Water Center, The


Date of this Version



Wang, Z., Hua, S., Timlin, D., Kojima, Y., Lu, S., Sun, W., et al. (2023). Time domain reflectometry waveform interpretation with convolutional neural networks. Water Resources Research, 59, e2022WR033895. https://doi. org/10.1029/2022WR033895


U.S. Government employees and their work is in the public domain in the USA.


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.