Comparison of Frequency-Domain Array Methods for Studying Earthquake Rupture Process

Monday, 15 December 2014
Yixiao Sheng1,2, Jiuxun Yin2 and Huajian Yao2, (1)Stanford University, Department of Geophysics, Stanford, CA, United States, (2)University of Science and Technology of China, Laboratory of Seismology and Physics of Earth’s Interior, Hefei, China
Seismic array methods, in both time- and frequency- domains, have been widely used to study the rupture process and energy radiation of earthquakes. With better spatial resolution, the high-resolution frequency-domain methods, such as Multiple Signal Classification (MUSIC) (Schimdt, 1986; Meng et al., 2011) and the recently developed Compressive Sensing (CS) technique (Yao et al., 2011, 2013), are revealing new features of earthquake rupture processes. We have performed various tests on the methods of MUSIC, CS, minimum-variance distortionless response (MVDR) Beamforming and conventional Beamforming in order to better understand the advantages and features of these methods for studying earthquake rupture processes. We use the ricker wavelet to synthesize seismograms and use these frequency-domain techniques to relocate the synthetic sources we set, for instance, two sources separated in space but, their waveforms completely overlapping in the time domain. We also test the effects of the sliding window scheme on the recovery of a series of input sources, in particular, some artifacts that are caused by the sliding window scheme. Based on our tests, we find that CS, which is developed from the theory of sparsity inversion, has relatively high spatial resolution than the other frequency-domain methods and has better performance at lower frequencies. In high-frequency bands, MUSIC, as well as MVDR Beamforming, is more stable, especially in the multi-source situation. Meanwhile, CS tends to produce more artifacts when data have poor signal-to-noise ratio. Although these techniques can distinctly improve the spatial resolution, they still produce some artifacts along with the sliding of the time window. Furthermore, we propose a new method, which combines both the time-domain and frequency-domain techniques, to suppress these artifacts and obtain more reliable earthquake rupture images. Finally, we apply this new technique to study the 2013 Okhotsk deep mega earthquake in order to better capture the rupture characteristics (e.g., rupture area and velocity) of this earthquake.