Applications of the Kuwahara and Gaussian filters on potential field data

Abstract

Edge detection methods based on derivatives of potential fields have been commonly utilizing to interpret the potential field data. Although these techniques are very useful tools for determining subsurface lineaments, they have some limitations such as sensitivity to noise, thick or diffuse detected boundaries and blurred edges. In this study, the Kuwahara filter, an edge enhancement operator which has smoothing, edge preserving and edge sharpening effects, is proposed to enhance the boundaries in the potential field data without above-mentioned limitations. Three synthetic model applications are made to demonstrate the mentioned advantages of the Kuwahara filter. The effect of the window size on the filter response has been studied and demonstrated in detail. After that, the total horizontal derivative, analytic signal and vertical gradient of the Kuwahara filter responses of the noisy synthetic gravity dataset have been calculated and compared with those calculated from the original data. In addition, it has been shown that the results can be further improved by applying the Gaussian smoothing to the Kuwahara filter responses. Finally, the Bouguer gravity dataset compiled from Konya-Beyşehir region in Turkey have been utilized to show the performance of the Kuwahara filter on real data. Both synthetic and real data applications have shown that more clearly edges and detailed results can be obtained from the datasets filtered by using the Kuwahara and Gaussian filters.