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We discuss several outstanding aspects of seismograms recorded during >4 weeks by a spatially dense Nodal array, straddling the damage zone of the San Jacinto fault in southern California, and some example results. The waveforms contain numerous spikes and bursts of high-frequency waves (up to the recorded 200 Hz) produced in part by minute failure events in the shallow crust. The high spatial density of the array facilitates the detection of 120 small local earthquakes in a single day, most of which not detected by the surrounding ANZA and regional southern California networks. Beamforming results identify likely ongoing cultural noise sources dominant in the frequency range 1-10 Hz and likely ongoing earthquake sources dominant in the frequency range 20-40 Hz. Matched-field processing and back-projection of seismograms provide alternate event location. Themedian noise levels during the experiment at different stations, waves generated by Betsy gunshots, and wavefields from nearby earthquakes point consistently to several structural units across the fault. Seismic trapping structure and local sedimentary basin produce localized motion amplification and stronger attenuation than adjacent regions.Cross correlations of high-frequency noise recorded at closely spaced stations provide a structural image of the subsurface material across the fault zone. The high spatial density and broad frequency range of the data can be used for additional high resolution studies of structure and source properties in the shallow crust. [ABSTRACT FROM AUTHOR]

We investigate the geometry of the Moho interface in the southern California region including the San Andreas fault (SAF), San Jacinto fault zone (SJFZ), Elsinore fault (EF) and Eastern California Shear Zone with systematic analysis of receiver functions. The data set consists of 145 teleseismic events recorded at 188 broadband stations throughout the region. The analysis utilizes a 3D velocity model associated with detailed double-difference tomographic results for the seismogenic depth section around the SAF, SJFZ, and EF combined with a larger scale community model. A 3D ray tracing algorithm is used to produce effective 1D velocity models along each source-receiver teleseismic ray. Common Conversion Point (CCP) stacks are calculated using the set of velocity models extracted for each ray. The CCP stacks are analyzed with volumetric plots, maps of maximum CCP stack values, and projections along profiles that cross major faults and other features of interest. The results indicate that the Moho geometry in the study area is very complex and characterized by large prominent undulations along the NE-SW direction. A zone of relatively deep Moho (~35-40 km) with overall N-S direction crosses the SAF, SJFZ, and EF. A section of very shallow Moho (~10 km) below and to the SE of the Salton Trough, likely associated with a young oceanic crust, produces large Moho offsets at its margins. Locations with significant changes of Moho depth appear to be correlated with fault complexity in the brittle crust. The observations also show vertical Moho offsets of ~8 km across the SAF and SJFZ close to Cajon pass, and sections with no clear Moho phase underneath Cajon pass and adjacent to the SJFZ near Anza likely produced by complex local velocity structures in the brittle upper crust. These features are robust with respect to various parameters of the analysis procedure. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data. [ABSTRACT FROM AUTHOR]