Seismic monitoring for underground nuclear explosions answers three questions for all global seismic activity: Where is the seismic event located? What is the event source type (event identification)? If the event is an explosion, what is the yield? The answers to these questions involves processing seismometer waveforms with propagation paths predominately in the mantle. Four discriminants commonly used to identify teleseismic events are depth from travel time, presence of long-period surface energy (m b vs. M S ), depth from reflective phases, and polarity of first motion. The seismic theory for these discriminants is well established in the literature (see, for example, Blandford [1982] and Pomeroy et al. [1982]). However, the physical basis of each has not been formally integrated into probability models to account for statistical error and provide discriminant calculations appropriate, in general, for multidimensional event identification. This article develops a mathematical statistics formulation of these discriminants and offers a novel approach to multidimensional discrimination that is readily extensible to other discriminants. For each discriminant a probability model is formulated under a general null hypothesis of H 0 : Explosion Characteristics. The veracity of the hypothesized model is measured with a p-value calculation (see Freedman et al. [1991] and Stuart et al. [1994]) that can be filtered to be approximately normally distributed and is in the range [0, 1]. A value near zero rejects H 0 and a moderate to large value indicates consistency with H 0 . The hypothesis test formulation ensures that seismic phenomenology is tied to the interpretation of the p-value. These p-values are then embedded into a multidiscriminant algorithm that is developed from regularized discrimination methods proposed by DiPillo (1976), Smidt andMcDonald (1976), andFriedman (1989). Performance of the methods is demonstrated with 102 teleseismic events with magnitudes (m b ) ranging from 5 to 6.5. Example p-value calculations are given for two of these events.
Earthquakes and explosions generate seismic waveforms that have different characteristics. However, the challenge of confidently differentiating between these two signatures is complex, and requires the integration of physical and statistical techniques. This article reviews the methods for constructing discrimination features from diverse physical observations. These discrimination features are appropriate for many statistical classification frameworks. Under the null hypothesis an event is an explosion, we discuss strategies for constructing P-values which can be interpreted as standardized discrimination features. We develop standardized discriminants for both teleseismic (simple propagation path in the mantle) and regional (complicated propagation path in the crust) events, following the trend toward characterizing increasingly smaller single-point explosions.
SUMMARY Event screening is an explosion monitoring practice that aims to identify an event as an explosion (‘screened in’) or not (‘screened out’). Confidence in event screening can be increased if multiple independent approaches are used. We describe a new approach to event screening using the seismic moment tensor and its representation on the hypersphere, specifically the 5-sphere of 6-degree unit vectors representing the normalized symmetric moment tensor. The sample of moment tensors from an explosion data set is unimodal on the 5-sphere and can be parametrized by the Langevin distribution, which is sometimes referred to as the Normal distribution on the hypersphere. Screening is then accomplished by finding the angle from the explosion population mean to any newly measured moment tensor and testing if that angle is in the tail of the Langevin distribution (conservatively quantified as greater than 99.9 per cent of the cumulative density). We apply the screen to a sample of earthquakes from the Western USA and the September 2017 explosion and subsequent collapse at the Pungyye-Ri Test Site in North Korea. All the earthquakes and the collapse screen out, but the explosion does not.
On 4 August 2020 Lebanon’s capital, Beirut, was rocked by a sequence of colocated fires and chemical explosions that left hundreds of people dead, thousands injured and homeless, demolished the city’s seaport, and heavily damaged the surrounding neighborhoods and businesses. The event was well recorded by many regional seismic stations in and around the eastern Mediterranean Sea. Using a network of 58 stations, 105 regional seismic phases, and a Bayesian methodology places the event at 1.8 km south of the ground-truth location, the seaport warehouse. Achieving this accuracy is significant, considering very limited local seismic data were available to use in this study. The location bias is attributed, in large part, to a small but statistically significant difference in the Moho velocity for sea paths compared with continental paths. The depth to the Moho is generally consistent with the iasp91 model. Concurrent to the port explosion is a series of unrelated small explosions, 11 s apart, attributed to a seismic survey that was being carried out at the time in the eastern Mediterranean Sea using air guns.
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