Forward modeling with forced neural networks for gravity anomaly profile
Küçük Resim Yok
Tarih
2007
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
Gravity anomaly, Modeling, Neural network, Sivas - Gürün basin
Kaynak
Mathematical Geology
WoS Q Değeri
Q3
Scopus Q Değeri
N/A
Cilt
39
Sayı
6
