Matched filtering method for separating magnetic anomaly using fractal model

Fractal/scaling distribution of magnetization in the crust has found with growing body of evidences from spectral analysis of borehole susceptibility logs and magnetic field data, and fractal properties of magnetic sources have already been considered in processing magnetic data such as the Spector and Grant method for depth determination. In this study, the fractal-based matched filtering method is presented for separating magnetic anomalies caused by fractal sources. We argue the benefits of considering fractal natures of source distribution for data processing in magnetic exploration: the first is that the depth determination can be improved by using multiscaling model to interpret the magnetic data power spectrum; the second is that the matched filtering can be reconstructed by employing the difference in scaling exponent together with the corrected depth and amplitude estimates. In the application of synthetic data obtained from fractal modeling and real aeromagnetic data from the Qikou district of China, the proposed fractal-based matched filtering method obtains more reliable depth estimations as well as improved separation between local anomalies (caused by volcanic rocks) and regional field (crystalline basement) in comparison with the conventional matched filtering method.