## 1 Introduction  
Proposed by [PERSON] and [PERSON] in 1993, phase-sensitive optical time-domain reflectometry (\(\Phi\)-OTDR) is a typical monitoring technique for distributed vibrations [1]. Capable of positioning distributed signals, this technique has been widely applied to health monitoring of large buildings [2], perimeter security of important places [3], etc. Compared to traditional 1D monitoring of vibration signals [4; 5; 6; 7], \(\Phi\)-OTDR can realize long-term and high-accuracy monitoring. However, these advantages are achieved at the cost of a huge amount of distributed vibration data, which may lead to insufficient storage and inefficient data processing. To solve this defect, this paper attempts to store and process the vibration signals as images according to existing image-based approaches for vibration signal processing [9; 10; 11; 12; 13; 14]. Specifically, the vibration signals were converted into storable images, and then an integrated strategy was proposed to extract features of these images. Next, the proposed strategy was applied to analyze \(\Phi\)-OTDR signals and track the vibration source. Overall, our research mainly tackles two issues, namely, signal storage (i.e., the conversion of signals into images for storage) and signal analysis (i.e., the processing of the stored images by the proposed method). Concerning signal storage, [PERSON] et al. [15] combined autoregressive-moving-average (ARMA) with swing door trending (SDT) to compress the vibration signals without sacrificing the key features. [PERSON] et al. [16] applied time delay estimation (TDE) in conjunction with differential pulse code modulation (DPCM) as the entropy coding of preprocessor, revealing that the integrated method can encode different types of aperiodic signals and compress vibration signals. Inspired by block compression, [PERSON] et al. [17] put forward a lossless compression plan that draws on the merits of both lossy and lossless compressions. [PERSON] et al. [18] developed a vibration signal compression technique called intrinsic mode function (IMF) based on ensemble empirical mode decomposition (EEMD), aiming to decompose the components of vibration signals in different frequency bands. To sum up, the above signal storage methods can be easily derived through analyzing and calculating the vibration signals. However, most of these methods require complex computation and do not apply to exceptional cases. By contrast, the \(\Phi\)-OTDR technique can overcome these problems by collecting distributed vibrations signals with two dimensions: Time and length. Therefore, this paper aims to convert vibration signals directly into storable images after a few simple steps of preprocessing. Concerning signal analysis, image target recognition has long been regarded as the key problem. The existing methods of image target recognition fall into five categories: Color feature extraction, texture feature extraction, shape feature extraction, intrinsic feature extraction, and spatial feature extraction. The color and spatial features are neglected here due to the lack of color and spatial information in the grayscale images generated from vibration signals. Because signal types of features cannot meet engineering requirements, many scholars have explored integrated feature extraction for image processing. For instance, [PERSON] et al. [19] achieved high-speed tracking of image targets via hybrid rotation invariant description and skip search. [PERSON] et al. [20] use color and edge feature distribution to build a mixture model to search for matching targets in the next frame image. [PERSON] et al. [21] combined the effective region index and multi-scale edge index for image processing. Considering the shape feature and other details of moving objects, [PERSON] et al. [22] presented a robust visual tracking method called the SURF Mean Shift Deep Learning Tracker (SMS-DLT). Nevertheless, the above integrated methods are not comprehensive enough to process the images generated from distributed vibration signals.