Comparison of the Third- order moving average and least square methods for estimating of shape and depth residual magnetic anomalies

Abstract

In the current study, we have developed a new method called the third- order moving average method to estimate the shape and depth of residual magnetic anomalies. This method, calculates a nonlinear relationship between depth and shape factor, at seven points with successive window length. It is based on the computing standard deviation at depths that are determined from all residual magnetic anomalies for each value of the shape factor. The method was applied to the synthetic model by geometrical shapes both as horizontal cylinder and combination of horizontal cylinder, sphere and thin sheet approaches, with and without noise. It was tested by real data in Geological Survey of Iran (GSI). In this study, least square methods were applied to interpret the magnetic field so that we can compare the results of this methods with the third- order moving average method. This method is applied to estimate the depth using second horizontal derivative anomalies obtained numerically from magnetic data with successive window lengths. This method utilizes the variance of the depths as a scale for calculation of the shape and depth. The results showed that the third- order moving average method is a powerful tool for estimating shape and depth of the synthetic models in the presence and absence of noise compared to least square method. Moreover, the results showed that this method is very accurate for real data while the least square method did not lead to feasible results. In this study, least square methods were applied to interpret the magnetic field so that we can compare the results of this methods with the third- order moving average method. This method is applied to estimate the depth using second horizontal derivative anomalies obtained numerically from magnetic data with successive window lengths. This method utilizes the variance of the depths as a scale for calculation of the shape and depth.The results showed that the third- order moving average method is a powerful tool for estimating shape and depth of the synthetic models in the presence and absence of noise compared to least square method. Moreover, the results showed that this method is very accurate for real data while the least square method did not lead to feasible results.