Automatic soil measurement method based on ground penetrating radar

Abstract

The invention relates to the technical field of soil quality analysis, and particularly discloses an automatic soil measurement method based on a ground penetrating radar, which comprises the following steps: acquiring multi-frequency data of a soil profile in real time along a measuring line by integrating the ground penetrating radar of a multi-frequency antenna, preprocessing the multi-frequency data, aligning the multi-frequency data in time and space to obtain first multi-frequency data, and calculating the first multi-frequency data according to the first multi-frequency data; performing fusion processing on the first multi-frequency data according to the corresponding spatial position to obtain second multi-frequency data, automatically judging whether dielectric abnormity caused by straw chippings exists on the surface layer or not based on the second multi-frequency data, correcting the dielectric constant, and after the corrected dielectric constant is obtained, determining whether the dielectric abnormity exists on each position along the measuring line or not; and associating the calculated soil thickness with the spatial position to generate 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. More specifically, the present invention relates to an automatic soil measurement method based on ground penetrating radar. Background Art

[0002] Ground penetrating radar has been widely used in fields such as soil thickness measurement, underground pipeline detection, archaeology, and engineering investigation. Its basic principle is to utilize the characteristic that high-frequency electromagnetic waves generate reflections at the interfaces of different media. By transmitting a narrow pulse signal into the ground through a transmitting antenna and capturing the reflected wave by a receiving antenna, and then inferring the position and shape of the underground structure based on the two-way travel time and propagation speed of the reflected wave. Existing literature (Liu Chang, Liu Qin, Zhang Qiong, Dan Chenxi, Liu Gang. Exploring the influence of soil water content and bulk density on soil dielectric constant and the feasibility of measuring the thickness of black soil by ground penetrating radar [J]. Acta Pedologica Sinica, 2024, 61(4): 952–963.) explored the influence of soil water content and bulk density on soil dielectric constant and the feasibility of measuring black soil thickness by ground penetrating radar, and gave the Figure 2 ground penetrating radar detection mode diagram as shown. However, in practical applications, there are many challenges in the accurate measurement of soil quality.

[0003] In the straw returning area, the surface soil has changed from a single mixture of mineral particles and water to a multi-phase medium system composed of soil particles, straw debris, and air pores intertwined. This medium inhomogeneity will introduce systematic errors in the sounding of ground penetrating radar (GPR). The specific manifestations are as follows: 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 speed locally increases, and the travel time of the reflection interface appears in advance. Since GPR data processing usually adopts a single average wave speed model and directly converts the travel time at different positions into depth without considering the spatial variation of wave speed caused by straw distribution, high-frequency depth fluctuations will be observed on adjacent survey lines, resulting in errors in soil quality assessment, affecting the precise positioning of geological profiles and hidden targets, and also interfering with the quantitative inversion of water 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 discriminates whether there is dielectric anomaly caused by straw debris on the surface based on multi-frequency data and real-time monitors the soil quality, and is used to solve the problem that the prior art does not consider the influence of the spatial variation of wave speed caused by straw distribution on the measurement of soil thickness, can effectively identify electromagnetic anomalies caused by straw debris or other shallow inhomogeneous media, and significantly reduce the misjudgment and missed judgment rates, so as to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: An automatic soil measurement method based on ground penetrating radar, comprising the following steps: Step 1, a ground penetrating radar integrating multi-frequency antennas is used to obtain multi-frequency data of the soil profile in real time along the measuring line; Step 2, preprocess the multi-frequency data, align the multi-frequency data in time and space to obtain the first multi-frequency data, and perform fusion processing on the first multi-frequency data according to the corresponding spatial positions to obtain the second multi-frequency data; Step 3, based on the second multi-frequency data, perform multi-level automatic discrimination on whether there is a dielectric anomaly caused by straw debris on the surface layer, and correct the dielectric constant; Step 4, after obtaining the corrected dielectric constant, for each position along the measuring line, pick up the two-way travel time of the bottom reflection isochrone of the soil layer in the ground penetrating radar profile, and use the average wave velocity of the dielectric mixing model at this position to convert it into the soil thickness, associate the calculated soil thickness with the spatial position, generate the soil thickness profile distribution, and realize real-time thickness monitoring.

