Estimation of seismic quality factor: Artificial neural networks and current approaches

dc.authorid0000-0002-5134-0625en_US
dc.contributor.authorYıldırım, Eray
dc.contributor.authorSaatcilar, Ruhi
dc.contributor.authorErgintav, Semih
dc.date.accessioned2021-03-20T20:14:16Z
dc.date.available2021-03-20T20:14:16Z
dc.date.issued2017
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe aims of this study are to estimate soil attenuation using alternatives to traditional methods, to compare results of using these methods, and to examine soil properties using the estimated results. The performances of all methods, amplitude decay, spectral ratio, Wiener filter, and artificial neural network (ANN) methods, are examined on field and synthetic data with noise and without noise. High-resolution seismic reflection field data from Yenikiiy (Arnavutkoy, Istanbul) was used as field data, and 424 estimations of Q values were made for each method (1,696 total). While statistical tests on synthetic and field data are quite close to the Q value estimation results of ANN, Wiener filter, and spectral ratio methods, the amplitude decay methods showed a higher estimation error. According to previous geological and geophysical studies in this area, the soil is water-saturated, quite weak, consisting of clay and sandy units, and, because of current and past landslides in the study area and its vicinity, researchers reported heterogeneity in the soil. Under the same physical conditions, Q value calculated on field data can be expected to be 7.9 and 13.6. ANN models with various structures, training algorithm, input, and number of neurons are investigated. A total of 480 ANN models were generated consisting of 60 models for noise free synthetic data, 360 models for different noise content synthetic data and 60 models to apply to the data collected in the field. The models were tested to determine the most appropriate structure and training algorithm. In the final ANN, the input vectors consisted of the difference of the width, energy, and distance of seismic traces, and the output was Q value. Success rate of both ANN methods with noise-free and noisy synthetic data were higher than the other three methods. Also according to the statistical tests on estimated Q value from field data, the method showed results that are more suitable. The Q value can be estimated practically and quickly by processing the traces with the recommended ANN model. Consequently, the ANN method could be used for estimating Q value from seismic data. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.jappgeo.2016.11.010en_US
dc.identifier.endpage278en_US
dc.identifier.issn0926-9851
dc.identifier.issn1879-1859
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage269en_US
dc.identifier.urihttp://doi.org/10.1016/j.jappgeo.2016.11.010
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1026
dc.identifier.volume136en_US
dc.identifier.wosWOS:000392769700024en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldırım, Eray
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofJournal Of Applied Geophysicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSeismic quality factoren_US
dc.subjectArtificial neural networken_US
dc.subjectWiener filteren_US
dc.subjectSpectral ratioen_US
dc.subjectAmplitude decayen_US
dc.subjectQ estimateen_US
dc.titleEstimation of seismic quality factor: Artificial neural networks and current approachesen_US
dc.typeArticleen_US

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