Determination of structure parameters on gravity method by using Radial Basis Functions Networks case study: Seferihisar Geothermal Area (Western Turkey)

KAFTAN İ., ŞALK M.

79th Society of Exploration Geophysicists International Exposition and Annual Meeting 2009, SEG 2009, Texas, Amerika Birleşik Devletleri, 25 - 30 Ekim 2009, ss.991-994 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Texas
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.991-994
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

© 1996-2018 Society of Exploration Geophysicists All Rights Reserved.Artificial Neural Networks (ANN) have been used in a variety of problems in the fields of science and engineering. Applications of ANN to the geophysical problems have increased within the last decade. Especially it has been used to solve inversion problems such as seismic, electromagnetic, resistivity. Also there are some other applications such as parameter estimation, prediction and classification. In this study, Radial Basis Function Networks (RBF NN) were applied to theoretical gravity data and Seferihisar gravity data to investigate the applicability and performance of this network for gravity method. Seferihisar is one of the important geothermal areas in the Western Turkey. There are many geological and geophysical studies in this area. Bouguer gravity data were analyzed by RBF NN to estimate depth and density contrast of the structure. Also the RBF NN results were compared with the results of a previous study.