Statistical algorithms for a comprehensive test ban treaty discrimination framework

Abstract

Seismic discrimination is the process of identifying a candidate seismic event as an earthquake or explosion using information from seismic waveform features (seismic discriminants). In the CTBT setting, low energy seismic activity must be detected and identified. A defensible CTBT discrimination decision requires an understanding of false-negative (declaring an event to be an earthquake given it is an explosion) and false-position (declaring an event to be an explosion given it is an earthquake) rates. These rates are derived from a statistical discrimination framework. A discrimination framework can be as simple as a single statistical algorithm or it can be a mathematical construct that integrates many different types of statistical algorithms and CTBT technologies. In either case, the result is the identification of an event and the numerical assessment of the accuracy of an identification, that is, false-negative and false-positive rates. In Anderson et al., eight statistical discrimination algorithms are evaluated relative to their ability to give results that effectively contribute to a decision process and to be interpretable with physical (seismic) theory. These algorithms can be discrimination frameworks individually or components of a larger framework. The eight algorithms are linear discrimination (LDA), quadratic discrimination (QDA), variably regularized discrimination (VRDA), flexible discrimination (FDA), logistic discrimination, K-th nearest neighbor (KNN), kernel discrimination, and classification and regression trees (CART). In this report, the performance of these eight algorithms, as applied to regional seismic data, is documented. Based on the findings in Anderson et al. and this analysis: CART is an appropriate algorithm for an automated CTBT setting.