Method for measuring soil water content based on AEA and ARMA coupling of ground penetrating radar

Abstract

The invention relates to a method for measuring soil moisture content based on coupling of a ground penetrating radar early signal average amplitude envelope (AEA) method and an autoregressive moving average power spectrum (ARMA) method, and belongs to the field of soil moisture content measurement, and the method comprises the following implementation steps: (1) researching the relationship between the early signal average amplitude envelope and the soil moisture content; (2) researching the relationship between a radar signal power spectrum below an early signal time window and the soil moisture content, and exploring the effective detection depth of the AEA method for inverting the soil moisture content; (3) calculating a radar signal power spectrum by adopting ARMA (autoregressive moving average), and preferably selecting attributes which are closely related to the soil moisture content and are mutually independent according to a fitting relation between each attribute of the power spectrum and the soil moisture content and a cross-correlation relation among the attributes; (4) combining the effective detection depth of the AEA method, and selecting a proper Gaussian window to divide the radar signal to obtain power spectrums of different depths; and (5) inverting the soil moisture content according to the relationship between each power spectrum attribute and the soil moisture content obtained by BP neural network training. According to the method provided by the invention, nondestructive detection of the field scale soil moisture content can be realized, detection of the soil moisture content in the whole detection range of the ground penetrating radar can be realized, and the error of the moisture content is less than 1%.

Description

Technical Field

[0001] The present invention relates to a method for measuring soil moisture parameters, and in particular to a method for measuring soil moisture by coupling AEA and ARMA based on ground penetrating radar, which belongs to the field of geological radar application and can be used for detecting and estimating soil moisture in agriculture and municipal engineering. Background Art

[0002] Soil moisture content is an important parameter for evaluating soil quality and guiding the design of agricultural irrigation and water conservancy projects. Traditional soil moisture measurement methods have disadvantages such as invasiveness, point measurement, and high workload. Ground penetrating radar, as a high-resolution, non-destructive in-situ detection technology, has broad application prospects in the field of soil moisture measurement. However, the single ground penetrating radar signal analysis method has problems such as limited detection depth when measuring soil moisture content. The detection depth of AEA in detecting soil moisture content is relatively limited. Combined with the ARMA method, the inversion calculation of soil moisture rate in the entire detection depth range of ground penetrating radar can be realized. Summary of the invention

[0003] The present invention provides a method for non-destructively and rapidly acquiring soil moisture information based on a ground penetrating radar. The method uses an AEA method in combination with an ARMA method to predict shallow soil moisture content, and can detect soil moisture content within the entire detection range of the ground penetrating radar.

[0004] The technical solution adopted by the present invention to solve the technical problem is: a method for measuring soil moisture content based on AEA and ARMA coupling of ground penetrating radar, specifically comprising: A: Process the geological radar data, extract the early signal amplitude envelope (AEA) information, and fit the relationship between the inverse of the early signal amplitude envelope and the soil volume moisture content; B: Velocity analysis is performed on the radar profile to calculate the propagation velocity of ground waves in soils of different thicknesses, thereby determining the effective detection depth of the AEA method; C: The autoregressive moving average power spectrum method (ARMA) is used to calculate the power spectrum of the radar signal, and the soil moisture content below the ground wave depth is calculated based on the relationship between the various attributes of the power spectrum and the soil moisture content.

[0005] Furthermore, the step A comprises the following steps: A1: Perform basic processing such as zero point correction, background denoising, one-dimensional filtering, etc. on the radar data collected, and update the radar data; A2: Select the multi-channel data in the actual detected radar profile, perform Hilbert transform on the single-channel waveform, read the time window range corresponding to the first positive half cycle, perform amplitude envelope calculation, and calculate the average amplitude envelope inverse value of the selected multi-channel data; A3: Regression analysis is used to fit the relationship between the inverse of the amplitude envelope and the soil moisture content, and an equation for calculating the soil moisture content based on the amplitude envelope is obtained.

[0006] Further, the step B comprises the following steps: B1: Perform velocity analysis on the radar profile collected by the antenna to calculate the propagation speed of ground waves in soils of different thicknesses; B2: Calculate the ground wave wavelength corresponding to the soil detected by antennas of different frequencies according to the wavelength formula, and perform linear fitting between the effective detection depth and the wavelength to obtain fitting equation 1. Combined with the Topp empirical formula to fit the relationship between soil volume moisture content and the effective detection depth of the ground wave, obtain fitting equation 2; B2: According to fitting equation 2, the effective detection depth of the AEA method is calculated for each measuring point and the average is taken to obtain the effective detection depth of the AEA method.

