The traditional M-s:m(b) discrimination method is routinely used for distinguishing between earthquakes and explosions within dense networks, but there is a need to improve discrimination at smaller magnitudes; therefore, we need magnitude scales that can successfully be applied to data from sparse networks. We developed a unified Rayleigh- and Love-wave magnitude scale (MsU) that is designed to maximize available information from single stations and then combine magnitude estimates into network averages. By combining Love- and Rayleigh-wave amplitudes, we minimize the effect of earthquake radiation patterns from sparse networks, thereby improving discrimination between earthquakes and explosions. MsU is built from M-s(V-MAX) (Russell, 2006) and is calculated from Love and Rayleigh waves that are narrowband filtered and corrected for propagation and source effects at periods between 8 and 25 s to find filter bands of maximum energy propagation. The data are also corrected for censoring effects at the station level, because either Rayleigh or Love waves may be below the signal-to-noise ratio threshold at a given period. We applied MsU to 39 earthquakes (3.21 < M-w < 5.08) located in the Yellow Sea and Korean Peninsula region, as well as to the three North Korean nuclear tests (4.1 < m(b) < 5.1). By using MsU:m(b) as a discriminant, there is an increase in the separation of small magnitude earthquakes and explosions in sparse networks and a significant reduction in outliers, as shown in the application from the Korean Peninsula. This research addresses the theory, methods, and capability of MsU as a discriminant.
