Cross-Hole GPR for Soil Moisture Estimation Using Deep Learning

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (9), p.2397
• Main Authors: Pongrac, Blaž, Gleich, Dušan, Malajner, Marko, Sarjaš, Andrej
• Format: Article
• Language: English
• Subjects: Analysis; Antenna design; Antennas; Artificial neural networks; Automation; Bandwidths; Boreholes; Canals; Change detection; convolutional neural network; cross-hole; Data acquisition; Data processing; Datasets; Deep learning; Design; Diodes; Feasibility studies; Fourier transforms; Frequencies; ground penetrating radar; L-band; Marx generators; Moisture effects; Neural networks; Pulse generators; Radar; Radar systems; Remote sensing; Soil moisture; soil moisture estimation; Tomography; Training; Transistors; Wave propagation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15092397

This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas.