We have developed an automatic P-wave arrival detection and picking algorithm based on the wavelet transform and Akaike information criteria (AIC) picker. Wavelet coefficients at high resolutions show the fine structure of the time series, and those at low resolutions characterize its coarse features. Primary features such as the P-wave arrival are retained over several resolution scales, whereas secondary features such as scattered arrivals decay quickly at lower resolutions. We apply the discrete wavelet transform to single-component seismograms through a series of sliding time windows. In each window the AIC autopicker is applied to the thresholded absolute wavelet coefficients at different scales, and we compare the consistency of those picks to determine whether a P-wave arrival has been detected in the given time window. The arrival time is then determined using the AIC picker on the time window chosen by the wavelet transform. We test our method on regional earthquake data from the Dead Sea Rift region and local earthquake data from the Parkfield, California region. We find that 81% of picks are within 0.2-sec of the corresponding analyst pick for the Dead Sea dataset, and 93% of picks are within 0.1 sec of the analyst pick for the Parkfield dataset. We attribute the lower percentage of agreement for the Dead Sea dataset to the substantially lower signal-to-noise ratio of those data, and the likelihood that some percentage of the analyst picks are in error.

We have obtained velocity images of the uppermost mantle beneath China by performing tomographic inversion of both Pn and Sn traveltimes. From the Annual Bulletin of Chinese Earthquakes, 99,139 Pn arrivals and 43,646 Sn arrivals were selected. Pn anisotropy was also obtained simultaneously with Pn velocity. Average Pn and Sn velocities are 8.05 and 4.55 km/s, respectively, and maximum velocity perturbations are about 3–4%. The Pn and Sn velocities are low in eastern China and high in western China. Particularly high velocities are associated with old basins (for example, Tarim, Junggar, Turpan‐Hami, Qaidam, and Sichuan) and stable craton (for example, Ordos). Low Sn velocities are found mainly throughout North China. In addition, velocities are relatively low beneath the central Tibetan Plateau and the North‐South Seismic Zone (along 103°E). In Tarim and central China where we observe strong anisotropy, the fast Pn velocity directions are consistent with the directions of maximum principal compressive stress as well as directions of crustal movement determined from Global Positioning System. Beneath the India‐Eurasia collision zone, the Pn anisotropy direction is parallel to the collision arc and nearly perpendicular to both the direction of maximum compression and crustal movement resulting from pure shear deformation. Both the velocity variations and anisotropy indicate that the Tibetan Plateau was extruded, and the mantle material beneath the plateau has flowed around the East Himalaya Syntax, while the remaining material has diverted northwestward beneath the Tarim Basin.

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We develop a Regional Seismic Travel Time (RSTT) model and methods to account for the first-order effect of the three-dimensional crust and upper mantle on travel times. The model parameterization is a global tessellation of nodes with a velocity profile at each node. Interpolation of the velocity profiles generates a 3-dimensional crust and laterally variable upper mantle velocity. The upper mantle velocity profile at each node is represented as a linear velocity gradient, which enables travel time computation in approximately 1 millisecond. This computational speed allows the model to be used in routine analyses in operational monitoring systems. We refine the model using a tomographic formulation that adjusts the average crustal velocity, mantle velocity at the Moho, and the mantle velocity gradient at each node. While the RSTT model is inherently global and our ultimate goal is to produce a model that provides accurate travel time predictions over the globe, our first RSTT tomography effort covers Eurasia and North Africa, where we have compiled a data set of approximately 600,000 Pn arrivals that provide path coverage over this vast area. Ten percent of the tomography data are randomly selected and set aside for testing purposes. Travel time residual variance for the validation data is reduced by 32%. Based on a geographically distributed set of validation events with epicenter accuracy of 5 km or better, epicenter error using 16 Pn arrivals is reduced by 46% from 17.3 km (ak135 model) to 9.3 km after tomography. Relative to the ak135 model, the median uncertainty ellipse area is reduced by 68% from 3070 km 2 to 994 km 2 , and the number of ellipses with area less than 1000 km 2 , which is the area allowed for onsite inspection under the Comprehensive Nuclear Test Ban Treaty, is increased from 0% to 51%.

We have developed an adaptive, automatic, correlation-and clusteringbased method for greatly reducing the degree of picking inconsistency in large, digital seismic catalogs and for quantifying similarity within, and discriminating among, clusters of disparate waveform families. Innovations in the technique include (1) the use of eigenspectral methods for cross-spectral phase estimation and for providing subsample pick lag error estimates in units of time, as opposed to dimensionless relative scaling of uncertainties; (2) adaptive, cross-coherency-based filtering; and (3) a hierarchical waveform stack correlation method for adjusting mean intercluster pick times without compromising tight intracluster relative pick estimates. To solve the systems of cross-correlation lags we apply an iterative, optimized conjugate gradient technique that minimizes an L 1-norm misfit. Our repicking technique not only provides robust similarity classification-event discrimination without making a priori assumptions regarding waveform similarity as a function of preliminary hypocenter estimates, but also facilitates high-resolution relocation of seismic sources. Although knowledgeable user input is needed initially to establish run-time parameters, significant improvement in pick consistency and waveform-based event classification may be obtained by then allowing the programs to operate automatically on the data. The process shows promise for enhancing catalog reliability while at the same time reducing analyst workload, although careful assessment of the automatic results is still important.