[0006] In Step 1, it runs with a fixed transmitting and receiving spacing (common offset configuration), continuously collects data during travel, and realizes near-real-time monitoring. Since multi-antenna or multiple scans are used to obtain data of each frequency, it is necessary to ensure that the spatial positions of different frequency profiles correspond one by one.

[0007] As a further solution of the present invention, in Step 3, based on the second multi-frequency data, perform multi-level automatic discrimination on whether there is a dielectric anomaly caused by straw debris on the surface layer, and correct the dielectric constant. The specific steps include: Step 31, obtain the first multi-frequency feature through the difference between adjacent second multi-frequency data, and obtain the second multi-frequency feature by extracting the difference between the maximum data and the minimum data measured at the same frequency from the second multi-frequency data; Step 32, according to the second multi-frequency data, perform secondary discrimination on whether there is a dielectric anomaly caused by straw debris. The steps for performing primary anomaly discrimination include: obtaining the frequency fluctuation factor according to the ratio of the first multi-frequency feature and the second multi-frequency feature, and successively comparing the frequency fluctuation factor with the preset fluctuation threshold. If the frequency fluctuation factor is greater than the preset fluctuation threshold, it is primarily determined that there is straw debris on the surface layer; otherwise, it is primarily determined that there is no straw debris on the surface layer.

[0008] As a further solution of the present invention, in Step 4, the specific steps for realizing real-time soil quality monitoring include: Taking the difference between the soil thicknesses of adjacent measuring points in the soil profile as the first quality monitoring factor, and evaluating the soil quality based on the comparison result between 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.

[0009] As a further solution of the present invention, if straw debris is detected on the surface layer in the first discrimination, the dielectric constant is corrected by the dielectric mixing model; if there is no straw debris on the surface layer, no correction is required for the first discrimination.

[0010] As a further solution of the present invention, if there is straw debris on the surface layer to correct the dielectric constant, the actual dielectric constant of the overlying soil at each point is obtained, and then the average dielectric constant is used as the calibrated soil dielectric constant value.

[0011] As a further solution of the present invention, in step 32, it is secondarily discriminated whether there is a dielectric anomaly caused by straw debris according to the second multi-frequency data. The specific steps of the secondary discrimination include: Step 321, obtaining the first multi-frequency feature and the second multi-frequency data of the same measuring point as the first distribution factor, and obtaining the first mean value of the first distribution factor of the measured measuring points and the second mean value of the first multi-frequency feature in real time; Step 322, using the ratio of the first mean value and the second mean value as the straw distribution factor, comparing the straw distribution factor with the preset distribution factor to determine whether there is a dielectric anomaly caused by straw debris.

[0012] As a further solution of the present invention, if the straw distribution factor is greater than the preset distribution factor, it is secondarily determined that there is a dielectric anomaly caused by straw debris; otherwise, it is secondarily determined that there is no dielectric anomaly caused by straw debris.

[0013] As a further solution of the present invention, in step 2, the preprocessing includes time zero correction, band-pass filtering to suppress environmental noise, and background subtraction to highlight the underground target echo; the multi-frequency data is 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 geometric configuration.

[0014] Technical effects and advantages of a soil automatic measurement method based on ground penetrating radar according to the present invention: By integrating a ground penetrating radar with multi-frequency antennas, multi-frequency data of the soil profile is obtained in real time along the survey line. The multi-frequency data is preprocessed, and the multi-frequency data is aligned in time and space to obtain first multi-frequency data. The first multi-frequency data is fusion-processed according to the corresponding spatial positions to obtain second multi-frequency data. Based on the second multi-frequency data, it is automatically determined whether there is dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected, which can eliminate the wave velocity deviation caused by straw and air pores. After obtaining the corrected dielectric constant, for each position along the survey line, the calculated soil thickness is associated with the spatial position to generate a soil thickness profile distribution, realizing real-time soil quality monitoring, which can effectively identify electromagnetic anomalies caused by straw debris or other shallow heterogeneous media, and significantly reduce the misjudgment and omission rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a three-dimensional spatial distribution diagram of soil thickness provided by the present invention; Figure 2 It is a detection mode diagram of the ground penetrating radar provided by the present invention; Figure 3 It is an image at the interface between the soil layer and the arsene sandstone layer provided by the present invention; Figure 4 It is a flowchart of the use and parameter input of the ground penetrating radar provided by the present invention; Figure 5 It is a radar image obtained before dielectric constant calibration of the shrub plot provided by the present invention; Figure 6 It is a radar image obtained after dielectric constant calibration of the shrub plot provided by the present invention; Figure 7 It is the "intelligent tracker" function provided by the present invention to identify the soil-rock interface. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the accompanying drawings in the present invention. Obviously, the described technical solutions are only a part of the present invention, rather than all of them. Based on the technical solutions in the present invention, all other technical solutions obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

