气候变化领域集成服务门户(CCPortal): Improving the Retrieval of Offshore-Onshore Correlation Functions With Machine Learning

CCPortal

DOI	10.1029/2020JB019730
	Improving the Retrieval of Offshore-Onshore Correlation Functions With Machine Learning
	Viens L.; Iwata T.
发表日期	2020
ISSN	21699313
卷号	125 期号:8
英文摘要	The retrieval of reliable offshore-onshore correlation functions is critical to improve our ability to predict long-period ground motions from megathrust earthquakes. However, localized ambient seismic field sources between offshore and onshore stations can bias correlation functions and generate nonphysical arrivals. We present a two-step method based on unsupervised learning to improve the quality of correlation functions calculated with the deconvolution technique (e.g., deconvolution functions, DFs). For a DF data set calculated between two stations over a long time period, we first reduce the data set dimensions using the principal component analysis and cluster the features of the low-dimensional space with a Gaussian mixture model. We then stack the DFs belonging to each cluster together and select the best stacked DF. We apply our technique to DFs calculated every 30 min between an offshore station located on top of the Nankai Trough, Japan, and 78 onshore receivers. Our method removes spurious arrivals and improves the signal-to-noise ratio of DFs. Most 30-min DFs selected by our clustering method are generated during extreme meteorological events such as typhoons. To demonstrate that the DFs obtained with our method contain reliable phases and amplitudes, we use them to simulate the long-period ground motions from a Mw 5.8 earthquake, which occurred near the offshore station. Results show that the earthquake long-period ground motions are accurately simulated. Our method can easily be used as an additional processing step when calculating offshore-onshore DFs and offers a new way to improve the prediction of long-period ground motions from potential megathrust earthquakes. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	clustering; correlation functions; long-period ground motions; machine learning; Nankai Trough; seismic interferometry
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187699
作者单位	Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto, Japan
推荐引用方式 GB/T 7714	Viens L.,Iwata T.. Improving the Retrieval of Offshore-Onshore Correlation Functions With Machine Learning[J],2020,125(8).
APA	Viens L.,&Iwata T..(2020).Improving the Retrieval of Offshore-Onshore Correlation Functions With Machine Learning.Journal of Geophysical Research: Solid Earth,125(8).
MLA	Viens L.,et al."Improving the Retrieval of Offshore-Onshore Correlation Functions With Machine Learning".Journal of Geophysical Research: Solid Earth 125.8(2020).