Landfill and leachate distribution range detection method based on multiple physical property parameters

Abstract

A landfill and leachate distribution range detection method based on multiple physical property parameters relates to the field of environmental protection, and comprises the following steps: acquiring a target area selected by a user, and detecting the target area by adopting a multi-frequency electromagnetic method to obtain surface area detection data; determining a preliminary distribution range of landfill and leachate according to the surface area detection data; according to the initial distribution range and the surface area detection data, determining a wiring area of refined detection; detecting electrical structure parameters of different depths in the wiring area by adopting an electrical sounding method, and generating line area detection data; determining a plurality of detection point locations according to the wiring area and the line area detection data; detecting speed construction parameters of different depths in the detection point by adopting a micro-motion method, and generating point area detection data; and inputting the surface area detection data, the line area detection data and the point area detection data into a joint inversion formula to obtain a distribution range result. By implementing the application, the landfill and the percolate in the designated area can be accurately detected.

Description

Technical Field

[0001] The present application relates to the field of environmental protection, and in particular to a method for detecting the distribution range of landfill and leachate based on multiple physical property parameters. Background Art

[0002] With the acceleration of urbanization and the improvement of living standards, the amount of domestic waste is growing rapidly. Landfill is currently one of the main ways to deal with domestic waste. These landfills will produce a large amount of leachate. If the anti-seepage system is damaged, the leachate will cause serious pollution to the surrounding soil and groundwater. Therefore, it is urgent to carry out landfill and leachate leakage detection.

[0003] At present, the detection of leachate leakage in landfills is mainly carried out by setting up monitoring holes around the landfill. By drilling monitoring holes at a certain interval around the landfill, water samples are regularly collected from the monitoring holes for testing and analysis, and the analysis results are used to determine whether leachate leakage has occurred and the degree of contamination.

[0004] However, the single-point monitoring method of monitoring holes is difficult to accurately reflect the actual diffusion range and migration path of leachate underground, which can easily lead to inaccurate judgment of the scope of pollution and affect the subsequent governance work. Summary of the invention

[0005] The present application provides a method for detecting the distribution range of landfill and leachate based on multiple physical property parameters, which is used for accurately detecting landfill and leachate in a specified area.

[0006] In the first aspect, the present application provides a method for detecting the distribution range of landfill and leachate based on multiple physical property parameters, which is applied to a detection system, and the method includes: obtaining a target area selected by a user, detecting the target area using a multi-frequency electromagnetic method, and obtaining surface area detection data; determining a preliminary distribution range of the landfill and leachate based on the surface area detection data; determining a wiring area for detailed detection based on the preliminary distribution range and the surface area detection data; detecting electrical structural parameters at different depths in the wiring area using an electrical depth sounding method, and generating line area detection data; determining a number of detection points based on the wiring area and the line area detection data; detecting velocity structural parameters at different depths in the detection points using a micro-motion method, and generating point area detection data; inputting the surface area detection data, line area detection data, and point area detection data into a joint inversion formula to obtain a distribution range result.

[0007] In the above embodiment, the detection system combines three detection methods, namely, multi-frequency electromagnetic method, electrical depth sounding method and micro-motion method, to form a detection mode in the form of "surface-line-point", and uses multi-frequency electromagnetic method to perform large-scale surface detection to obtain a preliminary distribution range; then, based on the preliminary results, linear detection using electrical depth sounding method is performed to obtain more detailed electrical structural information; finally, point detection is performed using micro-motion method to obtain velocity structural parameters at key locations; the three methods complement each other, characterize the distribution of underground landfill and leachate from different physical property angles, and obtain an accurate distribution range through joint inversion.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the joint inversion formula is: φ(ρ,ν,κ)=φ d (ρ)+αφ d (ν)+βφ d (κ)+λ1φ m (ρ)+λ2φ m (ν)+λ3φ m (κ)+t1φ j1 (ρ,ν)+t2φ j2 (ν,κ)+t3φ j3 (ρ, κ); Where ρ, ν, κ are the underground resistivity, velocity and magnetic susceptibility in the target area Ω0 respectively; φ d is the data fitting term, φ m is the model fitting term, φ j1 ,φ j2 ,φ j3 is the joint inversion constraint term; α and β are constants; λ1, λ2, λ3 and t1, t2, t3 are the weight coefficients of each data respectively.

[0009] In the above embodiment, the detection system characterizes the underground landfill and leachate distribution from different physical property perspectives based on the joint inversion formula, and effectively utilizes the data from three detections.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the preliminary distribution range of the landfill and leachate based on the surface area detection data specifically includes: extracting data results of different electromagnetic frequencies in the surface area detection data, and determining the resistivity distribution data and magnetic susceptibility distribution data in the target area; performing inversion calculation based on the resistivity distribution data and the magnetic susceptibility distribution data to obtain the preliminary distribution range of the landfill and leachate.

[0011] In the above embodiment, the detection system inverts the different frequency results obtained based on the planar detection to obtain a preliminary three-dimensional spatial distribution range, thereby avoiding the limitations of traditional single-point monitoring, and being able to comprehensively reflect the spatial distribution characteristics of the landfill and leachate, making it easier to determine the detailed detection range of key areas.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the wiring area for detailed detection based on the preliminary distribution range and the surface area detection data specifically includes: determining the resistivity extreme points and the magnetic susceptibility extreme points of the resistivity parameters and the magnetic susceptibility parameters within the preliminary distribution range based on the surface area detection data; determining the wiring area for detailed detection with the resistivity extreme points and the magnetic susceptibility extreme points as the center; determining the point spacing and length of the electrical depth sounding line based on the wiring area, so that the electrical depth sounding line completely covers the wiring area.

[0013] In the above embodiment, the detection system determines the refined detection range including the level and depth according to the extreme values ​​of the physical properties within the three-dimensional spatial distribution range, ensuring that key areas are not missed. The area division method based on physical property anomalies improves the pertinence and efficiency of subsequent detection work and avoids the waste of manpower and material resources caused by blind detection.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the step of determining a number of detection points based on the wiring area and line area detection data specifically includes: determining the electrical structure data at different depths in the wiring area based on the line area detection data; determining the detection points for point area detection based on the electrical extreme points in the electrical structure data; determining the observation array size of the detection points based on the detection points, so that the detection depth of the detection points meets the preset detection requirements.

