Enormous volumes of continuous seismic data have been acquired from seismograph networks over the past decade, with these data sets consisting of observations from multiple seismic stations. Dense seismograph networks, such as the Japanese Metropolitan Seismic Observation network (MeSO-net) and the Southern California Seismic Network, monitor real-time seismicity and provide continuous waveforms from their respective network stations. Efficient and thorough analyses of these data sets should be of great benefit to seismology. The main objective of the present work, which represents a novel approach to and advance in seismic data analysis, is the development of an improved earthquake detection technique for these massive seismic network data sets.In recent years, deep neural networks have been attracting increasing interest as tools for analyzing such complex big data in many applied fields such as image processing (

Enormous volumes of continuous seismic data have been acquired from seismograph networks over the past decade, with these data sets consisting of observations from multiple seismic stations. Dense seismograph networks, such as the Japanese Metropolitan Seismic Observation network (MeSO-net) and the Southern California Seismic Network, monitor real-time seismicity and provide continuous waveforms from their respective network stations. Efficient and thorough analyses of these data sets should be of great benefit to seismology. The main objective of the present work, which represents a novel approach to and advance in seismic data analysis, is the development of an improved earthquake detection technique for these massive seismic network data sets.In recent years, deep neural networks have been attracting increasing interest as tools for analyzing such complex big data in many applied fields such as image processing (

The matched filter technique is often used to detect microearthquakes such as deep low-frequency (DLF) earthquakes. It compares correlation coefficients (CC) between waveforms of template earthquakes and the observed data. Conventionally, the sum of CC at multiple seismic stations is used as an index to detect the DLF earthquakes. A major disadvantage of the conventional method is drastically reduced detection accuracy when there are too few seismic stations. The new matched filter method proposed in this study can accurately detect microearthquakes using only a single station. It adopts mutual information (MI) in addition to CC to measure the similarity between the template and target waveforms. The method uses the product of MI and CC (MICC) as an index to detect DLF earthquakes. This index shows a distinct peak corresponding to an earthquake signal in a synthetic data set consisting of artificial noise and the waveform of a DLF earthquake. Application of this single-station method to field observations of Kirishima volcano, one of the most active volcanoes in Japan, detected a total of 354 events from the data in December 2010, whereas the catalog of the Japan Meteorological Agency shows only two. Of the detected events, 314 (89%) are likely DLF earthquakes and other events may be false detections. Most of the false detections correspond to surface-wave arrivals from teleseismic events. The catalog of DLF earthquakes constructed here shows similar temporal behavior to that found by the conventional matched filter method using the sum of the CC of the six stations near the volcano. These results suggest that the proposed method can greatly contribute to the accurate cataloging of DLF earthquakes using only a single seismic station. Graphical Abstract

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We discuss the modeling of temporal dominance of sensations (TDS) data, time series data appearing in sensory analysis, that describe temporal changes of the dominant taste in the oral cavity. Our aims were to obtain the transition process of attributes (tastes and mouthfeels) in the oral cavity, to express the tendency of dominance durations of attributes, and to specify factors (such as sex, age, food preference, dietary habits, and sensitivity to a particular taste) affecting dominance durations, simultaneously. To achieve these aims, we propose an analysis procedure applying models based on the semi-Markov chain and the negative binomial regression, one of the generalized linear models. By using our method, we can take differences among individual panelists and dominant attributes into account. We analyzed TDS data for milk chocolate with the proposed method and verified the performance of our model compared with conventional analysis methods. We found that our proposed model outperformed conventional ones; moreover, we identified factors that have effects on dominance durations. Results of an experiment support the importance of reflecting characteristics of panelists and attributes.