Subdaily Ambient Noise Monitoring at Parkfield, California, by Combining C1 and C3

Abstract

Abstract Monitoring the temporal variation in seismic velocity plays a critical role in understanding the dynamic processes of the subsurface at different scales. Many seismic velocity changes related to earthquakes and volcanic activities have been obtained using ambient noise correlation in recent years; however, their temporal resolution is limited, typically from a few to dozens of days, which makes it challenging to explore the valuable but short-duration changes in subsurface media. In this article, we develop a method based on the correlation of the coda of the ambient noise correlation (C3) with a multiple-component combination and introduced singular value decomposition-based Wiener filter denoising technique. Using permanent network data, we achieved subdaily ambient noise monitoring at Parkfield, California, using 4-hr cross-correlation stacking with 2-hr step. We identified that the maximum seismic velocity drop delayed the mainshock of the 2004 Mw 6.0 Parkfield earthquake by ∼41 hr, during which the temporal velocity process may have been affected by strong aftershocks, including an Mw 5.0 aftershock that occurred one day after the mainshock; however, no significant precursory change was detected. Our method provides an opportunity for monitoring the short-term change of underground structures based on the widely distributed seismic networks. In addition, the idea of obtaining reliable subsurface information within a short time through high-order noise correlation in this work has important enlightenment for ambient noise imaging and monitoring in broader fields.