[0015] In the above embodiment, the detection system arranges electrical sounding lines within a determined refined detection range. The line layout positions and coverage range are optimized to ensure the integrity and continuity of the detection results. Line detection has a higher resolution than surface detection and can more accurately represent the distribution characteristics of the landfill and leachate.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of using the micro-motion method to detect velocity structure parameters at different depths within the detection point and generating point area detection data, the method also includes: comparing geological data and point area detection data to obtain detection accuracy; when the detection accuracy does not meet the preset accuracy requirements, adjusting the detection parameters of the micro-motion method and re-performing point area detection.

[0017] In the above embodiment, the detection system determines the point spacing and the length of the measuring line of the electrical sounding method based on the detection range, so that the detection work can not only cover the horizontal range of the target area, but also reach the required detection depth, thereby ensuring the quality and reliability of the detection data.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the target area is represented by Ω0, the preliminary distribution range is represented by Ω1; the wiring area is represented by Ω2; the detection point is represented by Ω3; the preset distances of the line spacing and point spacing of the multi-frequency electromagnetic method in the target area are between 0.1m and 1m, and the preset frequency of the multi-frequency electromagnetic method is between 10 -6 Hz and 10 8 Between Hz.

[0019] In the above embodiment, the detection system is arranged with a line spacing and a point spacing of 0.1-1m, which ensures the resolution and coverage of the surface detection; at the same time, by selecting 10 -6 The frequency range of 100 Hz to 108 Hz enables electromagnetic waves to effectively penetrate to the target depth, which not only ensures the detection accuracy but also achieves the goal of large-scale rapid detection.

[0020] In a second aspect, an embodiment of the present application provides a detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the detection system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product runs on a detection system, the above-mentioned detection system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a detection system, enable the detection system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the detection system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the use of "surface-line-point" multi-method combined detection technology and a joint inversion method based on multiple physical property parameters, it is possible to characterize underground landfills and leachate from multiple physical property angles such as resistivity, velocity and magnetic susceptibility, effectively solving the problem that a single monitoring method in the existing technology is difficult to accurately determine the scope of pollution, thereby achieving high-precision detection of the distribution range of landfills and leachate.

[0025] 2. Due to the use of 10 -6 Hz-108Hz wide-band electromagnetic detection technology, so electromagnetic waves can effectively penetrate to different depths at different frequencies, effectively solving the problem of limited detection depth in existing technologies, and thus realizing comprehensive detection of landfills and leachate within different depth ranges.

[0026] 3. Since the method of optimizing the point distance and survey line length of the electrical depth sounding method based on the detection range is adopted, the detection work can ensure the detection quality while ensuring the coverage rate, and effectively solves the problem of lack of scientific basis for the setting of detection parameters in the existing technology, so that the electrical depth sounding method can not only cover the horizontal range of the target area, but also reach the required detection depth, ensuring the quality and reliability of the detection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a method for detecting landfill and leachate distribution range based on multiple physical property parameters in an embodiment of the present application; Figure 2 (a) is a schematic diagram of an implementation area of ​​the multi-frequency electromagnetic method in an embodiment of the present application; Figure 2 (b) is a schematic diagram of an implementation area of ​​the electrical depth sounding method in the embodiment of the present application; Figure 2 (c) is a schematic diagram of an implementation area of ​​the micro-motion method in the embodiment of the present application; Figure 3 It is another flow chart of the method for detecting the landfill and leachate distribution range based on multiple physical property parameters in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of a physical device of the detection system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] In the suburbs of a certain city, a large-scale domestic waste landfill built in 2010 was put into operation for 14 years. Surrounding residents discovered that the groundwater had a strange smell. The environmental protection department found that the anti-seepage system of the landfill may have been damaged, resulting in leachate leakage. The traditional practice is to drill monitoring wells (monitoring holes) around the landfill for sampling and testing, but due to the complex flow of groundwater and the unknown diffusion range of leachate, it is difficult to accurately determine the scope of pollution by relying solely on monitoring wells. What's more serious is that the drilling process may aggravate the diffusion of leachate and cause secondary pollution. Therefore, how to quickly and accurately detect the distribution range of leachate has become an urgent problem to be solved.

[0032] In the related art, the monitoring of landfill leachate leakage can be achieved by adopting the method of deploying monitoring wells and regularly sampling and analyzing. The following introduces the scenario of using the detection method of landfill and leachate distribution range based on multi-physical parameters in the related art.

[0033] An environmental monitoring unit adopted the traditional monitoring method, drilling 20 monitoring wells at intervals of 50 meters around the landfill and taking samples for regular analysis. However, the monitoring results showed that the concentrations of pollutants in adjacent monitoring wells varied greatly and changed irregularly over time. Further investigation revealed that due to the complex underground rock and soil structure, the leachate migrated along the dominant infiltration channel, resulting in an irregular distribution of polluted areas. The traditional method of equidistant point distribution makes it difficult to accurately characterize the scope of pollution, and the drilling cost is high. At the same time, some monitoring wells did not monitor pollutants at all due to improper locations, resulting in a waste of resources. This method not only increases the cost of the investigation, but also fails to detect the location of the leak in a timely manner.

[0034] The method for detecting the distribution range of landfill and leachate based on multiple physical property parameters in the embodiment of the present application can realize comprehensive detection of the distribution range of leachate through a "surface-line-point" detection mode, accurately locate the leakage position, and determine the spatial distribution characteristics of the leachate. The following describes the scenario in which the method for detecting the distribution range of landfill and leachate based on multiple physical property parameters in the present application is used.

