A soil automatic measurement method based on ground penetrating radar

Abstract

The present invention relates to the technical field of soil quality analysis, and specifically discloses an automatic soil measurement method based on ground penetrating radar. By using a ground penetrating radar with an integrated multi-frequency antenna, multi-frequency data of a soil profile is acquired in real time along a survey line. The multi-frequency data is preprocessed, and the multi-frequency data are aligned in time and space to obtain first multi-frequency data. The first multi-frequency data are fused according to corresponding spatial positions to obtain second multi-frequency data. Based on the second multi-frequency data, it is automatically determined whether dielectric anomalies caused by straw debris exist in the surface layer, and the dielectric constant is corrected. After obtaining the corrected dielectric constant, the calculated soil thickness is associated with the spatial position at each position along the survey line to generate a soil thickness profile distribution, thereby realizing real-time soil quality monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of soil quality analysis, and more particularly to an automatic soil measurement method based on ground penetrating radar. Background Art

[0002] Ground penetrating radar has been widely used in soil thickness measurement, underground pipeline detection, archaeology, engineering survey and other fields. Its basic principle is to use the characteristics of high-frequency electromagnetic waves to produce reflection at the interface of different media, send narrow pulse signals to the underground through the transmitting antenna, and capture the reflected waves by the receiving antenna, and then infer the position and shape of the underground structure based on the two-way travel time and propagation speed of the reflected waves. The existing literature (Liu Chang, Liu Qin, Zhang Qiong, Dan Chenxi, Liu Gang. Using ground penetrating radar to measure the spatial variation of the thickness of the black soil layer on the slope of Northeast China [J]. Acta Pedologica Sinica, 2024, 61 (4): 952–963.) explores the influence of soil moisture content and bulk density on the soil dielectric constant and the feasibility of ground penetrating radar to measure the thickness of black soil, and gives the following results: Figure 2 The ground penetrating radar detection method shown in the figure, however, in practical applications, there are many challenges in accurately measuring soil quality.

[0003] In areas where straw is returned to the fields, the surface soil has transformed from a single mixture of mineral particles and water to a multiphase medium system composed of an interlaced network of soil particles, straw debris, and air pores. This medium inhomogeneity can introduce systematic errors in ground penetrating radar (GPR) soundings. Specifically, the dielectric constant of straw debris is significantly lower than the typical value of mineral-water mixed soil. When the debris is distributed in the upper shallow soil layer, the radar wave velocity is locally accelerated, and the travel time of the reflection interface appears earlier. Because GPR data processing typically uses a single average wave velocity model, the travel time at different locations is directly converted to depth without considering the spatial variation in wave velocity caused by straw distribution. High-frequency depth fluctuations will be observed on adjacent survey lines, leading to errors in soil quality assessment, affecting the precise positioning of geological profiles and hidden targets, and interfering with the quantitative inversion of moisture content and bulk density. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an automatic soil measurement method based on ground penetrating radar, which automatically determines whether there are dielectric anomalies caused by straw debris in the surface layer based on multi-frequency data, and monitors the soil quality in real time. It is used to solve the problem that the prior art does not consider the error influence of the spatial change of wave velocity caused by straw distribution on the measurement of soil thickness. It can effectively identify electromagnetic anomalies caused by straw debris or other shallow heterogeneous media, significantly reduce the misjudgment and missed judgment rates, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A soil automatic measurement method based on ground penetrating radar comprises the following steps:

[0007] Step 1: A ground penetrating radar with an integrated multi-frequency antenna acquires multi-frequency data of the soil profile in real time along the survey line;

[0008] Step 2: preprocess the multi-frequency data, align the multi-frequency data in time and space to obtain first multi-frequency data, and fuse the first multi-frequency data according to corresponding spatial positions to obtain second multi-frequency data;

[0009] Step 3: Based on the second multi-frequency data, a multi-level automatic determination is performed to determine whether there is dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected;

[0010] Step 4: After obtaining the corrected dielectric constant, for each position along the survey line, the two-way time of the reflection event axis at the bottom of the soil layer is picked up in the ground penetrating radar profile. The average wave velocity of the dielectric mixing model at that position is used to convert it into soil thickness. The calculated soil thickness is associated with the spatial position to generate a soil thickness profile distribution, realizing real-time thickness monitoring.

[0011] In step 1, the system operates with a fixed transmit-receive spacing (co-offset configuration), continuously collecting data while traveling to achieve near-real-time monitoring. Because multiple antennas or multiple scans are used to acquire data at each frequency, it is necessary to ensure that the spatial locations of the different frequency profiles correspond to each other.

[0012] As a further solution of the present invention, in step 3, a multi-level automatic determination is performed based on the second multi-frequency data to determine whether there is a dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected. The specific steps include:

[0013] Step 31, obtaining a first multi-frequency feature by using the difference between adjacent second multi-frequency data, and obtaining a second multi-frequency feature by extracting the difference between the maximum data and the minimum data from the second multi-frequency data measured at the same frequency;

[0014] Step 32, based on the second multi-frequency data, a secondary determination is made as to whether there is a dielectric anomaly caused by straw debris. The step of performing a primary anomaly determination includes: obtaining a frequency fluctuation factor based on the ratio of the first multi-frequency feature and the second multi-frequency feature, and comparing the frequency fluctuation factor with a preset fluctuation threshold in turn. If the frequency fluctuation factor is greater than the preset fluctuation threshold, it is determined that there is straw debris on the surface; otherwise, it is determined that there is no straw debris on the surface.

[0015] As a further solution of the present invention, in step 4, the specific steps of achieving real-time soil quality monitoring include:

[0016] The difference between the soil thicknesses of soil profiles at adjacent measuring points is used as the first quality monitoring factor, and the soil quality is evaluated based on the comparison result of the mean value of the first quality monitoring factor and the preset soil quality range; if the mean value of the first quality monitoring factor is within the preset soil quality range, the soil quality of the soil profile is qualified; if the mean value of the first quality monitoring factor is not within the preset soil quality range, the soil quality of the soil profile is unqualified.

[0017] As a further solution of the present invention, if straw debris is determined to exist on the surface layer, the dielectric constant is corrected using a dielectric mixture model; if straw debris is not found on the surface layer, no correction is required for the first determination.

[0018] As a further solution of the present invention, if straw debris exists on the surface, the dielectric constant is corrected to obtain the actual dielectric constant of the overlying soil at each point, and then the average dielectric constant is used as the calibrated soil dielectric constant value.

[0019] As a further solution of the present invention, step 32 is to perform a secondary determination based on the second multi-frequency data to determine whether there is a dielectric anomaly caused by straw debris. The specific steps of the secondary determination include:

[0020] Step 321: Obtain the first multi-frequency feature and the second multi-frequency data of the same measuring point as a first distribution factor, and obtain in real time a first mean of the first distribution factor and a second mean of the first multi-frequency feature of the measured point;

[0021] Step 322 : Using the ratio of the first mean value to the second mean value as a straw distribution factor, the straw distribution factor is compared with a preset distribution factor to determine whether dielectric anomalies caused by straw debris exist.

[0022] As a further solution of the present invention, if the straw distribution factor is greater than a preset distribution factor, a secondary determination is made that dielectric anomaly caused by straw debris exists; otherwise, a secondary determination is made that dielectric anomaly caused by straw debris does not exist.

[0023] As a further solution of the present invention, in step 2, preprocessing includes time zero point correction, bandpass filtering to suppress environmental noise, and background subtraction to highlight the underground target echo; the multi-frequency data are aligned in time and space to obtain the first multi-frequency data: by interpolation or resampling, the waveforms of different frequencies have the same time sampling rate and starting time reference; in space, the position deviation of each frequency is corrected according to the antenna geometric configuration.

[0024] The technical effects and advantages of the automatic soil measurement method based on ground penetrating radar of the present invention are as follows: the present invention uses a ground penetrating radar with an integrated multi-frequency antenna to obtain multi-frequency data of the soil profile in real time along the survey line, performs preprocessing on the multi-frequency data, aligns the multi-frequency data in time and space to obtain first multi-frequency data, fuses the first multi-frequency data according to the corresponding spatial position to obtain second multi-frequency data, automatically determines whether there is dielectric anomaly caused by straw debris in the surface layer based on the second multi-frequency data, and corrects the dielectric constant, which can eliminate the wave velocity deviation caused by straw and air pores. After obtaining the corrected dielectric constant, the calculated soil thickness is associated with the spatial position at each position along the survey line to generate a soil thickness profile distribution, realize real-time soil quality monitoring, effectively identify electromagnetic anomalies caused by straw debris or other shallow inhomogeneous media, and significantly reduce the misjudgment and missed judgment rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The three-dimensional distribution map of soil thickness provided by the present invention;

[0026] Figure 2 A diagram of the ground penetrating radar detection method provided by the present invention;