[0017] Embodiment 1

[0018] A soil automatic measurement method based on ground penetrating radar includes the following steps: Step 1, integrate a ground penetrating radar with multi-frequency antennas, and obtain multi-frequency data of the soil profile in real time along the survey line; Step 2: Perform preprocessing on the multi-frequency data, align the multi-frequency data in time and space to obtain the first multi-frequency data, and perform fusion processing on the first multi-frequency data according to the corresponding spatial positions to obtain the second multi-frequency data; Step 3: Automatically determine whether there is dielectric anomaly caused by straw debris on the surface layer based on the second multi-frequency data, and correct the dielectric constant; Step 4: After obtaining the corrected dielectric constant, for each position along the survey line, pick up the two-way travel time of the bottom reflection event of the soil layer in the ground penetrating radar profile , and use the average wave velocity of the dielectric mixing model at this position to convert it into the soil thickness , associate the calculated soil thickness with the spatial position, generate the profile distribution of the soil thickness, and realize real-time thickness monitoring.

[0019] It should be noted that in Step 1, it runs with a fixed transmitting and receiving spacing (common offset configuration), continuously collects data during movement, and realizes near-real-time monitoring. Since multi-antenna or multiple scans are used to obtain data of each frequency, it is necessary to ensure that the spatial positions of different frequency profiles correspond one by one.

[0020] In Step 2, the preprocessing includes time zero correction (removing the delay of the antenna direct pulse), band-pass filtering to suppress environmental noise, and background subtraction to highlight the underground target echo; the multi-frequency data is 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, correct the position deviation of each frequency B-scan according to the antenna geometric configuration.

[0021] The fusion processing includes sliding window fusion, empirical mode decomposition or wavelet transform fusion. For example, at each spatial position, the reflection signals of high, medium and low frequencies can be added according to certain weights to form a "fusion A-scan". The weights can be optimized according to the signal-to-noise ratio, resolution, etc. (even automatic optimization methods such as genetic algorithms can be used to assign weights). The fused signal contains comprehensive information of each frequency band, which is beneficial to the stability of subsequent inversion. At the same time, analyze the difference signal of the multi-frequency data: pay special attention to whether there is abnormal scattering or amplitude attenuation of the high-frequency signal within the shallow 0-10 cm, which is often an indication of dielectric inhomogeneity caused by heterogeneous substances such as straw.

[0022] In Step 3, the wave velocity is inferred using the signal characteristics of the radar itself. First, the average dielectric constant of the surface layer is measured using the direct wave: Since there is a fixed spacing between the antenna transmission and reception, both the ground wave that propagates directly through the surface layer and the air wave that propagates through the air will appear in the radar record. The air wave propagates at the speed of light, and the ground wave propagates at the velocity of the grounded medium. The time difference between their arrivals provides an estimate of the shallow dielectric constant. In this way, the effective dielectric constant of the surface layer (e.g., the top 5–10 cm) can be obtained. For the deep soil layer, the tomography method is adopted: Assuming that the dielectric constant of the soil varies smoothly with depth, the underground profile can be divided into several thin layers, and the dielectric constant of each layer is taken as the parameter to be determined. Using the main reflection events recorded by the radar (e.g., the reflection from the bedrock interface) and the reflection waveforms in the multi-frequency data, the dielectric constant of each layer is iteratively optimized to make the travel time of the radar wave calculated by forward modeling match the measured value. The selectable techniques include radar travel time inversion (fitting the travel times of the reflection and the direct wave) and full waveform inversion (using the entire A-scan waveform matching). For example, for a certain detection location, assuming the soil layer thickness is 1 m, we can change the dielectric constant distribution to make the arrival times of the reflections from the bedrock interface in the multi-frequency data consistent at different frequencies and match the waveform characteristics of each frequency (because different frequencies should reflect the same layer depth under the real medium). The dielectric constant profile obtained by the initial inversion lays the foundation for the thickness calculation.