[0035] After adopting this scheme, the investigators first used the multi-frequency electromagnetic method to quickly scan the entire landfill and its surroundings to obtain preliminary abnormal areas. Then, electrical depth measurement lines were laid in the abnormally significant areas to accurately locate the scope of leachate impact. Finally, micro-motion detection was carried out at key locations to obtain detailed underground structure information. Through the joint inversion of the three methods, a three-dimensional distribution map of leachate was successfully drawn, and the damaged position of the anti-seepage layer and the main pollution channel were accurately located. This non-destructive detection method not only avoids the secondary pollution that may be caused by drilling, but also greatly improves the detection efficiency and accuracy.

[0036] It can be seen that the method for detecting landfill and leachate distribution range based on multi-physical property parameters in the embodiment of the present application can not only achieve high-precision detection, but also effectively solve the problems of blind spots and secondary pollution in traditional detection methods, thereby realizing non-destructive, rapid and accurate leachate distribution detection.

[0037] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a method for detecting landfill and leachate distribution range based on multiple physical property parameters in an embodiment of the present application.

[0038] S101, obtaining a target area selected by a user, detecting the target area using a multi-frequency electromagnetic method, and obtaining surface area detection data.

[0039] Among them, the target area refers to the specific geographical range where the user needs to conduct landfill and leachate distribution detection; the multi-frequency electromagnetic method refers to a geophysical method that uses the underground propagation characteristics of electromagnetic waves of different frequencies for detection; the surface area detection data refers to the detection data covering the entire target area obtained by the multi-frequency electromagnetic method, including physical parameters such as resistivity and magnetic susceptibility.

[0040] After receiving the target area selected by the user through the human-computer interaction interface, the detection system will start the surface detection process. Specifically, the system first determines the multi-frequency electromagnetic method survey line layout plan according to the size of the target area, including the survey line spacing (0.1-1m) and the measurement point spacing (0.1-1m); then sets the detection frequency range (10 -6 -108Hz), using multi-frequency electromagnetic method to conduct grid detection of the entire area; finally, collect and organize the detection data to form a complete surface detection data set. Figure 2 (a) Figure 2 (a) is a schematic diagram of an implementation area of ​​the multi-frequency electromagnetic method in the embodiment of the present application; Figure 2 In (a), 201 is a planar survey line, 202 is a target area Ω0, and 203 is a subsequent preliminary distribution range Ω1; after selecting the target area 202 (Ω0), the detection system will perform measurement settings on the target area 202 (Ω0) and divide the area into the form of a planar survey line 201; through the measurement of the multi-frequency electromagnetic method, the preliminary distribution range 203 (Ω1) of the underground landfill and leachate can be detected, which includes the landfill area and the area where leachate exists.

[0041] In some embodiments, the acquisition of the target area and the detection of the surface area can be achieved in the following ways: Optionally, the user first selects the target area on the electronic map, and the system automatically obtains the coordinates of the area boundary; then automatically calculates the appropriate survey line layout parameters according to the area area; then the survey line is laid out according to the parameters and the detection is performed; finally, the data is preprocessed and stored. Optionally, the user first inputs the boundary coordinate points of the target area; then the system generates the survey area grid and plans the survey line; then the field detection is performed by manual layout; finally, the collected data is quality checked and sorted. It is understandable that other methods can also be used to achieve the determination of the target area and the collection of surface area data, which are not limited here.

[0042] S102. Determine the preliminary distribution range of the landfill and leachate based on the surface area detection data.

[0043] Among them, the preliminary distribution range refers to the spatial range where the landfill and leachate may exist obtained by inverting the surface area detection data.

[0044] After acquiring the surface area detection data, the detection system will start the process of determining the preliminary distribution range. Specifically, the system first classifies and organizes the detection data obtained at different frequencies, extracts the resistivity distribution data and magnetic susceptibility distribution data at each frequency; then uses the physical property parameter inversion algorithm to calculate the physical property distribution characteristics at different depths in the target area; finally, based on the physical property abnormal characteristics, the preliminary distribution range of the landfill and leachate is determined.

[0045] In some embodiments, the determination of the preliminary distribution range can be achieved in the following ways: Optionally, first filter and reduce noise on the surface area detection data; then use the resistivity-based inversion algorithm to calculate the electrical distribution; then make auxiliary judgments based on the magnetic susceptibility data; and finally comprehensively determine the preliminary distribution range. Optionally, first establish a physical parameter model of the underground medium; then use the iterative inversion method to calculate the physical distribution; then set a threshold to identify abnormal areas; and finally determine the spatial distribution range of the landfill and leachate. It is understandable that other methods can also be used to achieve the inversion of surface area detection data and the determination of the preliminary distribution range, which are not limited here.

[0046] S103: Determine the wiring area for detailed detection based on the preliminary distribution range and the surface area detection data.

[0047] The wiring area refers to the key linear area range that needs to be refined.

[0048] After determining the preliminary distribution range, the detection system will optimize the wiring area. Specifically, the system first analyzes the resistivity parameters and magnetic susceptibility parameters within the preliminary distribution range based on the surface area detection data to determine the location of the extreme points of physical properties; then, with these extreme points as the center, it delineates the wiring area for detailed detection; finally, according to the characteristics of the wiring area, it determines the layout parameters of the electrical depth sounding line, including the point spacing and the length of the line. Please refer to Figure 2 (b) Figure 2 (b) is a schematic diagram of an implementation area of ​​the electrical depth sounding method in the embodiment of the present application; Figure 2 In (b), 202 is the target area Ω0, 203 is the subsequent preliminary distribution range Ω1, and 204 is the electrical sounding line, that is, the wiring area Ω2; after determining the preliminary distribution range 203 (Ω1) of the underground landfill and leachate, the detection system will determine multiple electrical sounding lines 204 (Ω2) based on the surface area detection data of the target area 202 (Ω0) to perform electrical sounding detection.

[0049] In some embodiments, the determination of the wiring area can be achieved in the following ways: Optionally, first calculate the statistical characteristics of the physical property parameters within the preliminary distribution range; then identify the areas with significant physical property anomalies; then optimize the wiring area in combination with factors such as topography; and finally generate a survey line layout plan. Optionally, first extract the coordinates of the extreme point of the physical property within the preliminary distribution range; then set a buffer zone with the extreme point as the center; then determine the boundary of the wiring area according to the shape of the buffer zone; and finally calculate the survey line layout parameters. It is understandable that other methods can also be used to achieve the determination of the refined detection wiring area, which is not limited here.

