基于探地雷达的土体构型无损探测方法研究

土体构型对土壤水分、溶质运移过程和作物成长等有显著影响,常规测量采用人工挖土壤剖面取样、实验室化验分析等方法,其周期长效率低。针对以上问题,以探地雷达波形及其图像为研究对象,从检测土体构型的属性(层次、层厚、土质)入手,提出了一种使用探地雷达快速测量土壤土体构型的无损探测方法。基于探地雷达波形图像的纵向梯度信息能够反应土壤分层,采用包络检波法从探地雷达回波中提取包络信号,利用Hilbert分析其瞬时相位来确定分层位置;鉴于土壤介电常数与雷达回波振幅的关系,采用探地雷达回波振幅反演各层介电常数,由介电常数推算雷达波在土壤中的传播速度,以此得到土壤剖面各层厚度;根据探地雷达波形图的图像噪声与土壤砂壤比之间存在定量关系,提出采用主成分分析方法对每一层土质的图像进行噪声估计求得各层土壤的含砂量,结合支持向量机进而辨识各层土壤土质。建立涵盖地域信息、土壤指标、探测信息、图像多特征融合信息的土体构型知识库,并编制快速识别土壤土体构型的信息系统,在内蒙古自治区呼和浩特市周边2个试验基地、6个采样点、4类土壤土体构型使用该方法进行野外探测验证。研究表明:在上述地区地表以下1 m范围内的土体构型识别正确...

Nondestructive inspection method for soil profile configuration based on ground penetrating radar

Soil body configuration has a significant impact on soil moisture, solute transport process, and crop growth. Generally, measurements are performed using manual digging of the soil profile and sample collection, and then taking the samples to the laboratory for testing and analysis, etc., which has a low cycle length and low efficiency. To solve the aforementioned problems, by taking the ground penetrating radar (GPR) waveform and its image as the research object, and starting from the detection of the level, layer thickness, and soil quality of the soil structure, a nondestructive detection method is proposed to rapidly measure the soil body configuration using GPR. In this study, the longitudinal gradient information from the GPR waveform image can reflect soil stratification by using an envelope detection method to extract the envelope signal from the GPR echo and a Hilbert transform to analyze its instantaneous phase and determine the layer position. Given the relationship between the soil dielectric constant and the radar echo amplitude, this paper used the echo amplitude of the GPR to invert the dielectric constant of each layer, and then to calculate the propagation speed of the radar wave in the soil from the dielectric constant, so as to obtain the thickness of each layer of the soil profile. Based on the relationship between the image noise of the GPR waveform map and the soil sand to soil ratio, the principal component analysis method is used to evaluate the image noise for each layer of soil and obtain the sand content of each layer of soil, and combined with the support vector machine to identify the soil quality of each layer. The paper establish a knowledge base of soil structure covering regional information, soil indexes, detection information, and image multi-feature fusion information, and compile an information system for the rapid identification of soil and soil configuration, and use this method for field detection and verification in two test bases, six sampling points, and four types of soil and soil configurations around Hohhot City, Inner Mongolia Autonomous Region, China. Studies have shown that: (1) the correct rate of soil configuration recognition within 1 m below the surface of the above areas has reached more than 94%; (2) the relative measurement error of each layer of soil thickness detected is less than 10%. This research method provides a new mesoscopic detection concept for the rapid detection of soil configuration.