Inversion of Self Potential Anomalies with Multilayer Perceptron Neural Networks

Kaftan İ., Sındırgı P., Akdemir Ö.

PURE AND APPLIED GEOPHYSICS, cilt.171, sa.8, ss.1939-1949, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 171 Sayı: 8
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s00024-014-0778-y
  • Dergi Adı: PURE AND APPLIED GEOPHYSICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1939-1949
  • Anahtar Kelimeler: Self potential, MLPNN, DLS, Izmir, Urla, Turkey, EVOLUTION, GRAVITY, PICKING, PLATE
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This study investigates the inverse solution on a buried and polarized sphere-shaped body using the self-potential method via multilayer perceptron neural networks (MLPNN). The polarization angle (alpha), depth to the centre of sphere (h), electrical dipole moment (K) and the zero distance from the origin (x (0)) were estimated. For testing the success of the MLPNN for sphere model, parameters were also estimated by the traditional Damped Least Squares (Levenberg-Marquardt) inversion technique (DLS). The MLPNN was first tested on a synthetic example. The performance of method was also tested for two S/N ratios (5 % and 10 %) by adding noise to the same synthetic data, the estimated model parameters with MLPNN and DLS method are satisfactory. The MLPNN also applied for the field data example in A degrees zmir, Urla district, Turkey, with two cross-section data evaluated by MLPNN and DLS, and the two methods showed good agreement.