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2 Acta Geophysica

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Estimating the Shapes of Gravity Sources through Optimized Support Vector Classifier (SVC)

100%

Hekmatian M. E. , Ardestani V. E. , Riahi M. A. , Bagh A. M. K. , Amini J.

tom Vol. 63, no. 4

1000--1024

In gravity interpretation methods, an initial guess for the approximate shape of the gravity source is necessary. In this paper, the support vector classifier (SVC) is applied for this duty by using gravity data. It is shown that using SVC leads us to estimate the approximate shapes of gravity sources more objectively. The procedure of selecting correct features is called feature selection (FS). In this research, the proper features are selected using inter/intra class distance algorithm and also FS is optimized by increasing and decreasing the number of dimensions of features space. Then, by using the proper features, SVC is used to estimate approximate shapes of sources from the six possible shapes, including: sphere, horizontal cylinder, vertical cylinder, rectangular prism, syncline, and anticline. SVC is trained using 300 synthetic gravity profiles and tested by 60 other synthetic and some real gravity profiles (related to a well and two ore bodies), and shapes of their sources estimated properly.

Application of various methods for 2D inverse modeling of residual gravity anomalies

100%

Abedi M. , Afshar A. , Ardestani V. E. , Narouzi G. , Lucas C.

2010

tom Vol. 58, no. 2

317-336

This paper presents a neural network approach to determine 2D inverse modeling of a buried structure from gravity anomaly profile. The results of the applied neural network method are compared with the results of two other methods, least-squares minimization and the simple method. Sphere, horizontal cylinder and vertical cylinder and their gravity effects are considered as the synthetic models and the synthetic data, respectively. The synthetic data are also corrupted with noise to evaluate the capability of the methods. Then the Dehloran bitumen map in Iran is chosen as a real data application. Anomaly value of the cross-section, which is taken from the gravity anomaly map of Dehloran bitumen, is very close to those obtained from these methods.