USING LARGE-SCALE CORRECTION SURFACES , CROSS-CORRELATION TECHNIQUES AND GENETIC ALGORITHMS

The availability of seismic bulletin sources presents a problem when preparing data sets for studies. With so many choices, which catalog should be used? We have developed a method of merging data from all available seismic bulletins into a single database of non-redundant phases for each event. With this new database, additional ground truth (GT) events are readily identified due to the merging of all possible arrivals for each event. The compilation of over 8500 GT25 or better events in Asia allows the generation of large-scale travel time correction surfaces. We have created Pg, Pn, P, Sg/Lg, Sn, and S surfaces for the 1382 current and historic stations that detected a GT event. The availability of correction surfaces for any and all stations in a large region permit relocations that result in greater accuracy and increased event clustering for entire seismic catalogs. We have adapted a retroactive cross-correlation pick adjustment algorithm to function as a cross-correlation scanning detector. Application of this tool demonstrates the benefits of such procedures in areas having numerous, repeating events that give rise to highly similar waveforms. The adaptive, cross-coherency filtering method in our correlation detector enhances correlation success by comparing only the portions of waveform spectra exhibiting high cross-spectral coherency; this boosts the successful correlation of similar signals while eliminating the unintentional discarding of potentially useful parts of the spectrum which can occur when a priori filters are relied upon. In a recent application we applied the method to a swarm of earthquakes occurring in the middle Rio Grande Rift near Socorro, New Mexico, during October 2005. Initial analyst review of the data stream identified ~300 earthquakes in this seismic swarm, while application of our coherency-based cross-correlation scanner identified over 1600 repeating events. Location of events under conditions of poorly understood seismic velocity models with non-optimal station distribution is an ongoing problem for monitoring efforts. To improve event locations, particularly for increased robustness in depth estimation, we are working with colleagues at Gyeongsang National University, Korea, on a genetic algorithm that simultaneously finds an optimal average one-dimensional (1D) velocity structure and then relocates hypocenters in this model. The genetic-algorithm method for hypocentral parameters (GA-MHYPO) searches for a best-fit model within prescribed limits to minimize theoretical and observed travel time differences, using a two-point ray-tracing method. Once the optimal model has been found for the input data, these and other nearby events are relocated using the model. We are testing the algorithm, using the best-quality events from the Himalayan Nepal Tibet Seismic Experiment (HIMNT) PASSCAL Instrument Center deployment in Nepal and southern Tibet and have obtained improved location statistics for the shallowest events, as well as minor improvements for deeper earthquakes. 29th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies