@article {fang_sequencing_2023,
	title = {Sequencing Seismic Noise Correlations for Improving Surface Wave Retrieval and Characterizing Noise Sources},
	journal = {Seismological Research Letters},
	volume = {95},
	number = {2A},
	year = {2023},
	month = {03/2024},
	pages = {848{\textendash}858},
	abstract = {Cross-correlating continuous seismic data is a commonly employed technique to extract coherent signals to image and monitor the subsurface. However, due largely to site effects and poorly characterized noise sources in oceanic environments, its application to ocean-bottom seismometer (OBS) recordings often requires additional processing. In this contribution, we propose a method to improve the quality of the retrieved surface waves from OBS data and characterize the noise sources. We first cluster the pre-stack noise cross-correlation functions (NCFs) based on a sequencing algorithm, followed by selectively stacking those consisting of coherent and stable signals that are consistent with predicted surface-wave arrival times. Synthetic tests show that the sequenced NCFs can be used to recover the spatial and temporal distribution of noise sources. Applying the method to an OBS array offshore California increases the signal-to-noise ratios of the obtained Rayleigh waves. In addition, we find that the annual temporal distribution of selected NCFs with frequencies ranging from 0.04 to 0.1~Hz is nearly homogeneous during the recording period. In contrast, many NCFs excluded for stacking are temporally clustered. This method has the potential to be applied to other OBS recordings or possibly onland deployments, thus helping to obtain high-quality surface waves and to analyze temporal noise source characteristics.},
	issn = {0895-0695},
	doi = {10.1785/0220230151},
	url = {https://doi.org/10.1785/0220230151},
	author = {Fang, Hongjian}
}
