[S-TT37] 最先端ベイズ統計学が拓く地震ビッグデータ解析

[STT37-P04] Forecasting temporal variation of aftershocks immediately after a main shock using Gaussian process regression

*森川 耕輔¹、長尾 大道^2,3、伊藤 伸一^2,3、寺田 吉壱^1,4、酒井 慎一^2,5、平田 直^2,6 (1.大阪大学、2.東京大学地震研究所、3.東京大学大学院情報理工学系研究科、4.理研AIP、5.東京大学大学院情報学環・学際情報学府、6.防災科学技術研究所)

キーワード：統計地震学、時系列解析、ガウス過程回帰

Uncovering the distribution of magnitudes and arrival times of aftershocks is a key to comprehending the characteristics of earthquake sequences, which enables us to predict seismic activities and conduct hazard assessments. However, identifying the number of aftershocks immediately after the main shock is practically difficult due to contaminations of arriving seismic waves. To overcome this difficulty, we construct a likelihood based on the detected data, incorporating a detection function to which Gaussian process regression (GPR) is applied. The GPR is capable of estimating not only the parameters of the distribution of aftershocks together with the detection function, but also credible intervals for both the parameters and the detection function. The property that the distributions of both the Gaussian process and aftershocks are exponential functions leads to an efficient Bayesian computational algorithm to estimate hyperparameters. After its validation through numerical tests, the proposed method is retrospectively applied to the catalog data related to the 2004 Chuetsu earthquake for the early forecasting of the aftershocks. The results show that the proposed method stably and simultaneously estimates distribution parameters and credible intervals, even within 3hours after the main shock.