[0050] S104, using the electrical depth sounding method to detect electrical structural parameters at different depths in the wiring area, and generating line area detection data.

[0051] Among them, electrical sounding refers to a geophysical method that measures the resistivity of underground media as it changes with depth; electrical structural parameters represent the electrical properties of the medium at different depths; and line area detection data refers to the electrical parameter data along the survey line direction obtained by electrical sounding.

[0052] After determining the wiring area, the detection system will carry out electrical depth sounding detection. Specifically, the system first lays out the electrical depth sounding lines according to the scope of the wiring area; then measures the electrical parameters of each line according to the preset detection parameters; finally, the measurement data is processed to generate line area detection data reflecting the electrical structure at different depths.

[0053] In some embodiments, the line area detection can be achieved in the following ways: Optionally, firstly, the line is laid out and the instrument is set up; then, data is collected using equally spaced measuring points; then, data preprocessing and quality control are performed; and finally, an electrical structural profile is generated. Optionally, firstly, the line parameters are determined according to the detection purpose; then, detection is performed in a multi-channel parallel manner; then, the raw data is filtered and corrected; and finally, the line area data in a standard format is formed. It is understandable that other methods can also be used to achieve electrical structural detection of the wiring area, which is not limited here.

[0054] S105. Determine a number of detection points according to the wiring area and line area detection data.

[0055] The detection point refers to the specific location where micro-motion detection is required.

[0056] After acquiring the line area detection data, the detection system will determine the detection points. Specifically, the system first analyzes the electrical structural characteristics of different depths in the wiring area based on the line area detection data; then identifies the spatial distribution of electrical extreme points based on the electrical structural data; and finally determines the specific location of each detection point and the size of the observation array based on the detection depth requirements. Figure 2 (c) Figure 2 (c) is a schematic diagram of an implementation area of ​​the micro-motion method in the embodiment of the present application; Figure 2 In (c), 202 is the target area Ω0, 203 is the subsequent preliminary distribution range Ω1, 204 is the electrical sounding line Ω2, 205 is the detection point Ω3, and 206 is the detection depth direction at the detection point 205; after determining multiple electrical sounding lines 204 (Ω2) on the target area 202 (Ω0) and performing electrical sounding measurements, the detection system will determine multiple detection points 205 (Ω3) with characteristic properties based on the line area detection data, and perform micro-motion detection downward in the detection depth direction 206 to determine more detailed parameters of the preliminary distribution range 203 of the underground landfill and leachate.

[0057] In some embodiments, the determination of detection points can be achieved in the following ways: Optionally, first three-dimensionally visualize the line area detection data; then identify the area with significant electrical anomalies; then calculate the location of the center point of the anomaly; and finally optimize and determine the distribution of detection points. Optionally, first extract electrical structural data at different depths; then determine the threshold range of electrical parameters; then filter the extreme point locations that meet the conditions; and finally determine the point layout plan based on the coverage requirements. It is understandable that other methods can also be used to achieve point determination based on line area detection data, which is not limited here.

[0058] S106. Use the micro-motion method to detect velocity structural parameters at different depths within the detection point to generate point area detection data.

[0059] Among them, the micro-vibration method refers to a method of detection using the vibration characteristics of underground media; point area detection data refers to the data obtained at a specific point on the change of velocity parameters with depth.

[0060] After determining the detection point, the detection system will carry out micro-vibration detection. Specifically, the system first deploys an observation array at each detection point; then collects the vibration signal of the underground medium according to the preset parameters; then processes and analyzes the signal data; and finally generates point area detection data reflecting the velocity structure at different depths; the velocity structure parameters represent the wave velocity characteristics of the medium at different depths.

[0061] In some embodiments, point area detection can be achieved in the following ways: Optionally, firstly, the observation array is laid out; then the sampling parameters are set for data acquisition; then signal preprocessing and spectrum analysis are performed; finally, a velocity structure profile is generated. Optionally, firstly, the array size and detection depth are determined; then the array method is used for signal acquisition; then data filtering and velocity inversion are performed; finally, point area data in a standard format are formed. It is understandable that other methods can also be used to achieve velocity structure detection of the detection point, which is not limited here.

[0062] S107, inputting the surface area detection data, line area detection data and point area detection data into a joint inversion formula to obtain a distribution range result.

[0063] Among them, the joint inversion formula refers to a mathematical model that comprehensively utilizes multiple physical parameters for inversion calculations; the distribution range results represent the final spatial distribution characteristics of the landfill and leachate.

[0064] After acquiring all the detection data, the detection system will perform the final joint inversion calculation. Specifically, the system first standardizes the detection data of the surface area, line area and point area; then sets the weight coefficient of each type of data; then substitutes the processed data into the joint inversion formula; and finally obtains the final distribution range result through iterative calculation.

[0065] It should be noted that the joint inversion formula is: φ(ρ,ν,κ)=φ d (ρ)+αφ d (ν)+βφ d (κ)+λ1φ m (ρ)+λ2φ m (ν)+λ3φ m (κ)+t1φ j1 (ρ,ν)+t2φ j2 (ν,κ)+t3φ j3 (ρ, κ); In this formula, ρ, ν, κ are the underground resistivity, velocity and magnetic susceptibility in the target area Ω0 respectively; φ d is the data fitting term, φ d (ρ), φ d (ν), φ d (κ) represents the fit between the resistivity, velocity and magnetic susceptibility data and the observed values; φ m is the model fitting term, which is used to constrain the physical rationality of the inversion results; φ j1 ,φ j2 ,φ j3 is the joint inversion constraint term, which is used to describe the correlation between different physical property parameters; α and β are constants, which are used to balance different data fitting terms φ d (ρ), φ d (ν), φ d (κ); λ1, λ2, λ3 and t1, t2, t3 are the weight coefficients of each data, which are used to control the strength of the constraints between different physical parameters.

