Abstracts Online
TRANSFER FUNCTIONS AND SEISMIC DISCRIMINATION
RENWALD, M.D. and WALLACE, T.C., Southern Arizona Seismic Observatory, Department of Geosciences, University of Arizona, Tucson, AZ 85721; and TAYLOR, S.R., Los Alamos National Laboratory, EES-11, Los Alamos, NM 87545.
Seismic monitoring of any nuclear test moratorium or treaty requires both the detection and the identification of underground explosions. Although there are fundamental source physics which can predict the character of a seismic recording from an explosion, experience indicates that the discrimination between explosions and earthquakes is highly station centric. This is particularly true for recordings at regional distances where propagation effects associated with a complex crustal waveguide can overprint source effects. As new stations are installed in official monitoring networks, the lack of recording history can dramatically affect confidence in discrimination. While some of the monitoring stations have established recording histories, a significant portion have never recorded a nuclear explosion. To address this problem, we are investigating procedures to “map” the recording history of a long-operating station on to a newly installed station. This amounts to developing a transfer function to predict the recording at a given seismic station from the actual recording at a nearby station. To accomplish this, we have used a data set consisting of waveforms from nuclear explosions detonated at the Lop Nor test site in western China as well as earthquakes within a 100 km radius. The waveforms were recorded at the ten-station Kyrgyz [KNET] array located in Kyrgyzstan approximately 1100 km from Lop Nor. The data spans from 1992 through 1999 and contains six known explosions and nine earthquakes. The KNET stations are all very broadband (STS-2 seismometers), and have station separations ranging from 30 km to 250 km. The KNET stations are located in variety of geologic environments, making the assembled data set a good test bed for investigating both the variability of spectral measurements as well as the scale lengths for predicting station-to-station correlations.
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