# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 1 Introduction  
A distributed optical fiber vibration sensing system has the advantages of a simple structure, resistance to electromagnetic interference, adaptability in flammable environments and wide detection range [1, 2, 3]. As one typical distributed optical fiber vibration sensing system, a phase-sensitive optical time domain reflectometer (\(\Phi\)-OTDR) can achieve simultaneously multi-point vibration detection and location. Because of the benefits of its high sensitivity, good spatial resolution and long detecting distance [4, 5], \(\Phi\)-OTDR has broad application prospects in long-distance oil and gas pipelines [6], border security intrusion detection and smart grids [7, 8]. The current research hotspot of \(\Phi\)-OTDR is the improvement of its sensing performance, such as a sensing distance up to even hundreds of kilometers [9, 10], and a spatial resolution of a sub-meter magnitude [11], which leads to a huge amount of sensing data and a growing demand for computation resources. Consequently, the time consumption procedure of signal processing is longer and the real-time performance is more degradative. In orderto solve this issue, many researchers have studied computational enhancement technologies for distributed optical fiber vibration sensing. The preprocessing of Rayleigh backwards scattering (RBS) curves incurs costs in the major computing resource of the central processing unit (CPU). Many studies about the algorithm improvement of preprocessing have been proposed. Traditional and classical data preprocessing methods, such as the moving average method, moving differential method [12; 13] and frequency spectrum analysis have been widely used in a \(\Phi\)-OTDR system [14; 15]. In 2017, [16] proposed a novel single-source dual heterodyne detection scheme to improve the optical path of the system. It can reduce the data volume in order to shorten the response time and improve the real-time performance of the system. The authors of [17] used a data matrix matching algorithm to optimize the data demodulation process of the \(\Phi\)-OTDR system. By reducing the total amount of data needed for sensing data demodulation, the system calculation time is reduced and the real-time performance of the system is improved. It must be noted that, in previous related work, the authors reduced the amount of data to shorten the overall running time of the system. As these preprocessing methods are always performed by a single-thread computing system based on a CPU, the massive amount of data in the \(\Phi\)-OTDR system could lead to a longer processing time and even result in a system crash. Thus, the concurrent computation technology based on a graphics processing unit (GPU) could be used for large-scale data parallel processing, which can effectively enhance the processing speed of massive data [18; 19; 20]. [PERSON] et al. first demonstrated the method of applying a GPU, which is good at parallel computing, to a \(\Phi\)-OTDR sensing system, in order to provide a frequency spectrum analysis of the system, while the real-time performance of the system was not studied [21]. [PERSON] et al. also used GPU for dealing with the data of a \(\Phi\)-OTDR sensing system, so the speed of data processing was enhanced and the real-time performance was optimized [22]. The studies on GPU-parallel computing also have been applied to other fields, such as Fiber Bragg Grating (FBG) sensor and Stimulated Brillouin scattering (SBS) in optical telecommunication systems [23; 24]. The fields of cellular and remote sensor imaging [25] and the occupancy grid maps in automotive environments also utilized GPU-parallel technology to improve real-time performance [26]. However, the high-speed concurrent computation can hardly do without the support of high-speed data acquisition, particularly in the process of real-time collection of vibration information in time, space, and frequency.