Towards a Rapid Integrated Associator for Seismic Events

It is increasingly important to develop automatic and robust systems that can detect, associate, locate, and discriminate all significant seismic events from available global seismometers. Association can be the most challenging step in seismic monitoring, as signals may include other seismic sources and can be inundated with false detection due to natural or anthropogenic noise. Recently, deep neural networks were used to perform phase association with promising results for local or regional earthquakes. However, their performance on diverse global events and network information is unclear. This study focuses on the development of a Deep Neural Network (DNN) seismic associator, a form of an advanced multilateration algorithm using phase timing as well as anticipated event and path pattern recognition, that can operate during network operations. First, we developed a testing dataset, assembled using a catalog of all earthquakes globally between magnitudes 5.5 and 6.5 and years 2000 and 2016 from the ISC Bulletin and using the ISC analyst P-phase determinations, avoiding events with overlapping arrivals. In order to provide the DNN with as much information as possible, we use an autoencoder to identify the principal components of the seismograms in an unsupervised and structured format, in addition to using direct (manually extracted) features that were selected for their relevance to the seismic detection/association problem. Initial results based solely on P-wave detections are promising, and we expect significant improvements with the utilization of the full feature vector (encoded phases generated from an autoencoder and directly extracted features from the respective phases at multiple stations) from the feature extraction.