Forward modeling with forced neural networks for gravity anomaly profile

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dc.contributor.authorOsman, Onur
dc.contributor.authorAlbora, A. Muhittin
dc.contributor.authorUçan, Osman N.
dc.date.accessioned2020-11-21T15:53:31Z
dc.date.available2020-11-21T15:53:31Z
dc.date.issued2007en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractIn this paper, we introduce a new method called Forced Neural Network (FNN) to find the parameters of the object in geophysical section respect to gravity anomaly assuming the prismatic model. The aim of the geological modeling is to find the shape and location of underground structure, which cause the anomalies, in 2D cross section. At the first stage, we use one neuron to model the system and apply back propagation algorithm to find out the density difference. At the second level, quantization is applied to the density differences and mean square error of the system is computed. This process goes on until the mean square error of the system is small enough. First, we use FNN to two synthetic data, and then the Sivas - Gürün basin map in Turkey is chosen as a real data application. Anomaly values of the cross section, which is taken from the gravity anomaly map of Sivas - Gürün basin, are very close to those obtained from the proposed method. © International Association for Mathematical Geology 2007.en_US
dc.identifier.doi10.1007/s11004-007-9114-8en_US
dc.identifier.endpage605en_US
dc.identifier.issn0882-8121
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-35248839220en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage593en_US
dc.identifier.urihttps://doi.org/10.1007/s11004-007-9114-8
dc.identifier.urihttps://hdl.handle.net/11467/3611
dc.identifier.volume39en_US
dc.identifier.wosWOS:000250066300004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofMathematical Geologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGravity anomalyen_US
dc.subjectModelingen_US
dc.subjectNeural networken_US
dc.subjectSivas - Gürün basinen_US
dc.titleForward modeling with forced neural networks for gravity anomaly profileen_US
dc.typeArticleen_US

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