Towards Global Seismic Monitoring of Underground Nuclear Explosions Using Waveform Cross Correlation. Part Ⅱ: Synthetic Master Events

摘要

Waveform cross correlation is an efficient tool for detection and characterization of seismic signals. For the purposes of the Comprehensive Nuclear-Test-Ban Treaty, cross correlation can globally reduce the threshold of detection by 0.3 to 0.4 magnitude units. However, the technique critically depends on the availability of master events. In Part Ⅰ of this paper, we have demonstrated that in seismically active regions the best master events (grand-masters) replicated over a regular grid allow improving the efficiency of signal detection and event finding. In aseismic areas, there are two approaches to populate the global grid of master events for the International Monitoring System: the replication of grand-masters and calculation of synthetic seismograms for master-events in the global grid nodes. The efficiency of synthetic templates depends on the accuracy of shape and amplitude predictions controlled by focal depth and mechanism, source function, velocity structure and attenuation along the master/station path. Here we test three focal mechanisms (explosion, thrust fault, and actual Harvard CMT solution for one of the April 11,2012 Sumatra aftershocks) and two velocity structures (ak135 and CRUST 2.0). Sixteen synthetic master events were distributed over a 1° × 1° grid covering the zone of aftershocks. We built five cross correlation standard event lists (XSEL) and compared detections and events with those built using the real and grand master events as well as with the Reviewed Events Bulletin of the International Data Centre. The XSELs were built using an explosion source and akl35 and the reverse fault with isotropic radiation pattern to demonstrate the performance similar to that of the real and grand masters. Here we have proved quantitatively that it is possible to cover all aseismic areas with synthetic masters without significant loss in seismic monitoring capabilities based on cross correlation.