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- W2483630810 abstract "[1] We extend the Bayesloc seismic multiple-event location algorithm for application to global arrival time data sets. Bayesloc is a formulation of the joint probability distribution spanning multiple-event location parameters, including hypocenters, travel time corrections, pick precision, and phase labels. Stochastic priors may be used to constrain any of the Bayesloc parameters. Markov Chain Monte Carlo sampling is used to draw samples from the joint probability distribution, and the posteriori samples are summarized to infer conventional location parameters such as the hypocenter. The first application of the broad area Bayesloc algorithm is to a data set consisting of all well-recorded events in the Middle East and the most well-recorded events with 5° spatial sampling globally. This sampling strategy is designed to provide the ray coverage needed to determine lithospheric-scale P wave velocity structure in the Middle East using the complementary ray geometry provided by regional (subhorizontal) and teleseismic (subvertical) raypaths and to determine a consistent, albeit lower-resolution, image of global mantle structure. The data set consists of 5401 events and 878,535 P, Pn, pP, sP, and PcP arrivals recorded at 4606 stations. Relocated epicenters are an average of 16 km from bulletin locations. The data set included events that are known to an accuracy of 1 km (a.k.a. GT1) based on nonseismic information. The average distance between GT1 epicenters and our relocated epicenters is 5.6 km. For arrivals labeled P, Pn, and PcP, ∼92%, ∼90%, and 96% are properly labeled with probability >0.9, respectively. Incorrect phase labels are found to be erroneous at rates of 0.6%, 0.2%, 1.6%, and 2.5% for P, Pn, PcP, and depth phases (pP and sP), respectively. Labels found to be incorrect, but not erroneous, were reassigned to another phase label. P and Pn residual standard deviation with respect to ak135 travel times are dramatically reduced from 3.45 s to 1.01 s. The differences between travel time residuals for nearly reciprocal raypaths are significantly reduced from the input event locations, suggesting that Bayesloc relocation improves data set consistency. The reciprocity tests suggest that the dominant contribution to travel time residuals calculated from information provided in global bulletins is location and picks errors, not travel time prediction errors due to 3-D structure. Modeling the whole multiple-event system results in accurate locations and an internally consistent data set that is ideal for tomography and other travel time calibration studies. Simmons et al. (2011) (companion paper) use the Bayesloc-processed data set to develop a 3-D tomographic image, which further reduces residual standard deviation to 0.50 s." @default.
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- W2483630810 date "2010-08-31" @default.
- W2483630810 modified "2023-09-23" @default.
- W2483630810 title "Global-scale P-wave tomography optimized for prediction of teleseismic and regional travel times for Middle East events: 1. Data set development" @default.
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