Surface wave magnitude (M_s) estimation for small events recorded at
near-regional distances will often require a magnitude scale designed for Rayleigh
waves with periods less than 10 sec. We have examined the performance of applying
two previously published M_s scales on 7-sec Rayleigh waves recorded at distances
less than 500 km. First, we modified the Marshall and Basham (1972) M_s scale,
originally defined for periods greater than 10 sec, to estimate surface wave magnitudes
for short-period Rayleigh waves from earthquakes and explosions on or near
the Nevada Test Site. We refer to this modification as ^(M+B) M_s(7), and we have used
short-period, high-quality dispersion curves to determine empirical path corrections
for the 7-sec Rayleigh waves. We have also examined the performance of the Rezapour
and Pearce (1998) formula, developed using theoretical distance corrections
and surface wave observations with periods greater than 10 sec, for 7-sec Rayleigh
waves ^(R+P) (M_S(7)) as recorded from the same dataset. The results demonstrate that both
formulas can be used to estimate M_s for nuclear explosions and earthquakes over a
wider magnitude distribution than is possible using conventional techniques developed
for 20-sec Rayleigh waves. These M_s(7) values scale consistently with other
Ms studies at regional and teleseismic distances with the variance described by a
constant offset; however, the offset for the ^(M+B) M_s(7) estimates is over one magnitude
unit nearer the teleseismic values than the ^(R+P) M_s(7) estimates. Using our technique, it
is possible to employ a near-regional single-station or sparse network to estimate
surface wave magnitudes, thus allowing quantification of the size of both small earthquakes
and explosions. Finally, we used a jackknife technique to determine the false-alarm
rates for the ^(M+B) M_s(7)-m_b discriminant for this region and found that the probability of misclassifying an earthquake as an explosion is 10%, while the probability
of classifying an explosion as an earthquake was determined to be 1.2%. The misclassification
probabilities are slightly higher for the ^(R+P) M_s(7) estimates. Our future
research will be aimed at examining the transportability of these methods