Uluocak, Ebru ŞengülUlugergerli, Emin U.Göktaş, Hilal2025-01-272025-01-2720131301-2894https://hdl.handle.net/20.500.12428/13905In this study artificial neural network method was used to classify noisy components in the MT method data. For this purpose a multi-layered, feed-foorward and back propagation model was employed. Noisy time windows were determined to an accuracy of 89% depending on the training data set. In addition, when all types of noise in the data are defined (synthetic data), all noisy time windows can be sellected and eliminated by artificial neural network method.Test results from synthetic and field data indicate that artificial neural network classification is succesfull in identifying and eliminating the noisy data windows compared to both visual inspection and conventional assessment methods.eninfo:eu-repo/semantics/closedAccessArtificial neural network; Magnetotelluric; Signal-noise detection; Time seriesArticle34153722-s2.0-84877623032Q4