Interactive analysis of the results of NET-VISA, a Bayesian inference system, in CTBTO’s International Data Centre bulletin production

Abstract

AbstractThe Global Association model is a crucial tool in seismic data analysis at the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization. However, it faces challenges due to its limitations in accurately associating seismic events on a global scale. Over the past years, attempts have been undertaken to tackle these issues by introducing the Network Processing Vertically Integrated Seismic Analysis (NET-VISA) algorithm, specifically designed to enhance seismic event association across the globe. NET-VISA uses a machine learning Bayesian approach to solve the automatic association problem. NET-VISA has been implemented in operation as an additional automatic event scanner tool since January 2018. In this study, we assess the effect of the NET-VISA automatic scanner on the IDC output REB and LEB bulletins. We used three distinct time periods to evaluate the NET-VISA performance. The results show a 4.6% increase in the number of LEB events after including the NET-VISA scanner in operation, with an average of 7 additional events per day, and an increase of 17.90% in the number of scanned events. A comparison between the different bulletins in distinct periods shows NET-VISA is beneficial to build more valid events, providing opportunities to improve nuclear-test-ban monitoring. However, NET-VISA exhibits slightly reduced performance for events occurring at depths exceeding 300 km.