The Source Parameter Imaging (SPI™) method computes source parameters from gridded magnetic data. The method assumes either a 2-D sloping contact or a 2-D dipping thin‐sheet model and is based on the complex analytic signal. Solution grids show the edge locations, depths, dips, and susceptibility contrasts. The estimate of the depth is independent of the magnetic inclination, declination, dip, strike and any remanent magnetization; however, the dip and the susceptibility estimates do assume that there is no remanent magnetization. Image processing of the source‐parameter grids enhances detail and provides maps that facilitate interpretation by nonspecialists. The SPI method tests successfully on synthetic profile and gridded data. SPI maps derived from aeromagnetic data acquired over the Peace River Arch area of northwestern Canada correlate well with known basement structure and furthermore show that the Ksituan Magmatic Arc can be divided into several susceptibility subdomains.

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We have developed a new method for interpretation of gridded magnetic data which, based on derivatives of the tilt angle, provides a simple linear equation, similar to the 3D Euler equation. Our method estimates both the horizontal location and the depth of magnetic bodies, but without specifying prior information about the nature of the sources ͑struc-tural index͒. Using source-position estimates, the nature of the source can then be inferred. Theoretical simulations over simple and complex magnetic sources that give rise to noisecorrupted and noise-free data, illustrate the ability of the method to provide source locations and index values characterizing the nature of the source bodies. Our method uses second derivatives of the magnetic anomaly, which are sensitive to noise ͑high-wavenumber spectral content͒ in the data. Thus, an upward continuation of the anomaly may lead to reduce the noise effect. We demonstrate the practical utility of the method using a field example from Namibia, where the results of the proposed method show broad correlation with previous results using interactive forward modeling.