberg-Marquardt algorithm includes:

[0037] Initialize the guessed values ​​and damping factors, and run the forward model to calculate the residual vector;

[0038] The Jacobian matrix is ​​approximated by the finite difference method, and the incremental equation is solved.

[0039] Update the parameters and adjust the damping factor according to the changes in the objective function, repeating the iteration until the convergence condition is met.

[0040] This invention also provides a system for modeling and correcting background values ​​of radon concentration in fault soil, the system comprising:

[0041] The equation building module is used to construct a water-gas two-phase migration model. Using the dynamically changing water level interface as the boundary, the overburden is vertically divided into a gas-dominated unsaturated zone and a water-dominated saturated zone, and control equations describing the migration behavior of radon in the gas and liquid phases are established respectively.

[0042] The boundary constraint module is used to establish the gas-liquid interface transport flux formula, and based on this flux, couple the gas phase region control equation and the liquid phase region control equation to form the mass conservation and thermodynamic equilibrium boundary conditions at the gas-liquid interface.

[0043] The numerical solution module is used to acquire real-time dynamic data of groundwater level, use it as model input, dynamically adjust the vertical range of the gas phase zone and liquid phase zone, and calculate the dynamic background value of soil radon concentration over time by numerically solving the coupled control equations.

[0044] The inversion optimization module is used to construct the objective function. It optimizes the model parameters to be inverted by utilizing the difference between the synchronously acquired measured soil radon concentration data and the model calculation values, and obtains the optimal parameter combination.

[0045] Compared with the prior art, the beneficial effects of the present invention are: the dynamically corrected background value can effectively filter out the radon concentration fluctuations (environmental noise) caused by water level changes, making the abnormal signals caused by real tectonic activity or earthquake precursors more prominent, significantly improving the signal-to-noise ratio, and greatly improving the accuracy and reliability of anomaly identification.

[0046] The parameter inversion process can obtain important medium characteristic parameters that are difficult to measure directly, such as the interface transport rate k. These parameters are of great scientific value for understanding the transport mechanism of radon.

[0047] By introducing water-air two-phase migration and interface exchange mechanisms, the inherent limitations of traditional models in high groundwater levels are overcome. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0049] Figure 1 The flowchart illustrates a method for modeling and correcting background radon concentration in fault soil, as provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0051] In this embodiment of the invention, a method for modeling and correcting background values ​​of radon concentration in fault soil is provided, the method comprising:

[0052] A water-gas two-phase migration model was constructed, with the dynamically changing water level interface as the boundary, and the overburden was vertically divided into a gas-dominated unsaturated zone and a water-dominated saturated zone. Control equations describing the migration behavior of radon in the gas and liquid phases were established respectively.

[0053] A formula for the transport flux at the gas-liquid interface is established, and based on this flux, the governing equations of the gas phase region and the liquid phase region are coupled to form the mass conservation and thermodynamic equilibrium boundary conditions at the gas-liquid interface.

[0054] Real-time dynamic data of groundwater level is obtained and used as model input. The vertical range of the gas phase zone and liquid phase zone is dynamically adjusted. The dynamic background value of soil radon concentration over time is calculated by numerically solving the coupled control equations.

[0055] Construct the objective function, and optimize the model parameters to be inverted by using the difference between the measured soil radon concentration obtained synchronously and the model calculated value, so as to obtain the optimal parameter combination.

[0056] As Figure 1 shown, in this embodiment, the traditional soil radon migration model (such as the Abdoh model) is established based on the single gas phase assumption of homogeneous and isotropic medium, without considering the fluctuation of the phreatic water level and the liquid phase migration and gas-liquid interface exchange process caused by it. This results in significant deviations (the error can reach more than ±20%) between the background value of radon concentration calculated by the model and the measured value in areas such as the Jiangsu section of the Tanlu fault zone with "low altitude and high phreatic water level" (the annual amplitude of the phreatic water level can reach 2 meters). This deviation seriously interferes with the identification of true geochemical anomalies and reduces the accuracy and reliability of earthquake monitoring and prediction using fault soil radon gas. The present invention aims to solve the core technical problems of inaccurate calculation of the soil radon background value and difficulty in extracting abnormal signals caused by the dynamic change of the water level in a high phreatic water level environment, and provides a new method that can accurately model and dynamically correct the background value of radon concentration.