[0027] Figure 3 An image of the interface between the soil layer and the arsenic sandstone layer provided by the present invention;

[0028] Figure 4 A flow chart of the use and parameter input of the ground penetrating radar provided by the present invention;

[0029] Figure 5 A radar image obtained before the dielectric constant calibration of the shrub sample plot provided by the present invention;

[0030] Figure 6 A radar image obtained after the dielectric constant of the shrub sample plot provided by the present invention is calibrated;

[0031] Figure 7 The "intelligent tracker" function provided by the present invention identifies the soil-rock interface. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the technical solutions described are only part of the present invention, not the entire invention. Based on the technical solutions of the present invention, all other technical solutions obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] Example 1

[0034] A soil automatic measurement method based on ground penetrating radar comprises the following steps:

[0035] Step 1: A ground penetrating radar with an integrated multi-frequency antenna acquires multi-frequency data of the soil profile in real time along the survey line;

[0036] Step 2: preprocess the multi-frequency data, align the multi-frequency data in time and space to obtain first multi-frequency data, and fuse the first multi-frequency data according to corresponding spatial positions to obtain second multi-frequency data;

[0037] Step 3: Automatically determine whether there is dielectric anomaly caused by straw debris on the surface based on the second multi-frequency data, and correct the dielectric constant;

[0038] Step 4: After obtaining the corrected dielectric constant, pick up the two-way time of the reflection event at the bottom of the soil layer in the GPR profile for each position along the survey line. , using the average wave velocity of the dielectric mixing model at this location , which is converted into soil thickness , the calculated soil thickness is associated with the spatial position to generate the soil thickness profile distribution and realize real-time thickness monitoring.

[0039] It should be noted that in step 1, a fixed transmit-receive spacing (co-offset configuration) is used, continuously collecting data while traveling to achieve near-real-time monitoring. Because multiple antennas or multiple scans are used to acquire data at each frequency, it is necessary to ensure that the spatial locations of different frequency profiles correspond to each other.

[0040] In step 2, preprocessing is performed, including time zero point correction (removing the delay of the direct pulse from the antenna), bandpass filtering to suppress ambient noise, and background subtraction to highlight the underground target echo; the multi-frequency data are aligned in time and space to obtain the first multi-frequency data: by interpolation or resampling, the waveforms of different frequencies have the same time sampling rate and starting time reference; in space, the position deviation of each frequency B-scan is corrected according to the antenna geometry configuration.

[0041] Fusion processing can include sliding window fusion, empirical mode decomposition, or wavelet transform fusion. For example, at each spatial location, high-, medium-, and low-frequency reflection signals can be weighted and summed to form a "fused A-scan." Weights can be optimized based on factors such as signal-to-noise ratio and resolution (even automated optimization methods such as genetic algorithms can be used). The fused signal contains comprehensive information from each frequency band, facilitating subsequent inversion stability. Simultaneously, differential signals from the multi-frequency data are analyzed, with particular attention paid to whether high-frequency signals within the shallow 0–10 cm range exhibit abnormal scattering or amplitude attenuation, which are often signs of dielectric inhomogeneity caused by foreign matter such as straw.

[0042] In step 3, the radar's inherent signal characteristics are used to infer wave velocity. First, the average dielectric constant of the surface layer is measured using the direct wave. Due to the fixed spacing between the transmitting and receiving antennas, radar records contain both ground waves propagating directly through the surface layer and air waves propagating through the air. Air waves propagate at the speed of light, while ground waves propagate at the speed of the grounded medium. The arrival time difference between these two waves provides an estimate of the shallow dielectric constant. This method yields the effective dielectric constant of the surface layer (e.g., the first 5–10 cm). For deeper soil layers, a tomographic method is employed: Assuming that the soil dielectric constant varies smoothly with depth, the subsurface profile is divided into several thin layers, with the dielectric constant of each layer being the desired parameter. Using the primary reflection events recorded by the radar (e.g., reflections from bedrock interfaces) and the reflection waveforms from the multi-frequency data, the dielectric constant of each layer is iteratively optimized to ensure that the forward-calculated radar traveltime matches the measured value. Available techniques include radar traveltime inversion (based on traveltime fitting of reflections and direct waves) and full waveform inversion (using matching of the entire A-scan waveform). For example, at a given detection location, assuming a 1-meter soil layer thickness, we can modify the dielectric constant distribution so that the reflections from the bedrock interface in the multi-frequency data arrive consistently at different frequencies and match the waveform characteristics of each frequency (because different frequencies in a real medium should reflect the same layer depth). The dielectric constant profile obtained from the initial inversion lays the foundation for thickness calculation.

