A new efficient method for topographic distortion correction, analytical continuation, vertical derivatives and using equivalent source technique: Application to field data

In general, potential field data are noisy due to the surveys in rugged topography. Standard reduction techniques are not fully adequate to eliminate this problem. Most of the interpretation techniques require the observations measured on the horizontal surface. Furthermore, upward and downward continuations, and vertical derivatives can improve the ambiguity in the potential field data. In present, there are methods for the application of the analytical continuations and derivatives, but these are not successful enough due to technical restrictions and unwanted edge effects. In this paper, an easy and efficient technique known as the equivalent source is applied to the observations made over the rugged topography. The presented method is tested on synthetic example using an anomaly of a sphere located below the synthetic topography. At first, our method is applied only to obtain a topographic distortion corrected map by moving forward windowed parts of the area. Afterwards, analytic continuations of the topographic distortion corrected map are obtained. Finally, a vertical derivative map is obtained from the analytic continuations of the topographic distortion corrected map. Amplitudes are controlled along profiles on top of theoretical and calculated spheres. A good correlation can be observed from the amplitude values. This method is also applied to the field data. The second vertical derivative map is produced from the topographic distortion corrected and 5km upward continued gravity map of the region. We note that by the interpretations identified faults in these map are in good agreement with the faults shown in the structural map of the region. •The technique is used to obtain topographic correction and analytical continuation.•In addition, the first and second vertical derivatives are obtained.•Whole data were used in one go and decreases computational sensitivity.•In suggested method, a sliding window has been chosen from one corner of data set.•Value of window is calculated to increase sensitivity and eliminate edge effect.