{'fraction_non_alphanumeric': 0.02702702702702703, 'fraction_numerical': 0.004158004158004158, 'mean_word_length': 5.197424892703863, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spatio-temporal distribution of afterslip due to the 2011 Tohoku-Oki earthquake from MCMC inversion

We use a method based on a Markov chain Monte Carlo (MCMC) Method to estimate the spatiotemporal distribution of afterslip. The conventional inversion method, such as least squares method, estimates one solution for each unknown parameter. On the other hand, MCMC method estimates probability density functions (PDFs) of each unknown parameter. Especially, MCMC method represents an under-determined problem as the correlation between each solution. To estimate afterslip distribution, the observation equation, representing a relation between observed data and the afterslip distribution, is written as d = G m . Here, d is an observation data, m is afterslip of every sub-fault in strike and dip directions, and G is a Green function that is the coefﬁcient matrix which deﬁnes a relation between d and m , and including the coefﬁcient of Laplacian smoothing parameter. In this study, we use a Green function considered a 3-dimensional heterogeneous structure in northeast Japan, which is produced by 3D Finite Elements Method. In the smoothing parameter, we employ weighted Laplacian smoothing regularized by scale of the Green function. The sampling method of MCMC is Metropolis-Hastings algorithm. In order to enhance computational speed, we use GPU (Graphics Processing Unit), because MCMC method needs large amounts of calculations. the