## 1 Introduction
In the case of coherent OTDR, a highly coherent laser source is employed so that the backscattered light interferes coherently within each portion of the fiber illuminated by the pulse. This leads to the acquisition of a distributed speckle interferogram at the detector, which is highly sensitive to the smallest strain and temperature changes allowing for the acquisition of fiber fingerprints on a shot-to-shot basis. If no further components are employed to disentangle the phase of the recorded signal, the method is called direct detection C-OTDR, the simplest form of DAS/DVS. As the distribution of scatterers within the fiber is inherently random and consequently the transfer function of the distributed interferometer is unknown, such a system is not able to provide any quantitative information on the value of the strain. However, it does allow for the detection and localization of the overall occurrence of dynamic strain changes as well as their frequency. Further data analysis of C-OTDR data using methods of pattern recognition and machine learning has allowed successful recognition and classification of threatening events in a number of studies [23; 24; 25]. Although, strictly speaking, the term distributed acoustic sensing would imply detection of the full acoustic field including the phase, in many application fields it has become a convention to collect all C-OTDR schemes under the term DAS. For brevity, we will therefore employ this designation in the following. In the sense of a holistic monitoring system described above, the so far not utilized potential of DAS as a means for continuous condition monitoring of pipes by detecting and localizing acoustic signals that point to certain damage scenarios, has been investigated in the interdisciplinary research project AGIFAMOR (Ageing Infrastructures--Fibre Optic Monitoring of Pipes) at BAM from 2015 to 2018 [26]. The feasibility study has focused on three scenarios of potentially detectable acoustic events:  
1. [leftmargin=*]
2. Undamaged pipe: incidents causing (dynamic) circumferential changes of the pipe or initiating propagation of acoustic waves in the pipe wall (e.g., pressure shocks and cavitation, external mechanically induced shocks/vibrations). Damaged pipe: changes inside the pipeline causing altered flow generated noise (corrosion, sedimentation, clogging, in extreme cases also noise caused by leakage). 3. Developing damage of the pipe: crack formation and propagation in the wall with accompanying acoustic emissions. While the results for scenarios 2 [27] and 3 [26] are described elsewhere, the current study focuses on the early recognition of potentially threatening events according to scenario 1 in the undamaged pipe before occurrence of structural failure. Since the better part of the events falling into the first category is highly transient, e.g., impacts on the pipe wall or pressure waves travelling through the pipeline, it is necessary to develop a scheme for data analysis that makes do without any temporal averaging. Especially for steel pipes, which are commonly used, localization is additionally aggravated since each event causes acoustic waves propagating inside the pipe wall with the very high speed of sound around 5000 m/s and very little dampening. This means that dynamic strain changes are transmitted to the sensor fiber along many meters along the pipe within milliseconds, which could significantly reduce the spatial resolution of the measuring system. Therefore, tracking of propagating waves on a very fast time-scale is necessary to reveal the signal onset and the location of the initial source of disturbance. In any implementation of a (distributed) fiber-optic sensing system the application of the optical sensor fiber to the structure to be monitored is crucial. This is especially true for cases, where (dynamic) strain has to be transmitted to the fiber.