[0057] By introducing the water-gas two-phase migration and interface exchange mechanism, the inherent defects of the traditional model in high phreatic water level areas are overcome.

[0058] The background value after dynamic correction can effectively filter out the radon concentration fluctuation (environmental noise) caused by the water level change, making the abnormal signals caused by real tectonic activities or earthquake precursors more prominent, significantly improving the signal-to-noise ratio, and greatly enhancing the accuracy and reliability of abnormal identification.

[0059] The parameter inversion process can obtain important medium characteristic parameters such as the interface transfer rate k that are difficult to directly measure, and these parameters have important scientific value for understanding the radon migration mechanism.

[0060] As a preferred embodiment of the present invention, a water-gas two-phase migration model is constructed, and the overburden is divided into a gas phase region and a liquid phase region, with the dynamically changing phreatic water level L(t) as the boundary:

[0061] Unsaturated zone (gas phase dominant region): Above the phreatic water level, the range is 0 < y < L(t), and the pores are mainly filled with gas.

[0062] Saturated zone (water phase dominant region): Below the phreatic water level, the range is L(t) < y < H, and the pores are completely saturated with water.

[0063] For the two regions, one-dimensional vertical radon migration control equations (steady state form) are established respectively:

[0064] The control equations for both the gas phase region and the liquid phase region are one-dimensional vertical diffusion-convection-decay equations, and the control equation for the gas phase region is: ;

[0065] Where: C g Radon concentration in pores (unit: Bq / m³) 3 ); D g Radon's effective diffusion coefficient in porous gaseous media (unit: m) 2 / s); v g λ represents the convection velocity in the gas phase (unit: m / s), typically driven by pressure gradients, geothermal gradients, etc.; λ is the decay constant of radon (2.1 × 10⁻⁶). -6 s -1 A g The ejection rate of the medium in the gas phase region (unit: Bq / (m³)) 3 ·s)).

[0066] The governing equations for the liquid phase region are:

[0067] ;

[0068] Among them, C w Dissolved radon concentration in pore water (unit: Bq / m³) 3 ); D w Radon's effective diffusion coefficient in water (unit: m) 2 / s); v w A is the seepage velocity in the aqueous phase (unit: m / s), driven by the hydraulic gradient; w The ejection rate of the medium in the liquid phase region (unit: Bq / (m) 3 ·s)).

[0069] In a preferred embodiment of the present invention, at the gas-liquid interface y=L(t), radon undergoes mass exchange through the interface, and the interface transfer flux... As the core mechanism of the coupled two-phase model, the gas-liquid interface transport flux is expressed as:

[0070] ;

[0071] Interface transmission throughput This ensures the continuity of mass at the interface. At the gas-liquid interface y=L(t), the migration of radon satisfies the two fundamental physical principles of mass conservation and thermodynamic equilibrium. Therefore, the coupled boundary conditions are:

[0072] ;

[0073] Meanwhile, it is assumed that the concentration at the interface is instantaneously in equilibrium:

[0074] ;

[0075] in: Radon transport flux at the gas-liquid interface (unit: Bq / (m³)) 3 ·s), the direction is from high chemical potential to low chemical potential; k is the interfacial transport rate (unit: m / s), a key parameter to be inverted, which depends on the properties of the medium; α is the Ostwald coefficient (dimensionless), that is, the equilibrium distribution coefficient of radon in the aqueous and gas phases, which is temperature-dependent; C w / α represents the concentration C in water. w Hypothetical gas phase concentration at equilibrium (unit: Bq / m³) 3 L(t) represents the dynamic diving level.

[0076] In this embodiment, the formula precisely describes how radon escapes from the aqueous phase into the gas phase, or dissolves from the gas phase into the aqueous phase.

[0077] As a preferred embodiment of the present invention, the step of dynamically adjusting the vertical range between the gas phase region and the liquid phase region specifically includes:

[0078] Time-series groundwater level data and corresponding measured values ​​of soil radon concentration were obtained through monitoring wells;

[0079] For each point in time, the current diving level is used as the interface to re-divide the gas phase region and the liquid phase region;

[0080] The finite difference method was used to numerically solve the governing equations and interface conditions, and the gas concentration at the surface was obtained as the background value for model calculation at that moment.

[0081] The calculation results at all times are combined to obtain the dynamic background value curve, and the corrected residual sequence is calculated.

