This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground-motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.
The pathogenesis of immune-mediated myocarditis depends on genetic and environmental factors. To study the genetic mechanisms, we have developed a model of experimental autoimmune myocarditis in the A.SW mouse. Here we provide evidence that loci on murine chromosome 6, and possibly chromosome 1, are involved in regulating susceptibility. Moreover, these loci overlap with loci implicated in other autoimmune diseases including diabetes in the NOD mouse. These two loci also regulate apoptosis in thymocytes as well as peripheral T cells in the NOD mouse, and we report further that A.SW mice demonstrate the same characteristics in apoptosis. These results suggest that common pathogenetic mechanisms involving apoptosis of both thymic and peripheral T cells are shared by multiple autoimmune diseases.
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