[0066] The joint inversion formula φ(ρ, ν, κ) achieves comprehensive inversion of multiple physical property parameters through three key components: the data fitting term (φ d (ρ), αφ d (ν), βφ d (κ)) ensures the consistency between the inversion results and the measured data by minimizing the error between the observed value and the calculated value; the model fitting term (λ1φ m (ρ),λ2φ m (ν),λ3φ m (κ)) introduces a smoothness constraint to ensure the physical rationality of the inversion result and avoid unrealistic oscillations caused by measurement errors; the joint inversion constraint term (t1φ j1 (ρ, ν), t2φ j2 (ν, κ), t3φ j3(ρ, κ)) uses the structural correlation between resistivity, velocity and magnetic susceptibility to constrain, balances the contribution of different physical property data through α and β, and adjusts the influence of the correlation between physical property parameters with λ and t. In practical applications, this formula takes the surface area detection data obtained by multi-frequency electromagnetic method, the line area detection data obtained by electrical depth sounding method and the point area detection data obtained by micro-seismic method as input, minimizes the objective function φ(ρ, ν, κ) through iterative optimization algorithm, and finally outputs the optimal three-dimensional spatial distribution of underground resistivity ρ(x, y, z), velocity ν(x, y, z) and magnetic susceptibility κ(x, y, z), realizing high-precision joint inversion of landfill and leachate distribution range. The selection of weight coefficient adopts an adaptive strategy, dynamically adjusts according to the data quality of different depths and different regions, and considers the constraints of geological prior information to ensure the reliability and stability of the inversion results.

[0067] In some embodiments, joint inversion can be achieved in the following ways: Optionally, firstly, the data format is unified; then, the initial model of joint inversion is established; then, iterative optimization calculation is performed; and finally, the final distribution result is generated. Optionally, first, the weight coefficients of various types of data are determined; then, a constraint matrix is ​​constructed; then, an optimization algorithm is used to perform inversion calculation; and finally, the spatial distribution characteristics of the landfill and leachate are output. It is understandable that other methods can also be used to achieve the joint inversion calculation of multiple physical property parameters, which are not limited here.

[0068] In the above embodiment, the detection method of multiple physical property parameters can effectively improve the detection accuracy and efficiency. In practical applications, the reliability of the detection results can be further improved by adjusting the detection parameters and optimizing the data processing method. The following is a supplement to the scenario of this embodiment.

[0069] In actual application, the investigation team further optimized the detection plan. They developed an automated data acquisition system to achieve synchronous operation of multiple instruments, shortening the surface detection time from one week to two days. At the same time, through real-time data analysis, the survey line layout plan is dynamically adjusted to improve detection efficiency. In some areas with complex terrain, drones equipped with small electromagnetic detectors are also used for supplementary exploration. Finally, a complete underground pollution monitoring database was established, which can not only track the diffusion process of leachate, but also predict potential pollution risks, providing a scientific basis for pollution control.

[0070] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 3 , is another flow chart of the method for detecting the landfill and leachate distribution range based on multiple physical property parameters in an embodiment of the present application.

[0071] S301, obtaining a target area selected by a user, detecting the target area using a multi-frequency electromagnetic method, and obtaining surface area detection data.

[0072] Referring to step S101 , the detection system collects surface area detection data.

[0073] The detection system monitors the quality characteristics of the surface detection data in real time and automatically starts the data quality optimization process when the following abnormal conditions are found: 1. When the signal-to-noise ratio of the measuring point is detected to be lower than 10dB through the signal-to-noise ratio analysis module, the system will first extend the data acquisition time to twice the original time, and increase the sampling times to 50 times / point, and increase the transmission power by 20% if necessary; 2. If the signal-to-noise ratio of the entire area is generally low (average value <15dB), it will automatically switch to the low-noise period at night (22:00-4:00) for supplementary measurement, and adjust the measurement line to avoid interference sources within a radius of 50m; 3. When the spatial continuity analysis module detects that the data jump of adjacent measuring points exceeds 50%, the system will add 3 encrypted measuring points on both sides of the abnormal point (the spacing is reduced to 1 / 3 of the original), and start the instrument self-check program to verify the working parameters, including frequency setting, gain control and sampling rate, and use the RTK positioning system to review the coordinate accuracy of the measuring points; 4. If the optimized data still does not meet the quality requirements, the system will automatically generate an abnormal area mark and perform weighted processing on the data in this area during joint inversion.

[0074] S302, extracting data results of different electromagnetic frequencies from the surface area detection data, and determining resistivity distribution data and magnetic susceptibility distribution data in the target area.

[0075] Among them, the electromagnetic frequency represents the different frequency levels used for detection; the resistivity distribution data represents the spatial distribution characteristics of the resistivity in the target area; and the magnetic susceptibility distribution data represents the spatial distribution characteristics of the medium's magnetic properties.

[0076] After acquiring the surface detection data, the detection system will classify and extract the data. Specifically, the system first classifies the raw data obtained by the multi-frequency electromagnetic method by frequency; then performs filtering and noise reduction on the data at each frequency; then calculates the distribution data of resistivity and magnetic susceptibility respectively through the inversion algorithm; finally, the two physical property parameter data are standardized and stored.

[0077] In some embodiments, the extraction of different physical property parameter data can be achieved in a variety of ways: optionally, first perform spectrum analysis on the original data; then perform frequency domain filtering; then calculate the spatial distribution of each physical property parameter; and finally perform data format conversion and storage. Optionally, first classify and organize the data by frequency; then perform signal-to-noise ratio analysis and data screening; then obtain the physical property parameter distribution through inversion; and finally generate distribution data in a standard format. It is understandable that other methods can also be used to process and extract surface area detection data, which are not limited here.

[0078] S303: Perform inversion calculation based on the resistivity distribution data and the magnetic susceptibility distribution data to obtain the preliminary distribution range of the landfill and leachate.

[0079] Among them, inversion calculation refers to the process of converting detection data into the physical property distribution of underground media through mathematical models.

[0080] After obtaining the distribution data of the two physical property parameters, the detection system will perform a comprehensive inversion calculation. Specifically, the system first establishes an initial inversion model based on multiple physical property parameters; then sets the weight coefficient of each physical property parameter; then performs a joint inversion calculation through an iterative optimization method; and finally determines the preliminary distribution range of the landfill and leachate based on the inversion results.

