MATLAB programs for the synthetic models
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ORIGINAL PAPER
MATLAB programs for the synthetic models Mahmoud Mehramuz & Hossein Zomorrodian & Khadijeh Ahmadi & Majid Mahood
Received: 28 February 2012 / Accepted: 31 July 2012 # Saudi Society for Geosciences 2012
Abstract The separation of residual gravity anomaly from regional gravity has considerably been studied for many years in gravity explorations. In addition, it is considered as a critical step in gravity data inversion. Some techniques have been developed for regional–residual anomaly separation both in space and frequency domains. One of these techniques for computing the regional anomaly is nonlinear filtering. In this paper, some techniques such as low-pass filtering, Butterworth, upward continuation, and nonlinear filtering are used to on synthetic gravity data in present of random noise and noise free for the purpose of residual– regional anomaly separation. The obtained results of techniques are compared with each other. The results have shown that separation methods are so efficient where synthetic models are located in shallow depth. Moreover, it is found that in comparison with other separation techniques, nonlinear filtering is more efficient in residual–regional anomaly separation and upward continuation technique is more efficient than Butterworth filter and low-pass filter. In addition, all of the obtained results have shown that Butterworth and low-pass filters are the same. Keywords Nonlinear filtering Residual anomaly . Separation
. Regional anomaly . . Synthetic data . Gravity
M. Mehramuz (*) : H. Zomorrodian : K. Ahmadi : M. Mahood Department of Geophysics, Science and Research Branch, Islamic Azad University, Tehran, Iran e-mail: [email protected] H. Zomorrodian e-mail: [email protected] K. Ahmadi e-mail: [email protected] M. Mahood e-mail: [email protected]
Introduction In the recent years, lots of studies have carried out in residual– regional anomaly separation to improve the techniques and algorithms for appropriate computations, such as upward continuation technique by Fuller (1967), discrete wavelet transform by Fedi and Quarta (1998), wavelet domain technique by Basiliki et al. (2003), graphical method by Pinet et al. (2006), wavelet transform and spectrum analysis by Xu et al. (2009), and finite element technique by Kaftan et al. (2010).Keating and Pinet (2011) have recently used nonlinear filtering, upward continuation, Butterworth filter, and low-pass filter on a 2D prism as the synthetic model. In this study, nonlinear filtering, upward continuation, Butterworth filter, and low-pass filter are used to the synthetic gravity data in present of random noise (5 %) and noise free on synthetic models (sphere, horizontal cylinder, vertical cylinder, and rectangular prism ) at different depths. Finally, the results of these techniques are compared with each other.
Application of nonlinear filtering, upward continuation, Butterworth filter, and low-pass filter to the synthetic models Firstly, we have made the synthetic gravity data base on the synthetic
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