[0082] In this embodiment, the dynamic change L(t) of the groundwater level is acquired in real time via monitoring wells on a daily basis (or at a higher frequency). The model will calculate L(t) at each time point. i Using this as input, the vertical extent of the gas and liquid phase regions is redefined, thereby altering the migration path length of radon and the relative thickness of the two phase regions. The dynamic background value refers to the surface radon concentration C output by the model. calc The curve (t) changing over time can respond to changes in migration conditions caused by water level fluctuations.

[0083] Correction refers to using measured water level data to drive the model and calculate a background value that is closer to the actual situation, thereby eliminating the interference caused by water level fluctuations.

[0084] The specific implementation is as follows:

[0085] Input: Time-series groundwater level data L(t1), L(t2), ..., L(t) obtained from monitoring wells. n ), and the corresponding measured soil radon concentration C means (ti (Used for subsequent parameter inversion).

[0086] Calculation: For each time point t i The current diving level L(t) i Substitute into the model and redefine the vertical range of the gas phase and liquid phase regions as follows:

[0087] The gas phase region is defined as The liquid phase region is defined as , where H is the total thickness of the overburden (a constant obtained through geological exploration).

[0088] Then, the finite difference method is used to numerically solve the governing equations in combination with the interface conditions.

[0089] The specific calculation formula is as follows:

[0090] Depth domain Discretize into M equidistant grid points Step length .

[0091] For nodes within the gas phase region, the difference scheme for the radon migration governing equation is:

[0092] ;

[0093] For nodes inside the liquid phase region, similar to discrete equations; at the interface y=L(t) i At point ), its grid location needs to be determined first, and then the coupling equations are established using interface conditions. By iteratively solving the above linear equations, the C values ​​for all grid points are obtained. g and C w Value, where the gas phase concentration C at the surface y=0. g (0) is the background value C calculated by the model at that moment. calc (t i ).

[0094] Output: Combine the calculation results for all time points to obtain a dynamic background value curve C that varies with time. calc (t) This curve can reflect the impact of water level fluctuations on radon migration.

[0095] The corrected residual sequence is:

[0096] ;

[0097] Among them, C means (t i ) is at time t i Soil radon concentration measured by field instruments is used for subsequent anomaly identification.

[0098] As a preferred embodiment of the present invention, the specific method of parameter inversion and optimization is as follows:

[0099] Construct the objective function in least squares form:

[0100] ;

[0101] Among them, C calc (t i ;P) represents the surface radon concentration calculated by the model under a given parameter vector P; C means (t i ) is at time t i Soil radon concentration measured by field instruments;

[0102] The Levenberg-Marquardt algorithm is used to iteratively optimize the objective function. The optimal parameter combination is obtained through steps such as forward model calculation, Jacobian matrix construction, incremental equation solving, parameter updating and convergence judgment.

[0103] In this embodiment, key parameters that are difficult to measure directly in the calibration model (such as interface transport rate k, refractive index A) are used. g and A w The present invention employs the following optimized process:

[0104] Construct the objective function:

[0105] Measure the calculated value C of the model calc (t) and the actual measured value C at the same time point means The difference between (t) is determined using the least squares method:

[0106] ;

[0107] Among them, C calc (t i ;P) represents the surface radon concentration calculated by the model under a given parameter vector P; The parameter vector to be inverted.

[0108] In a preferred embodiment of the present invention, the iterative process of the Levenberg-Marquardt algorithm includes:

[0109] Initialize the guessed values ​​and damping factors, and run the forward model to calculate the residual vector;

[0110] The Jacobian matrix is ​​approximated by the finite difference method, and the incremental equation is solved.

[0111] Update the parameters and adjust the damping factor according to the changes in the objective function, repeating the iteration until the convergence condition is met.

[0112] In this embodiment, the Levenberg-Marquardt algorithm (LM algorithm) combines the advantages of gradient descent and Gauss-Newton methods and is suitable for nonlinear least squares problems.

[0113] The adjustment process is as follows:

[0114] Initialization: Given an initial guess P0 for the parameters to be inverted (e.g., ... , Set the initial value of the damping factor µ (e.g., µ=0.01) and the convergence tolerance ε.

[0115] Iteration steps:

[0116] Run the forward model and calculate all t i The corresponding C calc (t i ;P m ), and obtain the residual vector. Its components are:

[0117] ;

[0118] Calculate the Jacobian matrix (in The number of parameters to be inverted is 1 in this example. The parameters are k and A. g and A w .

[0119] The elements of the Jacobian matrix are defined as the partial derivatives of the residuals with respect to the parameters:

[0120] ;

[0121] Because of C calc For functions that cannot be written as explicit functions with parameters, the partial derivatives are approximated using the forward finite difference method:

[0122] ;

[0123] Among them, e j Let j be the j-th unit vector; For parameter P j The perturbation step size is usually taken as .

[0124] Therefore, the calculation of the Jacobian matrix requires running the forward model M times (perturbing one parameter each time) to obtain the perturbed calculated concentration.

[0125] Solving the incremental equation: Parameter increment of the LM algorithm This is obtained by solving the following system of linear equations:

[0126] ;

[0127] in, is an M×M approximate Hessian matrix; µ is the damping factor (a non-negative scalar); I is an M×M identity matrix;

[0128] It is an M-dimensional gradient vector.

[0129] Expand, set ;

[0130] ;

[0131] The incremental equation is then:

[0132] ;

[0133] in It is a Kronecker symbol (1 when j=k, 0 otherwise).

[0134] Update parameters: ;

[0135] Rerun the forward model and calculate ,like Then accept the new parameter and decrease µ (e.g.) Otherwise, reject and increase µ (e.g.) ).

[0136] Check convergence conditions: If If the change in φ is less than the threshold, then the iteration stops.

[0137] Output: Obtain the optimal parameter combination P opt This allows for the acquisition of a high-precision, personalized water-air two-phase migration model for a specific observation point, which can be used for subsequent long-term dynamic background value calculation and anomaly identification.

[0138] Model training and performance parameters:

[0139] Training environment: The method of this invention can be implemented on a standard personal computer. The recommended configuration is: CPU 2.0 GHz or higher, memory 8GB or higher, operating system Windows / Linux, programming language Python (using NumPy and SciPy libraries for numerical solution and LM optimization), or MATLAB environment.

[0140] Training data: At least 6 consecutive months of daily groundwater level data and synchronous soil radon concentration measurements are required. The first 5 months of data are used for parameter inversion (training), and the last month of data is used for validation.

[0141] Root Mean Square Error (RMSE): Target value <200 Bq / m 3 ).

[0142] Relative error (RE): The target average relative error is <5%.

[0143] Coefficient of determination (R) 2 ): Measures the goodness of fit of the model, the objective .

[0144] Convergence iterations: The LM algorithm typically converges within 50-200 iterations.

[0145] This invention also provides a system for modeling and correcting background values ​​of radon concentration in fault soil, the system comprising:

[0146] The equation building module is used to construct a water-gas two-phase migration model. Using the dynamically changing water level interface as the boundary, the overburden is vertically divided into a gas-dominated unsaturated zone and a water-dominated saturated zone, and control equations describing the migration behavior of radon in the gas and liquid phases are established respectively.

[0147] The boundary constraint module is used to establish the gas-liquid interface transport flux formula, and based on this flux, couple the gas phase region control equation and the liquid phase region control equation to form the mass conservation and thermodynamic equilibrium boundary conditions at the gas-liquid interface.

[0148] The numerical solution module is used to acquire real-time dynamic data of groundwater level, use it as model input, dynamically adjust the vertical range of the gas phase zone and liquid phase zone, and calculate the dynamic background value of soil radon concentration over time by numerically solving the coupled control equations.

[0149] The inversion optimization module is used to construct the objective function. It optimizes the model parameters to be inverted by utilizing the difference between the synchronously acquired measured soil radon concentration data and the model calculation values, and obtains the optimal parameter combination.

[0150] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for modeling and correcting background values ​​of radon concentration in fault soil, characterized in that, The method includes: A water-gas two-phase migration model was constructed, with the dynamically changing water level interface as the boundary, and the overburden was vertically divided into a gas-dominated unsaturated zone and a water-dominated saturated zone. Control equations describing the migration behavior of radon in the gas and liquid phases were established respectively. A formula for the transport flux at the gas-liquid interface is established, and based on this flux, the governing equations of the gas phase region and the liquid phase region are coupled to form the mass conservation and thermodynamic equilibrium boundary conditions at the gas-liquid interface. Real-time dynamic data of groundwater level is obtained and used as model input. The vertical range of the gas phase zone and liquid phase zone is dynamically adjusted. The dynamic background value of soil radon concentration over time is calculated by numerically solving the coupled control equations. An objective function is constructed, and the model parameters to be inverted are optimized by utilizing the difference between the synchronously acquired measured soil radon concentration data and the model calculation values ​​to obtain the optimal parameter combination.

2. The method for modeling and correcting background values ​​of radon concentration in fault soil according to claim 1, characterized in that, The governing equations for both the gas phase and liquid phase are one-dimensional diffusion-convection-decay equations in the vertical direction. The governing equation for the gas phase is: ； Where: C g D represents the radon concentration in the pores. g v is the effective diffusion coefficient of radon in the porous gas phase; g λ is the convection velocity in the gas phase; λ is the decay constant of radon; A g The ejection rate of the medium in the gas phase region; The governing equations for the liquid phase region are: ； Among them, C w D represents the concentration of dissolved radon gas in pore water. w v is the effective diffusion coefficient of radon in water; w A represents the seepage velocity in the aqueous phase. w The ejection rate of the medium in the liquid phase region.

3. The method for modeling and correcting background values ​​of radon concentration in fault soil according to claim 2, characterized in that, The gas-liquid interface transport flux is expressed as: ； At the gas-liquid interface y=L(t), the coupled boundary condition is: ； ； in: denoted as radon transport flux at the gas-liquid interface; k is the interfacial transport rate; α is the equilibrium distribution coefficient of radon in the aqueous and gas phases; C w / α represents the concentration C in water. w The equilibrium hypothetical gas phase concentration, L(t) is the dynamic water level.

4. The method for modeling and correcting background values ​​of radon concentration in fault soil according to claim 1, characterized in that, The specific steps for dynamically adjusting the vertical range between the gas phase and liquid phase regions are as follows: Time-series groundwater level data and corresponding measured values ​​of soil radon concentration were obtained through monitoring wells; For each point in time, the current diving level is used as the interface to re-divide the gas phase region and the liquid phase region; The finite difference method was used to numerically solve the governing equations and interface conditions, and the gas concentration at the surface was obtained as the background value for model calculation at that moment. The calculation results at all times are combined to obtain the dynamic background value curve, and the corrected residual sequence is calculated.

5. The method for modeling and correcting background values ​​of radon concentration in fault soil according to claim 1, characterized in that, The specific methods for parameter inversion and optimization are as follows: Construct the objective function in least squares form: ； Among them, C calc (t i ;P) represents the surface radon concentration calculated by the model under a given parameter vector P; C means (t i ) is at time t i Soil radon concentration measured by field instruments; The Levenberg-Marquardt algorithm is used to iteratively optimize the objective function. The optimal parameter combination is obtained through steps such as forward model calculation, Jacobian matrix construction, incremental equation solving, parameter updating and convergence judgment.

6. The method for modeling and correcting background values ​​of radon concentration in fault soil according to claim 5, characterized in that, The iterative process of the Levenberg-Marquardt algorithm includes: Initialize the guessed values ​​and damping factors, and run the forward model to calculate the residual vector; The Jacobian matrix is ​​approximated by the finite difference method, and the incremental equation is solved. Update the parameters and adjust the damping factor according to the changes in the objective function, repeating the iteration until the convergence condition is met.

7. A system for modeling and correcting background radon concentration in fault soil, used to implement the method for modeling and correcting background radon concentration in fault soil as described in any one of claims 1-6, characterized in that, The system includes: The equation building module is used to construct a water-gas two-phase migration model. Using the dynamically changing water level interface as the boundary, the overburden is vertically divided into a gas-dominated unsaturated zone and a water-dominated saturated zone, and control equations describing the migration behavior of radon in the gas and liquid phases are established respectively. The boundary constraint module is used to establish the gas-liquid interface transport flux formula, and based on this flux, couple the gas phase region control equation and the liquid phase region control equation to form the mass conservation and thermodynamic equilibrium boundary conditions at the gas-liquid interface. The numerical solution module is used to acquire real-time dynamic data of groundwater level, use it as model input, dynamically adjust the vertical range of the gas phase zone and liquid phase zone, and calculate the dynamic background value of soil radon concentration over time by numerically solving the coupled control equations. The inversion optimization module is used to construct the objective function. It optimizes the model parameters to be inverted by utilizing the difference between the synchronously acquired measured soil radon concentration data and the model calculation values, and obtains the optimal parameter combination.

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