[0007] Further, the step C comprises the following steps: C1: The radar signal power spectrum is calculated using the autoregressive moving average power spectrum method (ARMA). C2: Based on the fitting relationship between each attribute of the power spectrum and soil moisture content and the cross-correlation relationship between attributes, the attributes that are closely related to soil moisture content and independent of each other are selected; C3 determines the effective time window according to the effective detection depth of the AEA method, selects a suitable Gaussian window to divide the radar signal, and obtains the power spectrum at different depths; C4: Construct a BP neural network model, input the training data into the model for training, determine the number of hidden layers, the number of hidden layer nodes, the learning efficiency and the number of training times of the model, and establish the relationship between the power spectrum attribute and the soil moisture content; C5: Input the geological radar data of the soil moisture content to be tested into the trained BP neural network model and output the soil moisture content information.

[0008] The present invention has the following beneficial effects: The present invention uses ground penetrating radar as a platform, uses the early signal amplitude envelope method (AEA) combined with the autoregressive moving average power spectrum method (ARMA) based on BP neural network, and the coupling of the two methods can invert the soil moisture content in the entire ground penetrating radar detection depth range. It has the advantages of large-scale continuous detection, fast, non-destructive, and low cost, which can make up for the shortcomings of traditional measurement methods that are low in efficiency and cause damage to the soil.

[0009] The present invention will be described in more detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS The following is a brief description of the contents and symbols in the drawings of this specification: Figure 1 This is the radar waveform diagram of the excavation profile in the example of the present invention Figure 2 This is a comparison chart of the measured soil moisture content and the soil moisture content inverted by the AEA method in the example of the present invention. Figure 3 It is an error analysis diagram of soil moisture content inverted by radar power spectrum combined with BP neural network in the example of the present invention; Figure 4 It is the outdoor verification ground penetrating radar power spectrum combined with the BP neural network output regression line in the example of the present invention.

Claims

1. A method for measuring soil moisture content based on AEA and ARMA coupling of ground penetrating radar, characterized in that: The following steps are involved: A: Process the geological radar data, extract the early signal amplitude envelope (AEA) information, and fit the relationship between the inverse of the early signal amplitude envelope and the soil volume moisture content; B: Velocity analysis is performed on the radar profile to calculate the propagation velocity of ground waves in soils of different thicknesses, thereby determining the effective detection depth of the AEA method; C: The autoregressive moving average power spectrum method (ARMA) is used to calculate the power spectrum of the radar signal, and the soil moisture content below the ground wave depth is calculated based on the relationship between the various attributes of the power spectrum and the soil moisture content.

2. The method for measuring soil moisture content by coupling AEA and ARMA based on ground penetrating radar according to claim 1, characterized in that: The step A comprises the following steps: A1: Perform basic data processing on radar data. Select multi-channel data from the actual detected radar profile and perform Hilbert transform on the single-channel waveform. Read the time window range corresponding to the first positive half cycle, perform amplitude envelope calculation, and calculate the average amplitude envelope inverse value of the selected multi-channel data. Perform regression analysis to fit the relationship between the inverse of the amplitude envelope and soil moisture content, and obtain the equation for calculating soil moisture content based on the amplitude envelope.

3. According to claim 1, the velocity analysis of the radar profile is performed to calculate the propagation velocity of the ground wave in the soil with different layer thickness and different water content, so as to determine the effective detection depth of the AEA method, characterized in that: The step B comprises the following steps: B1: Perform velocity analysis on the radar profile collected by the antenna to calculate the propagation velocity of ground waves in soils of different thicknesses and moisture contents; B2: Calculate the wavelength of the ground wave corresponding to the soil detected by antennas of different frequencies according to the wavelength formula, perform a linear fit between the effective detection depth and the wavelength, and obtain fitting equation 1. Combine the Topp empirical formula to fit the relationship between soil volume moisture content and the effective detection depth of the ground wave, and obtain fitting equation 2.

4. The method for measuring soil moisture content based on ARMA of ground penetrating radar according to claim 1, characterized in that: The step C comprises the following steps: C1: The power spectrum of radar signals is calculated using the autoregressive moving average power spectrum method (ARMA). Based on the fitting relationship between each attribute of the power spectrum and soil moisture content and the cross-correlation relationship between attributes, the attributes that are closely related to soil moisture content and are independent of each other are selected; C2: Determine the effective time window according to the effective detection depth of the AEA method, select a suitable Gaussian window to divide the radar signal, and obtain the power spectrum at different depths; C3: Construct a BP neural network model, input the training data into the model for training, determine the number of hidden layers, the number of hidden layer nodes, the learning efficiency and the number of training times of the model, and establish the relationship between the power spectrum attribute and the soil moisture content; C4: Input the geological radar data of the soil moisture content to be tested into the trained BP neural network model and output the soil moisture content information.

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