## 1 Introduction  
As a main method of oil and gas transportation, pipelines play an important role in transporting supplies [1, 2]. During their long-term service life, various types of pipeline damages are related to pipeline ponding; corrosion, perforation, and leakage are not uncommon, and they usually bring about serious safety hazards to pipeline transportation [3]. Therefore, to ensure the safe and stable operation of pipelines, pipeline ponding detection has become more important and urgent. In pipeline ponding detection, changes in the ponding volume will cause changes in the structural characteristics of the pipeline system composed of pipeline and ponding. Therefore, some developed methods for the monitoring of structural characteristics may provide an approach as the reference for pipeline ponding detection. In recent decades, several common methods for pipeline structure characteristic detection have been introduced, including the CCTV (closed-circuit television) inspection method [4], the ultrasonic testing method [5] and the radiography method [6]. The CCTV inspection method presents very rich internal information of the pipeline in the form of photos or videos [7] by a robotic system with a camera [8]. However, the CCTV method is greatly affected by environmental factors, and its detection accuracy of the pipeline evaluation depends largely on the quality of the hardware system and the experience of the inspectors [9]. The ultrasonic testing method can estimate the health state by analyzing reflection waves [10; 11] in the pipelines. It is sensitive to changes in structural state and can be related to several structural characteristics [12]. However, the signals collected by the ultrasonic method are usually accompanied with noise, and effective noise reduction methods are required to obtain useful information [13; 14]. The radiography method detects the pipeline by evaluating the attenuation of the rays [15] which pass through the pipeline. This method can be used for pipelines with complex geometric shapes [16]. However, its detection accuracy decreases when it is employed for vertical angle defect detection [17], and the rays are harmful to human health [18]. Therefore this method's practical application is very limited. Compared with the aforementioned detection approaches, the percussive detection method [19; 20; 21] has the characteristics of deep detection and fast transmission speeds, and is user-friendly [22]. It is used to determine the pipeline structure characteristic by the sounds generated through impact on the pipeline under test [23]. Traditional percussive detection method still requires engineering experience, which can be subjective and inefficient [24]. This is solved by using the powerful computing power of computers or the automatic prediction and classification properties of machine learning. [PERSON] et al. proposed a new percussion-based method using analytical modeling and numerical simulation, whereby a percussion-induced sound pressure level (SPL) could be obtained via the acoustic radiation mode approach. The corresponding numerical simulation was developed with a focus on the acoustic-structure coupling, and the acoustic boundary conditions were satisfied through a perfectly matched layer (PML) [25]. [PERSON] et al. used Mel-frequency cepstral coefficients (MFCCs) as the features of percussion-induced acoustics, and support vector machine (SVM)-based machine learning was utilized to classify results [26]. [PERSON] et al. used power spectrum density (PSD) to process percussive sound, and a decision tree machine (DTM) learning approach was used to classify results [27]. CNN, one of the representative algorithms of deep learning, which automatically predicts and classifies the data [28], can overcome the drawbacks of percussive detection methods that requires engineering experience, and can therefore obtain superior results in visual classification tasks [29].