Electromagnetic phenomena associated with large earthquakes have been investigated to establish any method to monitor the crustal activity such as earthquakes and volcanic eruptions. The ULF geomagnetic approach is considered to be one of the most promising methods for the imminent prediction. In this paper, a new method of principal component analysis (PCA) has been applied to the ULF geomagnetic data associated with 2000 Izu Islands earthquake swarm. This swarm activity started on June 26, 2000 and terminated in September, 2000, and during this activity there were observed 5 large earthquakes whose magnitude was grater than 6 (July 1, 8, 15, 30, and August 18). During this period our ULF stations were fortunately in operation, and there are three stations closely distributed (about 5 km distances). The epicentral distances are about 80–100 km. The PCA has been applied to the ULF horizontal NS component, because we can distinguish a few noises sources based on the orthogonal decomposition in the PCA. We investigate the temporary variations of eigenvalues and eigenvectors of PCA results, and the results are summarized as follows. The first principal component is found to be the signal originated in the solar–terrestrial effects such as geomagnetic pulsation. The variation of eigenvector for the first principal component suggests that this signal is very stable over the whole analyzed period. As for the second principal component, the local artificial noise is included. As for the smallest third component in the local midnight at the Izu Peninsula, it indicated an apparent increase in the third eigenvalue a few days before the large earthquakes. Also about three months before the beginning of swarm activity, the level of the third eigenvalue was slightly enhanced. Correspondingly, the pattern of eigenvector direction in the signal subspace is changed simultaneously and it recovered to the original position after the swarm activity. These features are likely to be correlated with large earthquakes. Finally we want to emphasize that PCA approach is promising for monitoring the crustal activity.
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