Rotation Polarization Analysis With Single-Station Six-Component Seismometer

Accurate wave component separation is essential for seismic signal processing, playing a key role in seismic source localization and dispersion curve inversion. However, traditional three-component polarization filters struggle to distinguish various wave components due to complex wave propagation and ambient noise. Current single-station six-component methods emphasize wave type identification but lack detailed time-frequency information, limiting their applicability in certain scenarios. To address these limitations, we present a novel algorithm that effectively isolates circularly polarized components using single-station six-component data, offering detailed polarization parameters across time and frequency domains. To validate our algorithm, we conducted a series of experiments, using back azimuth estimation as a key performance metric. Compared to the traditional 3-C method, the proposed algorithm reduces the mean absolute error by 79.81% and the standard deviation by 83.07%. These improvements demonstrate its superior accuracy in distinguishing various wave components and its enhanced convergence in back azimuth estimation. Additionally, it achieves comparable performance with shorter data while providing more time-frequency domain information. These results confirm the advanced capability of the algorithm to accurately isolate different wave components.