We recover coseismic static surface deformation by double integration of strong motion accelerometric data. Compared to GPS measurement, the advantage of strong motion data is that they have the potential to provide real-time coseismic static displacements. Strong motion data, however, has the classic problem of baseline offsets which produce unrealistic displacements after double integration is applied. We adopted a bilinear line fitting of empirical baseline correction method to overcome such problem.
We investigate the improvement methods of baseline correction that constrain the maximum flatness of the displacement trace and use the cumulative energy ratio as a threshold. We apply the methods to data sets of the 2003, Mw 8.3 Tokachi-Oki earthquake, the 2007, Mw 7.7 Tocopilla earthquake, the 2010, Mw 7.8 Mentawai earthquake and the 2011, Mw 9.0 Tohoku earthquake. We show that, in general, the results of strong motion derived displacements are comparable to nearby GPS data for most data sets, although for far-field data the method may lead to poor results. It confirms that cumulative energy ratio is appropriate to be used as a threshold of baseline correction method.
The very large and very good quality of boreholes strong motion data of the Tohoku earthquake gives opportunity to investigate the method deeply. We analyze the dependency of the method on hypocenter distance, magnitude and rupture model of the earthquake. We found that the method has a strong dependency on the given parameters, particularly on hypocenter distance. We also show that the method should be distinguished for horizontal and vertical components. Using our improvement method in this study, the deviations of vector length between strong motion derived displacements and nearby GPS data either for horizontal or vertical components, are significantly minimized.
Further study, we optimize the use of valuable rapid static displacement data obtained from strong motion or GPS near-source station. We introduce a centroid grid search method to calculate the moment magnitude by using Okada (1985) model. Our method calculates reasonable moment magnitude using data even only from single station. This method can be done very rapidly within about 5 minutes. It provides crucial information e.g. for making tsunami warning decision.