Seismic-phase detection using multiple deep learning models for global and local representations of waveforms

The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning technologies have enabled the automatic detection of earthquakes from waveform data. In particular, various state-of-the-art deep-learning methods have been applied to this endeavour. In this study, we proposed and tested a novel phase detection method employing deep learning, which is based on a standard convolutional neural network in a new framework. The novelty of the proposed method is its separate explicit learning strategy for global and local representations of waveforms, which enhances its robustness and flexibility. Prior to modelling the proposed method, we identified local representations of the waveform by the multiple clustering of waveforms, in which the data points were optimally partitioned. Based on this result, we considered a global representation and two local representations of the waveform. Subsequently, different phase detection models were trained for each global and local representation. For a new waveform, the overall phase probability was evaluated as a product of the phase probabilities of each model. This additional information on local representations makes the proposed method robust to noise, which is demonstrated by its application to the test data. Furthermore, an application to seismic swarm data demonstrated the robust performance of the proposed method compared with those of other deep learning methods. Finally, in an application to low-frequency earthquakes, we demonstrated the flexibility of the proposed method, which is readily adaptable for the detection of low-frequency earthquakes by retraining only a local model

細胞内における遺伝子制御ネットワークの解明を目的とした粒子フィルタとMCMCによるハイブリッド版パラメータ推定法の開発

Open House, ISM in Tachikawa, 2010.7.9統計数理研究所オープンハウス（立川）、H22.7.9ポスター発

線虫細胞内における細胞質流動のデータ同化研究

Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス（立川）、H24.6.15ポスター発

Experimental characterization of the electronic structure of anatase TiO2: Thermopower modulation

Thermopower (S) for anatase TiO2 epitaxial films (n3D: 1E17-1E21 /cm3) and the gate voltage (Vg) dependence of S for thin film transistors (TFTs) based on TiO2 films were investigated to clarify the electronic density of states (DOS) around the conduction band bottom. The slope of the |S|-log n3D plots was -20 {\mu}V/K, which is an order magnitude smaller than that of semiconductors (-198 {\mu}V/K), and the |S| values for the TFTs increased with Vg in the low Vg region, suggesting that the extra tail states are hybridized with the original conduction band bottom.Comment: 11 pages, 4 figure

Adjoint-based uncertainty quantification for inhomogeneous friction on a slow-slipping fault

Long-term slow-slip events (LSSEs) usually occur on the deep, shallow parts of subducting plates and have substantial relation with adjacent megathrust fault motion. Conventional techniques of quantifying slow earthquake frictional features show that these features may be indicative of predictive seismic motion; however, quantifying high-accuracy uncertainty of the frictional fields has not yet been achieved. We therefore propose a method of uncertainty quantification for spatially inhomogeneous frictional features from slip motion on an LSSE fault--megathrust fault complex in southwestern Japan. By combining a fault motion model that mimics slow-slip motion and a variational data assimilation (DA) technique using a second-order adjoint method, we have succeeded in quantifying the spatial distribution of the uncertainty of the frictional features. Further, evaluation of the spatial distribution in high-resolution reveals the correlation between the dynamics of the slow-slip motion and the important components of the frictional features, which is valuable information for observational DA design. Findings from this study are expected to advance the theoretical foundation of applied seismic motion prediction techniques using slow-slip frictional features as stress meters for megaquakes, as well as improve understanding of the relationship between the slow-slip motion and frictional parameters of a fault

気圧変動で地震を診る

Open House, ISM in Tachikawa, 2011.7.14統計数理研究所オープンハウス（立川）、H23.7.14ポスター発

粒子フィルタを用いたクラウド計算サービス

平成21年度研究報告会、統計数理研究所(広尾）、H22.3.18-19ポスター発