[0023] In Step 3, based on the second multi-frequency data, a multi-level automatic discrimination is performed on whether there is a dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected. The specific steps include: Step 31: Obtain the first multi-frequency feature by taking the difference between adjacent second multi-frequency data, and obtain the second multi-frequency feature by extracting the difference between the maximum data and the minimum data measured at the same frequency from the second multi-frequency data. Step 32: According to the second multi-frequency data, perform a secondary discrimination on whether there is a dielectric anomaly caused by straw debris. The steps for the primary anomaly discrimination include: Obtain the frequency fluctuation factor according to the ratio of the first multi-frequency feature and the second multi-frequency feature, and compare the frequency fluctuation factor with the preset fluctuation threshold in turn. If the frequency fluctuation factor is greater than the preset fluctuation threshold, it is discriminated that there is straw debris on the surface layer in the primary discrimination; otherwise, it is discriminated that there is no straw debris on the surface layer in the primary discrimination.

[0024] In Step 4, the specific steps for realizing real-time soil quality monitoring include: Take the difference between the soil thicknesses of adjacent soil profiles at the measurement points as the first quality monitoring factor, and evaluate the soil quality based on the comparison result between 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.

[0025] If straw debris is detected on the surface during the first discrimination, the dielectric constant is corrected using the dielectric mixing model; if there is no straw debris on the surface, no correction is required for the first discrimination.

[0026] If straw debris is present on the surface and the dielectric constant is corrected, the actual dielectric constant of the overlying soil at each point is obtained, and then the average dielectric constant is used as the calibrated soil dielectric constant value.

[0027] Specifically, in step 32, based on the second multi-frequency data, a secondary discrimination is performed to determine whether there is a dielectric anomaly caused by straw debris. The specific steps of the secondary discrimination include: Step 321: Obtain the first multi-frequency feature and the second multi-frequency data at the same measurement point as the first distribution factor, and in real-time obtain the first mean of the first distribution factor of the measured measurement points and the second mean of the first multi-frequency feature. Step 322: Use the ratio of the first mean and the second mean as the straw distribution factor, and compare the straw distribution factor with a preset distribution factor to determine whether there is a dielectric anomaly caused by straw debris.

[0028] If the straw distribution factor is greater than the preset distribution factor, it is secondarily determined that there is a dielectric anomaly caused by straw debris; otherwise, it is secondarily determined that there is no dielectric anomaly caused by straw debris.

[0029] The first discrimination (frequency fluctuation factor) performs a preliminary screening on the sudden change in the overall frequency spectrum response to quickly capture strong interference caused by straw. The second discrimination (straw distribution factor) combines spatial distribution statistics to eliminate false alarms caused by accidental clutter or noise, ensuring that only true straw areas are corrected; both the frequency fluctuation threshold and the distribution factor can be dynamically updated according to the mean and variance statistically calculated in real-time to adapt to different soil types, moisture contents, and on-site noise levels; the first discrimination focuses on the detection of local mutation characteristics and can respond quickly at a small scale, while the second discrimination is more sensitive to weak but persistent straw effects based on cross-measurement point distribution statistics, avoiding omissions of persistent interference in the first discrimination; the dielectric constant of areas with true straw interference is corrected to avoid over-interference with homogeneous soil. The introduction of the average dielectric constant takes into account both local anomalies and overall consistency, making the travel time-depth conversion more reliable, and the depth error can be stably controlled within ±1 cm.

