A New Approach For Residual Gravity Anomaly Profile Interpretations: Forced Neural Network (FNN)

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dc.authoridTR13219en_US
dc.authoridTR46379en_US
dc.authoridTR26113en_US
dc.contributor.authorOsman, Onur
dc.contributor.authorAlbora, A. Muhittin
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2015-09-15T07:25:46Z
dc.date.available2015-09-15T07:25:46Z
dc.date.issued2006en_US
dc.departmentMeslek Yüksekokulları, Meslek Yüksek Okulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractThis paper presents a new approach for interpretation of residual gravity anomaly profiles, assuming horizontal cylinders as source. The new method, called Forced Neural Network (FNN), is introduced to determine the underground structure parameters which cause the anomalies. New technologies are improved to detect the borders If geological bodies in a reliable way. In a first phase one neuron is used to model the system and a back propagation algorithm is applied to find the density difference. In a second phase, density differences are quantified and a mean square error is computed. This process is iterated until the mean square error is small enough. After obtaining reliable results in the case of synthetic data, to simulate real data, the real case of the Gulf of Mexico gravity anomaly map, which has the form of anticline structure, is examined. Gravity anomaly values from a cross section of this real case, result to be very close to those obtained with the proposed method.en_US
dc.identifier.endpage1208en_US
dc.identifier.issn1593-5213
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-34447502335en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1201en_US
dc.identifier.urihttps://hdl.handle.net/11467/1192
dc.identifier.volume49en_US
dc.identifier.wosWOS:000248158000006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEditrice Compositori Bolognaen_US
dc.relation.ispartofAnnals Of Geophysicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectForced Neural Network; Gravity Anomaly; Modeling; Synthetic Model; Gulf Of Mexico.en_US
dc.titleA New Approach For Residual Gravity Anomaly Profile Interpretations: Forced Neural Network (FNN)en_US
dc.typeArticleen_US

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