## 1 Introduction  
In the subsea production system, a jumper is a pipeline connecting the wellhead, Christmas tree, pipeline manifold, and pipeline terminal, transporting the extracted oil and gas. As shown in Figure 1, a typical M-shaped rigid jumper has multiple bending sections and long overhanging sections. The generation and transformation of flow patterns inside the jumper are closely related to variable gas-liquid velocities, different gas content rates, and pipe shapes and sizes. Stratified flow, slug flow, annular flow, and bubbly flow are commonly seen multiphase flow patterns inside subsea jumpers. The research of flow patterns is essential for understanding multiphase flow characteristics for the design and safety of underwater production systems and operation efficiency. It can also determine the potential dangerous factors and take corresponding protection measures [1]. Therefore, it is important to study how the jumper's internal flow pattern changes with the flow rate of gas or liquid. The traditional gas-liquid two-phase flow pattern identification method is simplex and mainly relies on experimental data acquisition, in which researchers obtain flow pattern distribution diagrams of different flow patterns by experimental findings that describe how the flow patterns change and identify them from the practical level. The [PERSON] flow pattern diagram [3] and Mandhane flow pattern diagram [4] are the most representative. However, this method has been gradually eliminated by scholars. As the classification of flow diagrams varies with the subjective perception of scholars and is based on the results of a few different kinds of fluids within a certain range of parameters, the applicability of flow diagrams is conditional. At present, the main flow pattern identification methods are direct observation and indirect measurement [5]. Direct observation methods mainly include the visual method and high-speed camera method. This identification method is individually dependent and it is difficult to form a general identification standard for flow classification when using it. The indirect measurement method is based on data processing and analysis of the actual measurement signals of various extracted characteristics of different flow patterns. [PERSON] et al. [6] collected the vibration signals of two-phase flow in a vertical pipe and extracted the signals' root mean square values and the Pearson correlation coefficients to accurately classify the studied cases. By using electrical capacitance tomography (ECT) based on fuzzy logic, [PERSON] et al. [7] were able to determine the flow patterns in vertical pipes and horizontal pipes with inner diameters of 38 mm, 60 mm, and 90 mm. [PERSON] et al. [8] identified five flow patterns according to the flow pattern maps, pressure drop, and power spectral density (PSD) distribution of pressure drop in a bent tube, studied the regularity characteristics of PSD skewness and multiscale entropy (MSE) rate, and the flow patterns in U-shaped tubes were identified objectively. However, this method of identifying flow patterns based on extracting features from sensor signals consumes a lot of time and effort in the pre-processing data stage, which reduces the efficiency of real-time identification. A flow pattern recognition method based on deep learning has received wide attention. The deep learning model emphasizes the depth of model structure and the importance of feature learning, which can more profoundly depict data-rich information. To collect different photos of oil-water two-phase flow patterns, [PERSON] et al. [9] set up an oil-water experiment platform. They used an image segmentation method based on the lowest gray level to extract the characteristics of the flow patterns. To extract image features, three common convolutional neural networks structures were used to identify typical oil-water two-phase flow patterns. [PERSON] et al. [10] constructed a different deep neural network  
Figure 1: Ready for installation of M-shaped rigid jumper [2]. model.