[0081] In some embodiments, the inversion calculation of multiple physical property parameters can be achieved in a variety of ways: optionally, first construct the objective function of the joint inversion; then set the inversion parameters and constraints; then perform iterative calculations to solve; and finally output the preliminary distribution range results. Optionally, first determine the weight configuration of the physical property parameters; then establish a multi-parameter coupling model; then use the optimization algorithm to perform inversion; and finally obtain the distribution range results. It is understandable that other methods can also be used to achieve the joint inversion calculation of physical property parameters, which are not limited here.

[0082] S304. Based on the surface area detection data, determine the resistivity extreme value points and magnetic susceptibility extreme value points of the resistivity parameters and magnetic susceptibility parameters within the preliminary distribution range.

[0083] Among them, the resistivity extreme point represents the local maximum or minimum position in the resistivity parameter distribution; the magnetic susceptibility extreme point represents the local maximum or minimum position in the magnetic susceptibility parameter distribution.

[0084] After obtaining the preliminary distribution range, the detection system will perform extreme point analysis. Specifically, the system first grids the surface area detection data; then calculates the resistivity parameters and magnetic susceptibility parameters of each grid point; then identifies local extreme points through extreme value analysis algorithms; and finally locates the identified resistivity extreme points and magnetic susceptibility extreme points in space.

[0085] In some embodiments, the determination of extreme points can be achieved in a variety of ways: optionally, first construct a parameter space distribution model; then set an extreme value identification threshold; then calculate the local gradient change; and finally determine the position coordinates of the extreme points. Optionally, first perform data smoothing; then use a sliding window method to search for extreme values; then perform extreme point clustering analysis; and finally output the final extreme point set. It is understandable that other methods can also be used to determine the extreme points of physical property parameters, which are not limited here.

[0086] S305 , determining a wiring area for detailed detection with the resistivity extreme point and the magnetic susceptibility extreme point as the center.

[0087] The center represents the reference point for regional division based on the extreme point.

[0088] After the detection system determines the extreme points, it will divide the wiring area. Specifically, the system first sets a buffer zone with each extreme point as the center; then merges adjacent buffer zones based on the distribution characteristics of the extreme points; then optimizes the regional boundaries by considering factors such as topography; and finally determines the final wiring area range.

[0089] In some embodiments, the determination of the wiring area can be achieved in a variety of ways: optionally, first calculate the spatial relationship between extreme points; then generate a preliminary buffer range; then merge and optimize the regions; and finally output the wiring area boundary. Optionally, first set the influence range of a single extreme point; then analyze the spatial distribution of multiple extreme points; then divide and merge the regions; and finally determine the refined detection range. It is understandable that other methods can also be used to achieve the determination of the wiring area based on extreme points, which is not limited here.

[0090] S306: Determine the point spacing and length of the electrical depth sounding line based on the wiring area, so that the electrical depth sounding line completely covers the wiring area.

[0091] Among them, point distance refers to the interval between adjacent measuring points on the electrical sounding line; length refers to the total length of a single measuring line.

[0092] After determining the wiring area, the detection system will design the measurement line parameters. Specifically, the system first calculates the optimal measurement line direction based on the shape of the wiring area; then determines the measurement point spacing based on the expected detection accuracy; then calculates the measurement line length that meets both horizontal and depth coverage requirements; and finally generates a detailed measurement line layout plan.

[0093] In some embodiments, the determination of the survey line parameters can be achieved in a variety of ways: optionally, first analyze the geometric characteristics of the wiring area; then calculate the minimum measurement point spacing; then determine the survey line direction and length; and finally generate a survey line layout diagram. Optionally, first set the detection level and depth requirements; then optimize the measurement point distribution density; then calculate the survey line coverage; and finally output the survey line parameter solution. It is understandable that other methods can also be used to determine the electrical depth sounding method survey line parameters, which are not limited here.

[0094] S307, using the electrical depth sounding method to detect electrical structural parameters at different depths in the wiring area, and generating line area detection data.

[0095] Referring to step S104, the detection system collects line area detection data.

[0096] Among them, the electrical structural parameters represent the distribution of electrical characteristics of the underground medium; the line area detection data represents the electrical parameter data set obtained along the survey line direction; and different depths represent the stratification characteristics in the vertical direction.

[0097] After determining the survey line parameters, the detection system will carry out electrical depth sounding detection. Specifically, the system first lays out the line on site according to the survey line layout plan; then sets the working parameters of the electrical depth sounding instrument; then collects data according to the preset point distance; and finally processes the collected data to form line area detection data in a standard format.

[0098] In some embodiments, the detection of electrical structural parameters can be achieved in a variety of ways: optionally, first perform survey line positioning and setting out; then debug instrument parameter settings; then conduct point-by-point depth measurement; and finally perform data preprocessing and storage. Optionally, first determine the type of depth measurement device; then calibrate the instrument system error; then use the automatic measurement mode to collect; and finally generate an electrical structural data set. It is understandable that other methods can also be used to obtain line area detection data, which is not limited here.

[0099] In some embodiments, the detection system sets up a triple line area detection data anomaly detection mechanism: 1. Real-time analysis of the resistivity value of the measuring point. When it is detected that the value of a measuring point exceeds the range of plus or minus 2 standard deviations of the local average value, the abnormal processing process is automatically triggered, including re-measuring 3 times independently, checking the electrode grounding resistance (required to be <2kΩ), and adjusting the measurement parameters (including power supply current, sampling time and number of superpositions); 2. Continuously monitor whether the detection depth reaches the set target. If it does not meet the requirements, the system will automatically increase the power supply current by 30%, expand the maximum electrode distance by 1.5 times, increase the length of the measuring line to both sides by 1.5 times, and re-arrange the measuring line for additional measurement if necessary; 3. Perform repeatability tests on each measuring point. When the error exceeds 10%, start the optimization program, including increasing the number of measurements to 5 times / point, optimizing the acquisition parameter configuration (adjusting gain, filter settings, etc.), and changing the type of measuring device when necessary (such as switching from Wenner device to Schellenberg device); all exception handling records will be saved and used for subsequent data processing and quality assessment.

