Loic Viens, Tomotaka Iwata

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 non-physical arrivals. We present a two-step method based on unsupervised learning to improve the quality of correlation functions calculated with the deconvolution method (e.g., deconvolution functions, DFs). For a DF dataset calculated between two stations over a long time period, we first reduce the dataset dimensions using the Principal Component Analysis and cluster the features of the low-dimensional space with a Gaussian mixture model. We stack the DFs belonging to each cluster together and select the best stacked DF. We apply our technique to DFs calculated every 30 minutes between an offshore station located on top of the Nankai Trough, Japan, and 77 onshore receivers. Our method removes spurious arrivals and improves the signal-to-noise ratio of the 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 way to improve the prediction of long-period ground motions from potential megathrust earthquakes.