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Science Applications International Corporation
1300 N. 17th Street
Arlington, Virginia 22209
(X.Y., I.B., J.B., H.I., K.M.)
University of Colorado
Department of Physics
Boulder, Colorado 80309
(M.R., N.S.)
Harvard University
Department of Geology
Cambridge, Massachusetts 02138
(M.A., G.E.)
Recently developed 3D global seismic velocity models have demonstrated location improvements through independent regional and teleseismic travel-time calibration. Concurrently, a large set of high-quality ground-truth (GT) events with location accuracies 10 km or better (GT0-GT10) has been collected for Europe, the Mediterranean, North Africa, the Middle East, and western Eurasia. In this study we demonstrate event location improvements using this new data set by applying the regional and teleseismic model-based travel-time calibrations (independently and jointly). Besides relocating events using all arrivals, a subset of the GT events was also relocated using controlled station geometries generated from a constrained boot-strapping technique. This approach simulates sparse networks and reduces the effect of correlated errors to ensure valid 90% error ellipse coverage statistics. With respect to the GT events, we compared event relocations, with and without travel-time calibrations, considering statistics of mislocation, error ellipse area, 90% coverage, origin time bias, origin time errors, and misfit. Relocations of over 1200 GT events show that Pn and/or P calibration reduced mislocation for 60%–70% of the events. Joint regional Pn and teleseismic P travel-time calibration provided the largest location improvements and achieved approximately GT5 accuracy levels. Due to correlated errors, event locations using large numbers of stations have deficient 90% error ellipse coverage. However, the coverages derived from the model errors are appropriate for the simulated sparse regional and teleseismic networks. Our validation effort demonstrates that the global model-based calibrations of Pn and teleseismic P travel times reduce both location bias and uncertainty over wide areas.
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