doi:10.3390/s22176515engAlmudévar, AntonioSevillano, PascualVicente, LuisPreciado-Garbayo, JavierOrtega, AlfonsoUnsupervised anomaly detection applied to F-OTDRART-2022-130505Distributed acoustic sensors (DASs) based on direct-detection Φ-OTDR use the light–matter interaction between light pulses and optical fiber to detect mechanical events in the fiber environment. The signals received in Φ-OTDR come from the coherent interference of the portion of the fiber illuminated by the light pulse. Its high sensitivity to minute phase changes in the fiber results in a severe reduction in the signal to noise ratio in the intensity trace that demands processing techniques be able to isolate events. For this purpose, this paper proposes a method based on Unsupervised Anomaly Detection techniques which make use of concepts from the field of deep learning and allow the removal of much of the noise from the Φ-OTDR signals. The fact that this method is unsupervised means that no human-labeled data are needed for training and only event-free data are used for this purpose. Moreover, this method has been implemented and its performance has been tested with real data showing promising results.2022http://zaguan.unizar.es/record/11994110.3390/s22176515http://zaguan.unizar.es/record/119941oai:zaguan.unizar.es:119941info:eu-repo/grantAgreement/ES/DGA/T20-20Rinfo:eu-repo/grantAgreement/ES/DGA/T36-20Rinfo:eu-repo/grantAgreement/ES/MICINN-AEI/RTC2019-007207-4/AEI/10.13039/501100011033Sensors 22, 17 (2022), 6515 [14 pp]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess