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An evaluation of generalized likelihood ratio outlier detection to identification of seismic events in western China

Earth and Environmental Sciences Division Geophysics Group, EES-3 Los Alamos National Laboratory, Los Alamos, New Mexico 87545

Abstract

The generalized likelihood ratio outlier detection technique for seismic event identification is evaluated using synthetic test data and frequency-dependent P_g/L_g measurements from western China. For most seismic stations that are to be part of the proposed International Monitoring System (IMS) for the Comprehensive Test Ban Treaty (CTBT), there will be few or no nuclear explosions in the magnitude range of interest (e.g., m_b < 4) on which to base an event-identification system using traditional classification techniques. Outlier detection is a reasonable alternative approach to the seismic discrimination problem when no calibration explosions are available. Distance-corrected P_g/L_g data in seven different frequency bands ranging from 0.5 to 8 Hz from the Chinese Digital Seismic Station WMQ are used to evaluate the technique. The data are collected from 157 known earthquakes, 215 unknown events (presumed earthquakes and possibly some industrial explosions), and 18 known nuclear explosions (1 from the Chinese Lop Nor test site and 17 from the East Kazakh test site). A feature selection technique is used to find the best combination of discriminants to use for outlier detection. Good discrimination performance is found by combining a low-frequency (0.5 to 1 Hz) P_g/L_g ratio with high-frequency ratios (e.g., 2 to 4 and 4 to 8 Hz). Although the low-frequency ratio does not discriminate between earthquakes and nuclear explosions well by itself, it can be effectively combined with the high-frequency discriminants. Based on the tests with real and synthetic data, the outlier detection technique appears to be an effective approach to seismic monitoring in uncalibrated regions.

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