Time-frequency analysis of CHAMP scalar and vector magnetic data

Wavelet transforms began to be used in geophysics in the early 1980s for the analysis of seismic signals. The advantage of analyzing a signal with wavelets as the analyzing kernels, is that it enables one to study features of the signal locally with a detail matched to their scale. Owing to their unique time-frequency localization property, wavelet analysis is especially useful for signals that are non-stationary, have short-lived transient components, have features at different scales, or have singularities. Unfortunately, many studies using time-frequency analysis have suffered from an apparent lack of quantitative results. We have applied the continuous wavelet transform to analyze 3 years of Fluxgate and Overhauser magnetometer data of the CHAMP satellite mission. We have detected and classified not only artificial source noise (e.g. instrument problems and pre-processing errors) but also high frequency natural signals of external fields (e.g. F-region currents and pulsations). The results of this analysis will be used for: (a) consequent correction and flagging of the data, (b) derivation of a suitable (undisturbed) dataset for the purposes of crustal and main field modeling, and, (c) study of natural signals (e.g. F-region currents, pulsations) contained in the data.