[0043] In step 3, a multi-level automatic determination is performed based on the second multi-frequency data to determine whether there is dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected. The specific steps include:

[0044] Step 31, obtaining a first multi-frequency feature by using the difference between adjacent second multi-frequency data, and obtaining a second multi-frequency feature by extracting the difference between the maximum data and the minimum data from the second multi-frequency data measured at the same frequency;

[0045] Step 32, based on the second multi-frequency data, a secondary determination is made as to whether there is a dielectric anomaly caused by straw debris. The step of performing a primary anomaly determination includes: obtaining a frequency fluctuation factor based on the ratio of the first multi-frequency feature and the second multi-frequency feature, and comparing the frequency fluctuation factor with a preset fluctuation threshold in turn. If the frequency fluctuation factor is greater than the preset fluctuation threshold, it is determined that there is straw debris on the surface; otherwise, it is determined that there is no straw debris on the surface.

[0046] In step 4, the specific steps to achieve real-time soil quality monitoring include:

[0047] The difference between the soil thicknesses of soil profiles at adjacent measuring points is used as the first quality monitoring factor, and the soil quality is evaluated based on the comparison result of the mean value of the first quality monitoring factor and the preset soil quality range; if the mean value of the first quality monitoring factor is within the preset soil quality range, the soil quality of the soil profile is qualified; if the mean value of the first quality monitoring factor is not within the preset soil quality range, the soil quality of the soil profile is unqualified.

[0048] If straw debris is detected on the surface, the dielectric constant is corrected using the dielectric mixture model; if straw debris is not detected on the surface, no correction is required.

[0049] If there is straw debris on the surface, the dielectric constant is corrected to obtain the actual dielectric constant of the overlying soil at each point, and then the average dielectric constant is used as the calibrated soil dielectric constant value.

[0050] Specifically, step 32 is to determine whether there is a dielectric anomaly caused by straw debris based on the second multi-frequency data. The specific steps of the secondary determination include:

[0051] Step 321: Obtain the first multi-frequency feature and the second multi-frequency data of the same measuring point as a first distribution factor, and obtain in real time a first mean of the first distribution factor and a second mean of the first multi-frequency feature of the measured point;

[0052] Step 322 : Using the ratio of the first mean value to the second mean value as a straw distribution factor, the straw distribution factor is compared with a preset distribution factor to determine whether dielectric anomalies caused by straw debris exist.

[0053] If the straw distribution factor is greater than the preset distribution factor, the secondary judgment is that there is a dielectric anomaly caused by straw debris; otherwise, the secondary judgment is that there is no dielectric anomaly caused by straw debris.

[0054] The primary discrimination (frequency fluctuation factor) performs an initial screening for mutations in the overall spectral response, quickly capturing strong interference caused by straw. The secondary discrimination (straw distribution factor) combines spatial distribution statistics to eliminate false alarms caused by accidental clutter or noise, ensuring that only areas with genuine straw are corrected. Both the frequency fluctuation threshold and the distribution factor can be dynamically updated based on the mean and variance of real-time statistics to adapt to different soil types, moisture contents, and on-site noise levels. The first discrimination focuses on detecting local mutation characteristics and can respond quickly at small scales. The second discrimination is based on cross-point distribution statistics and is more sensitive to weak but persistent straw effects, avoiding missing persistent interference in the first discrimination. The dielectric constant is corrected for areas where straw interference is truly present to avoid excessive interference in homogeneous soil. The introduction of the average dielectric constant takes into account both local anomalies and overall consistency, making the travel time-to-depth conversion more reliable and the depth error stably controlled within ±1 cm.

[0055] Example 2

[0056] The present invention can not only be used to identify abnormalities in straw debris, but can also be used for sandstone. The study selected tree, shrub, and grass sample slopes in the thin soil cover area of the sandstone area. The sample slope sizes were 32m×32m, 20×20m, and 16m×16m, respectively. Five survey lines were evenly distributed on the sample slope according to the slope width. The survey lines for tree, shrub, and grass sample slopes were spaced 8m, 5m, and 4m apart, respectively. A 400MHz frequency antenna was used in combination with the profile method for measurement, and the traditional excavation method was adopted for verification. A point was selected on each survey line for excavation to obtain the actual soil thickness, which was used to calibrate the radar to measure the soil thickness. The lateral distances between the points were roughly equal (the excavation points for trees, shrubs, and grass were spaced 8m, 5m, and 4m apart, respectively). Five excavation points were selected for each sample plot, and the excavation was carried out to 20cm below the sandstone layer. The longitude and latitude, slope, elevation, and soil thickness of each excavation profile point were recorded, as shown in Table 1. For the sake of convenience, the following procedures introduce the GPR parameter calibration and measurement process of the shrub plot.

