Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks
Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks
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DOI：
10.1029/2022wr033895
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发表时间：
2023-02
影响因子：
5.4
通讯作者：
Zhuangji Wang;Shan Hua;D. Timlin;Yuki Kojima;Songtao Lu;Wenguang Sun;D. Fleisher;R. Horton;V. Reddy;Katherine Tully 2
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文献类型：
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作者：
Zhuangji Wang;Shan Hua;D. Timlin;Yuki Kojima;Songtao Lu;Wenguang Sun;D. Fleisher;R. Horton;V. Reddy;Katherine Tully 2
解释在含水量不均匀的土壤中获得的时域反射仪（TDR）波形是一个悬而未决的问题。本文设计了一种基于卷积神经网络的TDR波形解释模型，该模型能够揭示土壤相对介电常数和含水量沿着TDR传感器的空间变化。所提出的模型，即TDR-CNN，由三个模块构成。首先，用简化的VGG 16网络提取TDR波形的几何特征。其次，在TDR波形中的反射位置被跟踪使用1D版本的区域建议网络。最后，通过CNN回归网络估计土壤相对介电常数值。这三个模块使用Google TensorFlow和Keras API在Python中开发，然后堆叠在一起以制定TDR-CNN架构。每个模块都是单独训练的，并且可以自动促进模块之间的数据传输。使用具有不同相对介电常数但在相对稳定的土壤电导率下的模拟TDR波形评估TDR-CNN，并显示TDR-CNN的准确性和稳定性。来自水渗透研究的TDR测量提供了TDR-CNN的应用以及TDR-CNN和逆模型之间的比较。所提出的TDR-CNN模型易于实现，并且TDR-CNN中的模块可以使用新的数据集单独更新或微调。总之，TDR-CNN提出了一种模型架构，可用于解释在含水量分布不均匀的土壤中获得的TDR波形。
Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution.