In microtremor data analysis, the fundamental mode component has been considered as predominant. However, recent studies demonstrate that microtremor analysis such as the SPAC method requires consideration of higher modes of surface waves. To investigate microtremor data including the higher modes, we review analysis methods by using simulated microtremor data. Although calculation of phase velocities of superposed modes from SPAC coefficient has been proposed for microtremor analyses which include the higher modes, we cannot apply these methods to microtremor data obtained using multiple-receiver spacing. Here, we propose two new methods which can analyze data with multiple-receiver spacing. The first method calculates the phase velocities of superposed modes incorporating multiple-receiver spacing. The second is to compare the observed value with the theoretical value in SPAC coefficient. As a result, phase velocity and SPAC coefficient calculated from simulated microtremor data are well consistent with those derived from these two methods. By applying to field data, these two methods can be considered as applicable methods. It is concluded that these new analysis methods enable us to use higher modes in the microtremor data obtained from multiple-receiver spacing.

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 We developed a method to separate P, SV and SH waves from three component (3C) and three dimensional (3D) reflection seismic data by a plane wave decomposition in frequency-wave number (kx, ky) domain. First, we applied the method to 3C-3D synthetic data and demonstrated to separate multi-mode mixed wave fields into P, SV and SH modes, respectively. Furthermore, we applied the method to an actual 3C-3D field data. As a result, the developed method improved the S/N ratio of P-wave signals and it extracted P-SV converted waves properly. These application results to the synthetic and actual data proof the validity and applicability of this method.

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 In the magnetotelluric (MT) method, long recording times are required to obtain usable readings at frequencies below 1Hz. However, due to the long recording times, signals may be contaminated by coherent noise such as signals generated by DC trains. If low frequency MT data are contaminated by such noise, it was difficult to remove or segregate the contaminated data from the usable data. In this paper, we show the validity of the new weighted stacking method proposed by Negi et al. (2010) using survey data. We used four magnetotelluric sites located between northern and southern regions of the Noto peninsula, central Japan, where MT signals are usually contaminated by coherent noise. In this region, DC train signals are dominant over long periods of time. However, it was easy to estimate the true signal spectrum because the DC trains do not travel late at night. We processed the data acquired at these sites by the weighted stacking method. As a result, we were able to decrease the influence of the low quality data acquired in the time slot dominated by coherent noise. In similar circumstances, it is expected that the weighted stacking method following the proposed algorithm can be used to edit the MT spectrum data efficiency and effectively.

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