[0057]

[0058] Table 1 Latitude and longitude, slope, elevation and soil thickness of each excavation profile point

[0059] In Table 1, G, Q, and C represent shrubland, treeland, and grassland, respectively.

[0060] At each measuring point, an iron pipe (length, 60 cm; inner diameter, 5.2 cm; outer diameter, 6 cm) was vertically driven into the Figure 3 The interface between the soil layer and the arsenic sandstone layer is shown.

[0061] The process of using ground penetrating radar and inputting parameters is as follows Figure 4 As shown:

[0062] The acquisition mode for this study is set to distance mode. Before setting the parameters, the distance wheel needs to be calibrated; set scan / m to "50-100"; sample / scan to "512"; record length to "40"; time zero to "manual"; delay to "-12"; surface to "0";

[0063] Set the gain to "auto"; set the gain curve points to "3-8"; set the IIR low pass to 800; the IIR high pass to 100; and the IIR overlap to between 3-5.

[0064] Because the dielectric constant of the soil overlying the arsenic sandstone could not be determined previously, the initial dielectric constant was set to "14" (between dry and wet soil) before measurement. After the dielectric constant was calibrated, the dielectric constant value was customized again.

[0065] Determine the dielectric constant of the soil overlying the arsenic sandstone based on the radar output image:

[0066] Due to the large difference in dielectric constants between the iron pipe, soil and sandstone, there will be an obvious jump phenomenon on the radar image. The top position of the "arch" corresponds to the actual position of the iron pipe, such as Figure 5 The radar image shown here shows the shrub plot before permittivity calibration. The radar image was imported into Radan7 software to calculate the two-way travel time for the electromagnetic wave to reach the iron pipe at each point on the shrub slope. Combined with field-measured soil thickness, the actual permittivity of the overlying soil at each point was determined. The average permittivity was then used as the calibrated soil permittivity value, resulting in a calculated value of 13.33.

[0067] The calibrated dielectric constant value was manually input through the "user-defined" mode, and each point was scanned again with the ground penetrating radar. The radar image was imported into the Radan7 software to read the depth value of the iron pipe. The radar image obtained after the dielectric constant calibration of the shrub sample plot is as follows: Figure 6 shown.

[0068] Comparing the iron pipe depth values obtained from the calibrated radar image with the measured iron pipe depth values, it was found that the accuracy of the thickness of the thin soil overlying the arsenic sandstone measured by GPR was between 86.15% and 97.97%, as shown in Table 2. The dielectric constant correction and GPR thickness measurement accuracy verification.

[0069]

[0070] Table 2 Dielectric constant correction and ground penetrating radar thickness measurement accuracy verification table

[0071] Thickness measurement by ground penetrating radar: The corrected dielectric constant is input into the ground penetrating radar, and then the soil thickness is measured along the survey line (20m long). The collected radar images of the survey line are imported into Radan7 software, and the position of the iron pipe is identified, and the "smart tracker" function is used to identify the soil-rock interface. Figure 7 As shown, the red dotted line represents the identified soil-rock interface.

[0072] Radan7 software not only identifies the soil and rock thickness, but also records the soil thickness values at each point on the survey line. The interpreted thickness values are exported to a CSV file and then plotted using Origin software. Figure 1 The three-dimensional distribution of soil thickness is shown.

[0073] The embodiment of the present invention uses a ground-penetrating radar with an integrated multi-frequency antenna to acquire multi-frequency data of a soil profile in real time along a survey line, preprocesses the multi-frequency data, aligns the multi-frequency data in time and space to obtain first multi-frequency data, fuses the first multi-frequency data according to corresponding spatial positions to obtain second multi-frequency data, and automatically determines whether dielectric anomalies caused by straw debris are present in the surface layer based on the second multi-frequency data, and corrects the dielectric constant, thereby eliminating wave velocity deviations caused by straw and air pores. After obtaining the corrected dielectric constant, the calculated soil thickness is associated with the spatial position at each position along the survey line to generate a soil thickness profile distribution, thereby realizing real-time soil quality monitoring, effectively identifying electromagnetic anomalies caused by straw debris or other shallow heterogeneous media, and significantly reducing the misjudgment and missed judgment rates.

