Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks

Abstract

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.