[0100] S308 , determining electrical structure data at different depths in the wiring area based on the line area detection data.

[0101] Among them, the electrical structure data represents the electrical characteristics of the underground medium at different depths, and different depths represent the layered information from the surface to the depth of the detection target.

[0102] After acquiring the line area detection data, the detection system will perform electrical structure analysis and imaging. Specifically, the system first processes the line area detection data in layers; then uses the inversion algorithm to calculate the electrical parameters of each layer; then performs spatial interpolation of electrical structures at different depths; and finally forms a complete three-dimensional electrical structure data set.

[0103] In some embodiments, the determination of electrical structural data can be achieved in a variety of ways: optionally, first perform data layer analysis; then establish a layered medium model; then calculate the electrical parameters of each layer; and finally generate an electrical structural profile. Optionally, first process the original bathymetric data; then perform a one-dimensional inversion calculation; then perform a two-dimensional interpolation process; and finally output the electrical structural data. It is understandable that other methods can also be used to achieve structural analysis of line area detection data, which is not limited here.

[0104] S309, determining the detection points for point area detection according to the electrical extreme value points in the electrical structure data.

[0105] Among them, the electrical extreme point indicates the location point where the resistivity parameter in the electrical structure data has a local maximum or minimum value; the detection point indicates the specific coordinate position where the micro-seismic observation array needs to be deployed.

[0106] After completing the analysis of the electrical structure data, the detection system will optimize and determine the detection points. Specifically, the system first performs three-dimensional visualization processing on the electrical structure data to generate a spatial distribution map of the electrical parameters; then the local extreme value points of the electrical parameters are identified through the extreme value analysis algorithm, and their abnormal intensity is calculated; then the extreme value points are sorted and screened according to the abnormal intensity, and the points with weaker abnormalities are eliminated; finally, the final micro-motion detection point layout is determined by comprehensively considering the site conditions and detection efficiency.

[0107] In some embodiments, the determination of the detection points can be achieved in a variety of ways: optionally, first establish a three-dimensional model of electrical parameters; then set the extreme value recognition threshold; then calculate the local abnormality intensity; then perform point optimization analysis; and finally output the coordinates of the detection points. Optionally, first grid the electrical data; then use a local window to search for extreme values; then perform cluster analysis on the extreme value points; then evaluate the representativeness of the points; and finally determine the detection point plan. It is understandable that other methods can also be used to achieve the determination of detection points based on electrical extreme value points, which are not limited here.

[0108] S310: Determine the size of the observation array of the detection point based on the detection point, so that the detection depth of the detection point meets the preset detection requirements.

[0109] Among them, the size of the observation array indicates the size of the spatial layout of the sensors during micro-motion detection; the detection depth indicates the maximum distance from the surface at which effective signals can be obtained; the preset detection requirements include technical indicators such as target detection depth and resolution.

[0110] After determining the detection point, the detection system will design the parameters of the observation array. Specifically, the system first calculates the minimum required array size based on the expected detection depth using an empirical formula; then optimizes the array shape and size based on the on-site terrain conditions and the surrounding environmental noise level; then calculates the optimal layout spacing and arrangement of the sensors; and finally generates an observation array layout map containing the precise coordinates of the sensors.

[0111] In some embodiments, the design of the observation array can be achieved in a variety of ways: optionally, first calculate the theoretical minimum array size; then optimize the array shape; then determine the sensor deployment density; then generate the equipment deployment diagram; and finally output detailed array parameters. Optionally, first set the target detection depth; then evaluate the site constraints; then optimize the array geometry; then calculate the instrument deployment plan; and finally form the array design file. It is understandable that other methods can also be used to achieve the design of an observation array that meets detection needs, which is not limited here.

[0112] S311. Use the micro-motion method to detect velocity structural parameters at different depths within the detection point and generate point area detection data.

[0113] Referring to step S106, the detection system will collect point area detection data.

[0114] Among them, the micro-vibration method refers to a geophysical method of detection using small vibrations naturally or artificially excited by underground media; the velocity structure parameter represents the variation characteristics of the wave velocity of the underground medium with depth; and the point area detection data represents the complete set of detection results obtained at a single detection point.

[0115] After completing the design of the observation array, the detection system will carry out micro-motion detection. Specifically, the system first accurately arranges the observation array according to the design drawings, installs and debugs the detection equipment; then sets the data acquisition parameters such as sampling rate and record length, and starts continuous acquisition; then monitors and evaluates the quality of the collected signals in real time, and adjusts the parameters when necessary; finally, processes and analyzes the effective data to form point area detection data in a standard format.

[0116] In some embodiments, the detection of velocity structure parameters can be achieved in a variety of ways: optionally, firstly, array equipment is deployed; then, signal quality testing is performed; then, continuous data acquisition is performed; then, data preprocessing is performed; and finally, velocity structure profiles are generated. Optionally, the detection system parameters are first calibrated; then, an acquisition trigger mechanism is established; then, synchronous data recording is performed; then, signal analysis and processing is performed; and finally, standard data results are output. It is understandable that other methods can also be used to obtain point area detection data, which are not limited here.

[0117] In some embodiments, the detection system compares the geological data and the point area detection data to obtain the detection accuracy; when the detection accuracy does not meet the preset accuracy requirements, the detection parameters of the micro-motion method are adjusted and the point area detection is performed again.

[0118] Among them, geological data refers to the existing stratigraphic information and physical parameter data in the exploration area; detection accuracy refers to the degree of consistency between the point area detection results and the actual geological conditions; the preset accuracy requirement refers to the minimum accuracy standard that the detection results need to achieve; detection parameters refer to the adjustable acquisition parameters during the micro-motion detection process, including technical indicators such as sampling rate, record length, and number of superpositions.

