4.5 Article

Body-Wave Methods of Distinguishing between Explosions, Collapses, and Earthquakes: Application to Recent Events in North Korea

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SEISMOLOGICAL RESEARCH LETTERS
卷 89, 期 6, 页码 2131-2138

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SEISMOLOGICAL SOC AMER
DOI: 10.1785/0220180128

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  1. U.S. Department of Energy by LLNL [DE-AC52-07NA27344]

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Examination of regional distance seismic data from historic nuclear test sites has led to a variety of very effective discriminants between explosions, earthquakes, and collapses. We focus on the body-wave methods. We show that ratios between P- and S-wave amplitudes (P/S ratios) above about similar to 2 Hz very effectively separate the six Democratic People's Republic of Korea (DPRK) declared nuclear tests between 2006 and 2017 from natural earthquakes in the region. Similarly, P/S ratios separate historic Nevada Test Site (NTS) nuclear explosions from western U.S. earthquakes. We show that combining P/S ratios with ratios of low-frequency to high-frequency S-wave amplitudes can effectively separate postexplosion collapse events, such as the 1982 NTS Atrisco collapse, and the apparent collapse that followed about eight and a half minutes after the 3 September 2017 DPRK explosion. Explosions often produce fewer and smaller aftershocks than comparably sized earthquakes, which has been proposed as a potential discriminant. We apply the body-wave techniques to the recent seismicity following the largest DPRK event, after first using correlation methods to build a more complete catalog of these events. Despite the empirical effectiveness of the regional body-wave discriminants, the physical basis for the generation of explosion S waves, and therefore the predictability of P/S and low/high frequency techniques, as a function of path, frequency, and event properties such as size, depth, and geology, remains incompletely understood. A goal of current research, such as the Source Physics Experiments (SPE), is to improve our physical understanding of the mechanisms of explosion S-wave generation and advance our ability to numerically model and predict them.

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