In this study, using a machine learning method, in particular, a deep learning approach, we propose for the first time a model of Beta-Gamma coincidence radioxenon spectra classification. Specifically, by means of real data from the noble gas system in Charlottesville (USX75) as part of the International Monitoring System (IMS) operated by CTBTO between 2012 and 2019, we apply the convolution...
NET-VISA is a Physics-Based Generative Model of global scale seismology. The model includes a description of the generation of events which include under-water and atmospheric events, the propagation of waveform energy from the events in multiple phases, and the detection or mis-detection of these phases at the network of stations maintained by the International Monitoring System (IMS) as well...
Deep Learning Travel Time (DeLTTa) is a deep-learning method and computer code for emulating seismic-phase travel times that are based on a 3-dimensional (3D) Earth model. Greater accuracy of travel time predictions using a 3D Earth model are known to reduce the bias of event location estimates and improve the process of associating detections to events. However, practical use of 3D models is...
In this work, we develop an algorithm for automatic identification of repeating seismic events such as aftershocks and mine explosions. Identification of such events will help to improve the quality of automatic bulletins and to lighten the analysts’ burden. The algorithm constructs a low-dimensional representation of the examined data by using a variant of a non-linear dimensionality...
The International Monitoring System (IMS) infrasound network is well positioned to record atmospheric nuclear explosions, but algorithmically classifying and characterizing these events is challenging. Difficulties stem from the variable and dynamic atmosphere that modulates acoustic transients at regional to global distances. Machine learning (ML) is well suited to classify infrasound...
