Underground Diagnosis Based on GPR and Learning in the Model Space
This work addresses underground diagnosis for pipeline detection and anomaly identification, representing an incremental improvement in automating GPR data analysis.
The paper tackles the challenge of automatically identifying underground structures from Ground Penetrating Radar (GPR) data by proposing a method based on learning in the model space, using a 2D Echo State Network to fit image segments, and demonstrates its effectiveness through experiments on real-world datasets.
Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or abnormals automatically. In this paper, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model.