[0119] After completing the point area detection data collection, the detection system will conduct a detection accuracy assessment. Specifically, the system first collects the existing geological drilling, geophysical exploration and other data in the area; then conducts a quantitative comparative analysis of the velocity structure obtained from the point area detection and the geological data; then calculates the relative error and absolute error of the detection results; if the detection accuracy does not meet the preset requirements, it is necessary to adjust the sampling rate, record length and other parameters, and re-collect data until the preset accuracy requirements are met.

[0120] In some embodiments, the detection accuracy evaluation and parameter optimization can be achieved in a variety of ways: optionally, first establish a geological reference model; then calculate the detection result deviation; then perform error statistical analysis; then optimize the detection parameter settings; and finally re-carry out data acquisition. Optionally, first extract key layer information; then perform velocity structure comparison; then evaluate the fit level; then adjust the acquisition parameter plan; and finally perform supplementary detection work. It is understandable that other methods can also be used to achieve detection accuracy evaluation and parameter optimization, which are not limited here.

[0121] S312, input the surface area detection data, line area detection data and point area detection data into the joint inversion formula to obtain the distribution range result.

[0122] Referring to step S107 , the detection system generates a distribution range result based on the data collected three times.

[0123] In the embodiments of the present application, due to the adoption of a "surface-line-point" multi-method combined detection technology based on multi-frequency electromagnetic method, electrical depth sounding method and micro-motion method, and the proposal of a joint inversion formula for multiple physical property parameters, it is possible to characterize underground landfill and leachate from multiple physical property angles such as resistivity, velocity and magnetic susceptibility, effectively solving the problems of low single-point monitoring accuracy, small coverage, and easy secondary pollution in the prior art, thereby achieving rapid, non-destructive, and high-precision detection of the distribution range of landfill and leachate, reducing detection costs and improving work efficiency.

[0124] The detection system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 4 , is a schematic diagram of a physical device structure of a detection system in an embodiment of the present application.

[0125] It should be noted that Figure 4 The structure of the detection system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0126] like Figure 4 As shown, the detection system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0127] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0128] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are performed.

[0129] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0131] Specifically, the detection system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the method for detecting the landfill and the distribution range of leachate based on multiple physical property parameters provided in the above embodiment is implemented.

[0132] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the detection system described in the above embodiment; or may exist independently without being assembled into the detection system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the detection system, the detection system implements the method for detecting landfill and leachate distribution range based on multiple physical property parameters provided in the above embodiment.

[0133] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0134] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0135] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for detecting landfill and leachate distribution range based on multiple physical parameters, characterized in that: Applied to a detection system, the method comprises: Acquire a target area selected by a user, detect the target area using a multi-frequency electromagnetic method, and obtain surface area detection data; Determine the preliminary distribution range of the landfill and leachate based on the surface area detection data; Determining a wiring area for detailed detection according to the preliminary distribution range and the surface area detection data; Using electrical depth sounding method to detect electrical structural parameters at different depths in the wiring area to generate line area detection data; Determining a plurality of detection points according to the wiring area and the line area detection data; The micro-motion method is used to detect velocity structural parameters at different depths within the detection point to generate point area detection data; The surface area detection data, the line area detection data and the point area detection data are input into a joint inversion formula to obtain a distribution range result.

2. The method according to claim 1, characterized in that The joint inversion formula is: φ(r,n,k)=φ d (r)+af d (n)+bf d (k)+λ1φ m (r)+λ2φ m (n)+λ3φ m (k)+t1φ j1 (p,n)+t2φ j2 (n, k)+t3φ j3 (r, k); Wherein, ρ, ν, κ are respectively the underground resistivity, velocity and magnetic susceptibility in the target area Ω0; d is the data fitting term, the φ m is the model fitting term, the φ j1 ,φ j2 ,φ j3 is the joint inversion constraint term; α and β are constants; λ1, λ2, λ3 and t1, t2, t3 are weight coefficients of each data respectively.

3. The method according to claim 1, characterized in that The step of determining the preliminary distribution range of the landfill and the leachate according to the surface area detection data specifically includes: Extracting data results of different electromagnetic frequencies from the surface area detection data to determine resistivity distribution data and magnetic susceptibility distribution data in the target area; An inversion calculation is performed based on the resistivity distribution data and the magnetic susceptibility distribution data to obtain a preliminary distribution range of the landfill and leachate.

4. The method according to any one of claim 1, characterized in that: The step of determining the wiring area for detailed detection according to the preliminary distribution range and the surface area detection data specifically includes: Based on the surface area detection data, determining the resistivity extreme value points and the magnetic susceptibility extreme value points of the resistivity parameters and the magnetic susceptibility parameters within the preliminary distribution range; Determine a wiring area for detailed detection with the resistivity extreme point and the magnetic susceptibility extreme point as the center; The point spacing and length of the electrical depth sounding line are determined based on the wiring area, so that the electrical depth sounding line completely covers the wiring area.

5. The method according to claim 1, characterized in that: The step of determining a plurality of detection points according to the wiring area and the line area detection data specifically includes: Based on the line area detection data, determining electrical structure data at different depths in the wiring area; Determining detection points for performing point area detection according to the electrical extreme value points in the electrical structure data; The size of the observation array of the detection point is determined based on the detection point, so that the detection depth of the detection point meets the preset detection requirements.

6. The method according to any one of claims 1 to 5, characterized in that: After the step of using the micro-motion method to detect the velocity structural parameters at different depths in the detection point and generating point area detection data, the method further includes: Compare the geological data with the point area detection data to obtain the detection accuracy; When the detection accuracy does not meet the preset accuracy requirement, the detection parameters of the micro-motion method are adjusted and the point area detection is performed again.

7. The method according to any one of claims 1 to 5, characterized in that: The target area is represented by Ω0, the preliminary distribution range is represented by Ω1; the wiring area is represented by Ω2; the detection point is represented by Ω3; the preset distances of the line spacing and point spacing of the multi-frequency electromagnetic method in the target area are between 0.1m and 1m, and the preset frequency of the multi-frequency electromagnetic method is between 10 -6 Hz and 10 8 Between Hz.

8. A detection system, characterized in that: The detection system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the detection system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a detection system, the detection system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a detection system, the detection system is caused to perform the method according to any one of claims 1 to 7.

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