[Türkçe .pdf]
2D INVERSE MODELING OF THE GRAVITY FIELD DUE TO A CHROMITE DEPOSIT USING THE MARQUARDT’S ALGORITHM AND FORCED NEURAL NETWORK 

Başlık: 2D INVERSE MODELING OF THE GRAVITY FIELD DUE TO A CHROMITE DEPOSIT USING THE MARQUARDT’S ALGORITHM AND FORCED NEURAL NETWORK  
Yazarlar: Ata ESHAGHZADEH, Sanaz SEYEDI SAHEBARI, Alireza DEHGHANPOUR  
Anahtar Kelimeler: Chromite deposit,finite vertical cylinder,Forced Neural Networks,gravity,Marquardt’s algorithm,  
Özet: In this paper, two modeling method are employed. First, a method based on the Marquardt’s algorithm is presented to invert the gravity anomaly due to a finite vertical cylinder source. The inversion outputs are the depth to top and bottom, and radius parameters. Second, Forced Neural Networks (FNN) for interpreting the gravity field as try to fit the computed gravity in accordance with the estimated subsurface density distribution to the observed gravity. To evaluate the ability of the methods, those are employed for analyzing the gravity anomalies from assumed models with different initial parameters as the satisfactory results were achieved. We have also applied these approaches for inverse modeling the gravity anomaly due to a Chromite deposit mass, situated east of Sabzevar, Iran. The interpretation of the real gravity data using both methods yielded almost the same results.

[English .pdf]
2D INVERSE MODELING OF THE GRAVITY FIELD DUE TO A CHROMITE DEPOSIT USING THE MARQUARDT’S ALGORITHM AND FORCED NEURAL NETWORK 

Title: 2D INVERSE MODELING OF THE GRAVITY FIELD DUE TO A CHROMITE DEPOSIT USING THE MARQUARDT’S ALGORITHM AND FORCED NEURAL NETWORK  
Authors: Ata ESHAGHZADEH, Sanaz SEYEDI SAHEBARI, Alireza DEHGHANPOUR  
Keywords: Chromite deposit,finite vertical cylinder,Forced Neural Networks,gravity,Marquardt’s algorithm,  
Abstract: In this paper, two modeling method are employed. First, a method based on the Marquardt’s algorithm is presented to invert the gravity anomaly due to a finite vertical cylinder source. The inversion outputs are the depth to top and bottom, and radius parameters. Second, Forced Neural Networks (FNN) for interpreting the gravity field as try to fit the computed gravity in accordance with the estimated subsurface density distribution to the observed gravity. To evaluate the ability of the methods, those are employed for analyzing the gravity anomalies from assumed models with different initial parameters as the satisfactory results were achieved. We have also applied these approaches for inverse modeling the gravity anomaly due to a Chromite deposit mass, situated east of Sabzevar, Iran. The interpretation of the real gravity data using both methods yielded almost the same results.
