000119941 001__ 119941 000119941 005__ 20240319081019.0 000119941 0247_ $$2doi$$a10.3390/s22176515 000119941 0248_ $$2sideral$$a130505 000119941 037__ $$aART-2022-130505 000119941 041__ $$aeng 000119941 100__ $$aAlmudévar, Antonio$$uUniversidad de Zaragoza 000119941 245__ $$aUnsupervised anomaly detection applied to F-OTDR 000119941 260__ $$c2022 000119941 5060_ $$aAccess copy available to the general public$$fUnrestricted 000119941 5203_ $$aDistributed acoustic sensors (DASs) based on direct-detection Φ-OTDR use the light–matter interaction between light pulses and optical fiber to detect mechanical events in the fiber environment. The signals received in Φ-OTDR come from the coherent interference of the portion of the fiber illuminated by the light pulse. Its high sensitivity to minute phase changes in the fiber results in a severe reduction in the signal to noise ratio in the intensity trace that demands processing techniques be able to isolate events. For this purpose, this paper proposes a method based on Unsupervised Anomaly Detection techniques which make use of concepts from the field of deep learning and allow the removal of much of the noise from the Φ-OTDR signals. The fact that this method is unsupervised means that no human-labeled data are needed for training and only event-free data are used for this purpose. Moreover, this method has been implemented and its performance has been tested with real data showing promising results. 000119941 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T20-20R$$9info:eu-repo/grantAgreement/ES/DGA/T36-20R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/RTC2019-007207-4/AEI/10.13039/501100011033 000119941 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000119941 590__ $$a3.9$$b2022 000119941 592__ $$a0.764$$b2022 000119941 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 86 = 0.302$$c2022$$dQ2$$eT1 000119941 593__ $$aInstrumentation$$c2022$$dQ1 000119941 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 63 = 0.302$$c2022$$dQ2$$eT1 000119941 593__ $$aAnalytical Chemistry$$c2022$$dQ1 000119941 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2 000119941 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2 000119941 593__ $$aInformation Systems$$c2022$$dQ2 000119941 593__ $$aBiochemistry$$c2022$$dQ2 000119941 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ2 000119941 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2 000119941 594__ $$a6.8$$b2022 000119941 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000119941 700__ $$0(orcid)0000-0002-4094-3826$$aSevillano, Pascual$$uUniversidad de Zaragoza 000119941 700__ $$0(orcid)0000-0003-4391-5203$$aVicente, Luis$$uUniversidad de Zaragoza 000119941 700__ $$0(orcid)0000-0001-5898-8777$$aPreciado-Garbayo, Javier 000119941 700__ $$0(orcid)0000-0002-3886-7748$$aOrtega, Alfonso$$uUniversidad de Zaragoza 000119941 7102_ $$12002$$2385$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Física Aplicada 000119941 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac. 000119941 773__ $$g22, 17 (2022), 6515 [14 pp]$$pSensors$$tSensors$$x1424-8220 000119941 8564_ $$s2123291$$uhttps://zaguan.unizar.es/record/119941/files/texto_completo.pdf$$yVersión publicada 000119941 8564_ $$s2667888$$uhttps://zaguan.unizar.es/record/119941/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000119941 909CO $$ooai:zaguan.unizar.es:119941$$particulos$$pdriver 000119941 951__ $$a2024-03-18-15:57:37 000119941 980__ $$aARTICLE