On the estimation of the structural index from low-pass filtered magnetic data

What is it about?

The estimation of thedepthto the source and of its geometrical shape is the main task of many popular methods used to analyze magnetic or gravity data in explorationgeophysics. However, these estimates are unstable even in the presence of a weak amount of noise so, in general, a low-pass filtering of the data is required. We show that analysis of noisy data leads to an underestimation of structural index and depth, and low-pass filtered data analysis lead to increase of the values of these parameters.

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Why is it important?

We have studied how the structural index and depth estimates are affected by applying low-pass filtering to the data. Physically based low-pass filters, such as upward continuation and integration, have been shown to be the best choice over a range of altitudes (upward continuation) or orders (integration filters), mainly because their outputs have a well-defined physical meaning. In contrast, mathematical low-pass filters require that the filter parameters be tuned carefully by means of several trial tests to produce optimally smoothed fields, otherwise the data may result overfiltered (distorted). We found that the estimated structural index and depth to source increase proportionally with the amount of smoothing, unless in the case of overfiltering. In that case, the severe distortion of the original field may cause a decrease of the estimated structural index and depth to source.