[0074] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0075] Finally: 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, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A soil automatic measurement method based on ground penetrating radar, characterized in that: The steps include: Step 1: A ground penetrating radar with an integrated multi-frequency antenna acquires multi-frequency data of the soil profile in real time along the survey line; Step 2: preprocess the multi-frequency data, align the multi-frequency data in time and space to obtain first multi-frequency data, and fuse the first multi-frequency data according to corresponding spatial positions to obtain second multi-frequency data; Step 3: Based on the second multi-frequency data, a multi-level automatic determination is performed to determine whether there is dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected; Based on the second multi-frequency data, a multi-level automatic determination is performed to determine whether there is dielectric anomaly caused by straw debris on the surface, and the dielectric constant is corrected. The specific steps include: Step 31, obtaining a first multi-frequency feature by using the difference between adjacent second multi-frequency data, and obtaining a second multi-frequency feature by extracting the difference between the maximum data and the minimum data from the second multi-frequency data measured at the same frequency; Step 32, secondary determination is made based on the second multi-frequency data to determine whether there is a dielectric anomaly caused by straw debris. The step of performing the primary anomaly determination includes: obtaining a frequency fluctuation factor based on the ratio of the first multi-frequency feature and the second multi-frequency feature, and sequentially comparing the frequency fluctuation factor with a preset fluctuation threshold. If the frequency fluctuation factor is greater than the preset fluctuation threshold, it is determined that straw debris exists on the surface; otherwise, it is determined that there is no straw debris on the surface. Step 4: After obtaining the corrected dielectric constant, for each position along the survey line, the two-way time of the reflection event axis at the bottom of the soil layer is picked up in the ground penetrating radar profile. The average wave velocity of the dielectric mixing model at that position is used to convert it into soil thickness. The calculated soil thickness is associated with the spatial position to generate a soil thickness profile distribution, realizing real-time soil quality monitoring.

2. The automatic soil measurement method based on ground penetrating radar according to claim 1, characterized in that: In step 4, the specific steps to achieve real-time soil quality monitoring include: The difference between the soil thicknesses of soil profiles at adjacent measuring points is used as the first quality monitoring factor, and the soil quality is evaluated based on the comparison result of the mean value of the first quality monitoring factor and the preset soil quality range; if the mean value of the first quality monitoring factor is within the preset soil quality range, the soil quality of the soil profile is qualified; if the mean value of the first quality monitoring factor is not within the preset soil quality range, the soil quality of the soil profile is unqualified.

3. The automatic soil measurement method based on ground penetrating radar according to claim 1, characterized in that: If straw debris is detected on the surface, the dielectric constant is corrected using the dielectric mixture model; if straw debris is not detected on the surface, no correction is required.

4. The automatic soil measurement method based on ground penetrating radar according to claim 3, characterized in that: If there is straw debris on the surface, the dielectric constant is corrected to obtain the actual dielectric constant of the overlying soil at each point, and then the average dielectric constant is used as the calibrated soil dielectric constant value.

5. The automatic soil measurement method based on ground penetrating radar according to claim 1, characterized in that: Step 32: secondary determination is made based on the second multi-frequency data to determine whether dielectric anomalies caused by straw debris exist. The specific steps of the secondary determination include: Step 321: Obtain the first multi-frequency feature and the second multi-frequency data of the same measuring point as a first distribution factor, and obtain in real time a first mean of the first distribution factor and a second mean of the first multi-frequency feature of the measured point; Step 322 : Using the ratio of the first mean value to the second mean value as a straw distribution factor, the straw distribution factor is compared with a preset distribution factor to determine whether dielectric anomalies caused by straw debris exist.

6. The automatic soil measurement method based on ground penetrating radar according to claim 5, characterized in that: If the straw distribution factor is greater than the preset distribution factor, the secondary judgment is that there is a dielectric anomaly caused by straw debris; otherwise, the secondary judgment is that there is no dielectric anomaly caused by straw debris.

7. The automatic soil measurement method based on ground penetrating radar according to claim 1, characterized in that: In step 2, preprocessing includes time zero point correction, bandpass filtering to suppress environmental noise, and background subtraction to highlight underground target echoes; the multi-frequency data are aligned in time and space to obtain the first multi-frequency data: through interpolation or resampling, the waveforms of different frequencies have the same time sampling rate and starting time reference; in space, the position deviation of each frequency is corrected according to the antenna geometry configuration.

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