# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 1 Introduction  
Road surface and subsurface distress significantly affect the service life and driving comfort [1]. Therefore, it is necessary to detect distress for road maintenance. Cavity and crack are two typical road subsurface distresses. Many non-destructive testing techniques have been applied to detect these distresses [2, 3]. Compared with the other non-destructive testing (NDT) techniques, ground penetrating radar (GPR) has the advantages of high speed, high precision, and low cost, and it has been one of the most wildly used geophysical prospecting methods [4]. The GPR device transmits electromagnetic waves (EW) through the subsurface medium by transmitting antennae, while the receiving antennae receive the signals reflected from anomalies in the subsurface. Meanwhile, the computer software system records the two-way travel time and the signal strength (amplitude). The GPR image is obtained when the amplitude intensity of the multiple signals is expressed in color or grayscale. The EW emitted by the GPR contains 10-5000 MHz [5], also commonly referred to ultra-wideband (UWB) radio wave device. The operating frequency band of the GPR device contains a variety of communication signals, so it is unavoidable to interfere with the external environment, which will cause the received signal to be accompanied by a large number of undesired signals. Additionally, the GPR signal attenuates when traveling through the subsurface medium because of the geometrical spreading losses of the EW waves [6]. Therefore, signal pre-processing methods are usually required by experiencedtechnicians to improve the signal-to-noise ratio and eliminate wavelet attenuation. The efficiency and accuracy of GPR data interpretation are highly dependent on experienced technicians, which cannot be guaranteed when the amount of data is large [7]. Therefore, the automatic identification of GPR data is of great significance to its development. Various algorithms for the automatic detection and interpretation of GPR data have been proposed and successfully applied. The initial automatic identification of the object mainly utilized the electromagnetic wave theory and rule-based algorithms for the identification of special graphs, such as drawing the normalized amplitude value fluctuation map of the A-scan single-channel signal to determine the approximate location of the target [6], and the identification of the hyperbolic curve formed by the target echo based on the Hough transform and least squares method in the B-scan image [8]. Although the rule-based recognition algorithm performs better under ideal conditions such as numerical simulations and indoor models, the EWs are seriously distorted by the interference of the environment in actual engineering, and it is difficult to separate the target well from the background noise. The development of digital signal and image processing techniques has further contributed to the automatic identification of GPR data [9]. Many advanced machine learning classifiers have been proposed to construct the relationship between extracted features and objects, such as support vector machine (SVM), neural network (NN), K-nearest neighbors (KNN), hidden Markov model (HMM), naive Bayes (NB), decision tree (DT), etc. [10; 11]. However, the precision of traditional machine learning algorithms is highly dependent on manually selected features, so the above algorithms only perform well on specific GPR radargrams. Automatic feature extraction by deep learning has gradually become the primary orientation of machine learning in recent years [12]. The power of deep learning to extract complex features of images has significantly improved the recognition efficiency and accuracy of GPR data and accelerated the development of machine learning. The emergence of AlexNet promotes the development of deep learning which was first proposed in the ImageNet competition [13]. Then, similar models VGG-16, GoogleNet, ResNet-50, DenseNet, and SqueezeNet etc. appeared.