Machine Learning and Data Mining for Comprehensive Test Ban Treaty Monitoring

Russell, S ; Vaidya, S

关键词: ACCURACY;    CTBT;    DATA ANALYSIS;    DETECTION;    LEARNING;    MINING;    MONITORING;    PROLIFERATION;    REFINING;    TESTING;    VERIFICATION;   

DOI  :  10.2172/967289
RP-ID  :  LLNL-TR-416780
PID  :  OSTI ID: 967289
Others  :  TRN: US200923%%541

学科分类：社会科学、人文和艺术（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The Comprehensive Test Ban Treaty (CTBT) is gaining renewed attention in light of growing worldwide interest in mitigating risks of nuclear weapons proliferation and testing. Since the International Monitoring System (IMS) installed the first suite of sensors in the late 1990's, the IMS network has steadily progressed, providing valuable support for event diagnostics. This progress was highlighted at the recent International Scientific Studies (ISS) Conference in Vienna in June 2009, where scientists and domain experts met with policy makers to assess the current status of the CTBT Verification System. A strategic theme within the ISS Conference centered on exploring opportunities for further enhancing the detection and localization accuracy of low magnitude events by drawing upon modern tools and techniques for machine learning and large-scale data analysis. Several promising approaches for data exploitation were presented at the Conference. These are summarized in a companion report. In this paper, we introduce essential concepts in machine learning and assess techniques which could provide both incremental and comprehensive value for event discrimination by increasing the accuracy of the final data product, refining On-Site-Inspection (OSI) conclusions, and potentially reducing the cost of future network operations.