[0030] Example 2

[0031] The present invention can be used not only for the abnormal discrimination of straw debris, but also for the arid sandy rock. It is studied to select tree, shrub, and grass sample slopes in the thin soil covered area of the arid sandy rock area. The sizes of the sample slopes are 32m×32m, 20×20m, and 16m×16m respectively. Five survey lines are evenly arranged on the sample slopes according to the slope width. The survey lines of the tree, shrub, and grass sample slopes are spaced 8m, 5m, and 4m apart respectively. Measurement is carried out using an antenna with a frequency of 400MHz in combination with the profile method, and the traditional excavation method is used for verification. One point is selected on each survey line for excavation to obtain the actual soil thickness, which is used to calibrate the soil thickness measured by the radar. The lateral distance between the points is approximately equal (the excavation points of the tree, shrub, and grass are spaced 8m, 5m, and 4m apart respectively). Five excavation points are selected in each sample plot and excavated to 20cm below the arid sandy rock layer. Record the longitude, latitude, slope, elevation, and soil thickness of each excavation profile point, as shown in Table 1. For the convenience of specific description, the following process will introduce the ground penetrating radar parameter calibration and measurement process of the shrub sample plot.

[0032]

[0033] Table 1 Longitude, latitude, slope, elevation, and soil thickness of each excavation profile point In Table 1, G, Q, and C represent shrub land, tree land, and grass land respectively.

[0034] At each excavation profile point of the measurement point, a steel pipe (length, 60cm; inner diameter, 5.2cm; outer diameter, 6cm) is vertically driven into the interface between the soil layer and the arid sandy rock layer as Figure 3 shown.

[0035] The process of using the ground penetrating radar and inputting parameters is as Figure 4 shown: In this study, the acquisition mode is set to the distance mode. Before parameter setting, the ranging wheel needs to be calibrated; the setting of scans / meter is "50 - 100"; the sampling / scan is set to "512"; the record length is set to "40"; the time zero point is set to "manual"; the delay is "-12"; the surface is "0"; the gain is set to "automatic"; the gain curve points are set to "3 - 8"; the IIR low pass is set to 800; the IIR high pass is set to 100; the IIR stacking is between 3 - 5.

[0036] Since the dielectric constant of the soil overlying the arid sandy rock could not be determined previously, before measurement, the initial dielectric constant is set to "14" (between dry soil and wet soil). After the dielectric constant is calibrated, the dielectric constant value can be re-customized.

[0037] Determine the dielectric constant value of the soil overlying the arid sandy rock according to the radar output image: Due to the large difference in dielectric constants between the iron pipe and soil and arsenic sandstone, there will be obvious jumps 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 obtained before the dielectric constant calibration of the shrub sample plot is shown. The radar image was imported into the Radan7 software to obtain the two-way travel time of the electromagnetic wave reaching the iron pipe position at each point of the shrub sample slope. Combined with the measured soil thickness obtained in the field, the actual dielectric constant of the overlying soil at each point was obtained, and then the average dielectric constant was used as the calibrated soil dielectric constant value. After calculation, the calibrated dielectric constant value was "13.33".

[0038] 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.

[0039] By comparing the iron pipe depth value obtained from the calibrated radar image with the measured iron pipe depth value, it is found that the accuracy of the thickness of the thin soil overlying the arsenic sandstone measured by the ground penetrating radar is between 86.15% and 97.97%, as shown in Table 2. The dielectric constant correction and ground penetrating radar thickness measurement accuracy verification.

[0040]

[0041] Table 2 Dielectric constant correction and ground penetrating radar thickness measurement accuracy verification table GPR thickness measurement: The corrected dielectric constant is input into the GPR, and then the soil thickness is measured along the survey line (20m long). The collected radar images of the survey line are imported into the Radan7 software, and the soil-rock interface is identified by identifying the position of the iron pipe and using the "Smart Tracker" function. Figure 7 As shown, the red dotted line represents the identified soil-rock interface.

[0042] 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 drawn using Origin software. Figure 1 The three-dimensional distribution of soil thickness is shown.

[0043] In the embodiment of the present invention, a ground penetrating radar integrating a multi-frequency antenna is used to obtain multi-frequency data of a soil profile in real time along a survey line. The multi-frequency data is preprocessed, and the multi-frequency data is aligned in time and space to obtain first multi-frequency data. The first multi-frequency data is fused according to the corresponding spatial positions to obtain second multi-frequency data. Based on the second multi-frequency data, it is automatically determined whether there is a dielectric anomaly caused by straw debris on the surface layer, and the dielectric constant is corrected, so as to eliminate the wave velocity deviation caused by straw and air pores. After obtaining the corrected dielectric constant, for each position along the survey line, the calculated soil thickness is associated with the spatial position to generate a soil thickness profile distribution, realizing real-time soil quality monitoring. It can effectively identify electromagnetic anomalies caused by straw debris or other shallow heterogeneous media, and significantly reduce the false judgment and omission judgment rates.

