摘要：Energy generated from earthquake (EQ) is transferred to the ionosphere and results in co-seismic ionospheric disturbances (CID). CID can be observed in the ionospheric combination using L1, L2 frequency carrier phase. As ionospheric trend due to normal conditions such as elevation angle of satellites is generally larger than disturbances, a proper measure is required to extract disturbance signals. Derivative, or sequential combination, is a simple and effective way to remove the normal trend in the ionospheric delay. When using derivative, however, disturbance signals can often be obscured by noise due to its small amplitude. In order to reduce the noise while preserving the time rate of data, and thus to improve signal-to-noise ratio (SNR), we designed a new derivative method using optimization under a couple of assumptions. With simulation data, it is found that N, the number of epochs used for sequential combination, turned out to be the best when N=160 with maximum SNR. Finally, the proposed algorithm’s SNR was compared to that of the previous study which also used derivative method. 120~260% improvements were observed for the proposed method compared to the conventional method.
其他摘要：Energy generated from earthquake (EQ) is transferred to the ionosphere and results in co-seismic ionospheric disturbances (CID). CID can be observed in the ionospheric combination using L1, L2 frequency carrier phase. As ionospheric trend due to normal conditions such as elevation angle of satellites is generally larger than disturbances, a proper measure is required to extract disturbance signals. Derivative, or sequential combination, is a simple and effective way to remove the normal trend in the ionospheric delay. When using derivative, however, disturbance signals can often be obscured by noise due to its small amplitude. In order to reduce the noise while preserving the time rate of data, and thus to improve signal-to-noise ratio (SNR), we designed a new derivative method using optimization under a couple of assumptions. With simulation data, it is found that N, the number of epochs used for sequential combination, turned out to be the best when N=160 with maximum SNR. Finally, the proposed algorithm’s SNR was compared to that of the previous study which also used derivative method. 120~260% improvements were observed for the proposed method compared to the conventional method.
