[S-TT40] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

[STT40-P01] Fast and high-precision estimation for temporal variation of aftershocks immediately after a main shock with Gaussian process regression

*Kosuke Morikawa¹, Hiromichi Nagao^2,3, Shin-ichi Ito^2,3, Shinichi Sakai^2,5, Naoshi Hirata^2,4 (1.Osaka University, 2.Earthquake Research Institute, The University of Tokyo,, 3.Graduate School of Information Science and Technology, 4.NIED, 5.Interfaculty Initiative in Information Studies, The University of Tokyo)

Keywords:Statistical seismology, Gaussian process regression, Noise contrastive estimation

A large main shock often makes it challenging to identify a number of subsequent aftershocks, and it distorts estimates for the distribution of magnitudes and arrival times of the aftershocks. We early proposed a method to correct the bias with high accuracy by incorporating a detection function with the Gaussian process regression, a flexible nonparametric Bayesian method. However, the algorithm estimating hyperparameters takes much time due to the MCMC (Markov chain Monte Carlo) sampling and needs to know tuning parameters beforehand. In this talk, we propose a fast estimation algorithm to estimate the hyperparameters by using the technique of noise contrastive estimation ( NCE). We apply our proposed algorithm to various earthquake catalogs and show that the proposed method can estimate hyperparameters fast in keeping the high precision for estimating the target parameters.