[0044] As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0045] Finally: The above is only the preferred solution of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automatic soil measurement method based on ground penetrating radar, characterized in that, It includes the following steps: Step 1, a ground penetrating radar integrating a multi-frequency antenna obtains multi-frequency data of the soil profile in real time along the survey line; Step 2, perform preprocessing on the multi-frequency data, align the multi-frequency data in time and space to obtain the first multi-frequency data, and perform fusion processing on the first multi-frequency data according to the corresponding spatial positions to obtain the second multi-frequency data; Step 3, based on the second multi-frequency data, perform multi-level automatic discrimination on whether there is a dielectric anomaly caused by straw debris on the surface layer, and correct the dielectric constant; Step 4, after obtaining the corrected dielectric constant, for each position along the survey line, pick up the two-way travel time of the bottom reflection isochrone of the soil layer in the ground penetrating radar profile, and use the average wave velocity of the dielectric mixing model at this position to convert it into the soil thickness, associate the calculated soil thickness with the spatial position, generate the soil thickness profile distribution, and realize 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 3, based on the second multi-frequency data, perform multi-level automatic discrimination on whether there is a dielectric anomaly caused by straw debris on the surface layer, and correct the dielectric constant. The specific steps include: Step 31, obtain the first multi-frequency feature by the difference between adjacent second multi-frequency data, and obtain the second multi-frequency feature by extracting the difference between the maximum data and the minimum data measured from the second multi-frequency data at the same frequency; Step 32, according to the second multi-frequency data, perform secondary discrimination on whether there is a dielectric anomaly caused by straw debris. The steps for the first anomaly discrimination include: obtain the frequency fluctuation factor according to the ratio of the first multi-frequency feature and the second multi-frequency feature, and compare the frequency fluctuation factor with the preset fluctuation threshold in turn. If the frequency fluctuation factor is greater than the preset fluctuation threshold, it is discriminated for the first time that there is straw debris on the surface layer; otherwise, it is discriminated for the first time that there is no straw debris on the surface layer.

3. A soil automatic measurement method based on ground penetrating radar according to claim 1, characterized in that In Step 4, the specific steps for realizing real-time soil quality monitoring include: Use the difference between the soil thicknesses of adjacent measurement points' soil profiles as the first quality monitoring factor, and evaluate the soil quality based on the comparison result between 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.

4. The automatic soil measurement method based on ground penetrating radar according to claim 2, wherein If it is discriminated for the first time that there is straw debris on the surface layer, correct the dielectric constant through the dielectric mixing model; if there is no straw debris on the surface layer, no correction is required.

5. The automatic soil measurement method based on ground penetrating radar according to claim 4, wherein If the dielectric constant is corrected due to the existence of straw debris on the surface layer, obtain the actual dielectric constant of the overlying soil at each point, and then use the average dielectric constant as the calibrated soil dielectric constant value.

6. The automatic soil measurement method based on ground penetrating radar according to claim 2, characterized in that Step 32, according to the second multi-frequency data, perform secondary discrimination on whether there is a dielectric anomaly caused by straw debris. The specific steps for the secondary discrimination include: Step 321, obtain the first multi-frequency feature and the second multi-frequency data of the same measurement point as the first distribution factor, and obtain the first mean value of the first distribution factor of the measured measurement points in real time and the second mean value of the first multi-frequency feature; Step 322: Use the ratio of the first mean value and the second mean value as the straw distribution factor, compare the straw distribution factor with the preset distribution factor, and determine whether there is dielectric anomaly caused by straw debris.

7. A soil automatic measurement method based on ground penetrating radar according to claim 6, characterized in that, If the straw distribution factor is greater than the preset distribution factor, it is secondarily determined that there is dielectric anomaly caused by straw debris; otherwise, it is secondarily determined that there is no dielectric anomaly caused by straw debris.

8. A method for automatic soil measurement based on ground penetrating radar according to claim 1, characterized in that In step 2, the preprocessing includes time zero point correction, band-pass 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; spatially, the position deviation of each frequency is corrected according to the antenna geometric configuration.

Images