12:00 PM - 12:15 PM
[SEM35-12] Improving horizontal magnetic components in MT data using independent component analysis
Keywords: MT methods, independent component analysis
ICA is one of the multivariate analysis methods and in which complicated data sets can be separated into all underlying sources without knowing these sources or the way that they are mixed. It assume that the mixing is liner, and yields the relation x(t)=As(t) with input signals x(t), mixing matrix A and source signal s(t). In this study, to determine the matrix W(=A-1), we used FastICA algorithm which was introduced by Aapo Hyvärinen (2000). It is based on a fixed-point iteration scheme for finding maximum of the nongaussianity of WTx(t).
We applied the ICA method to improve horizontal magnetic components in MT data using both the data observed in Boso area and the noise free magnetic data observed in Esashi (Iwate) or Memanbetsu (Hokkaido). After applying ICA, each component is not defined intensity scale. To extract noise free data in original data scale, kept the noise free components, and other component set to 0. (i.e. x(t)=W-1u'(t) Where u': components vector after ICA, x(t): the original data vector.) Finally we calculated the apparent resistivity and phases using the horizontal magnetic data processed as above.
In comparison between before and after the ICA processing, the noise components were removed. The apparent resistivity and phase improved by ICA were free from the influence of near-field phenomenon. These results revealed that ICA has the potential to handle noisy data. However we must more improve MT data to reveal the deep area, we also need to remove the noise of electric data.