Unified Structural Representation of the southern California crust and upper mantle
Introduction
Recent advances in numerical methods and parallel computing technology have enabled large-scale 3D simulations of seismic wavefields in realistic earth models (e.g., Olsen et al., 1995, Komatitsch and Tromp, 1999, Komatitsch et al., 2004, Bielak et al., 2010). These simulations are able to capture the effects of basin amplification, resonance, wave focusing, and dynamic rupture propagation. Thus, they offer a physics-based alternative to attenuation relationships (e.g., Abrahamson and Silva, 1997, Abrahamson and Silva, 2008; Field, 2000, Boore and Atkinson, 2008) for forecasting the distribution of hazardous ground shaking during large earthquakes (e.g., Zhao et al., 2000, Tromp et al., 2005, Tarantola, 1984, Chen et al., 2007). These methods also provide an objective, quantitative means of using seismic observations to improve 3D earth models. The revised models, in turn, help make strong ground motion forecasts more accurate.
To facilitate these and other studies, we present a Unified Structural Representation (USR) of southern California (Fig. 1). The USR consists of two major components: a 3D description of seismic wavespeeds (Vp, Vs) and density (ρ), known as a community velocity model (CVM); and a 3D description of the major fault systems in the region, known as a community fault model (CFM). The CVM includes a framework of geologic horizons that define the various rock units in the region and integrates a wide range of direct observations that define velocity structure. These include tens of thousands of velocity measurements in boreholes, as well as constraints from seismic reflection and refraction studies in sedimentary basins. The basin structures are used to develop travel time tomographic models of the crust and upper mantle extending to a depth of 33 km, and a teleseismic shear wave model of the upper mantle to a depth of 150 km. This combined velocity model was then subjected to a series of 3D adjoint tomographic inversions that highlight areas of the starting model that were responsible for mismatches between observed and synthetic waveforms (Tape et al., 2009, Tape et al., 2010). Sixteen tomographic iterations, requiring 6800 fully 3D wavefield simulations, yielded perturbations to the starting model that have been incorporated into the current CVM. The second component of the USR is the CFM, which provides 3D descriptions of the major fault systems in southern California that are considered to pose earthquake hazards. These 3D fault representations are defined by surface geology, earthquake hypocentral locations, focal mechanisms, well, and seismic reflection data. The USR provides compatible fault and velocity models, in which the locations and displacements of major faults are explicitly represented in the velocity descriptions.
Section snippets
Tectonic history and structure
Southern California sits astride a tectonic plate boundary that has been active for at least 200 million years. Beginning in the Jurassic Period, subduction of oceanic crust beneath North America created the Sierra Nevada arc and associated igneous terrains, a widespread series of forearc deposits including the Great Valley sequence, and the Franciscan accretionary complex, which is exposed in the Coast Ranges (e.g., Hamilton, 1969, Ernst, 1970, Dickinson, 1981, Cowan and Bruhn, 1992). These
Development of a Unified Structural Representation
The USR incorporates a variety of different velocity constraints, ranging in resolution from 10-cm-scale borehole observations in shallow sedimentary sections to 3D tomographic models that describe the upper mantle at scales of tens of kilometers. These components must be assembled in a way that ensures their internal consistency. Thus, we developed a workflow for building the USR that begins with the development of structural representations of the basins and parameterization of their internal
Assembly of the USR
The upper solid surface of the USR is marked by topographic or bathymetric elevations. For bathymetry we use ETOPO-1 (Amante and Eakins, 2008) and, where available, measurements derived from seafloor reflectors of seismic surveys. For topography we used GTOPO30 (USGS, 1996). ETOPO-1 and GTOPO30 have resolutions of about 1.8 km and 0.9 km, respectively.
The various components of the USR, including the topography, basin representations, basement and Moho surfaces, tomographic crust and upper
Description
The primary velocity structures in the upper crust of southern California are the deep sedimentary basins. Average velocity functions for sediments within these basins all show general trends of increasing velocity with depth (Fig. 6A). Notably, the average velocity profiles for the Los Angeles, Ventura, and Santa Barbara basins are similar, reflecting that these basins contain comparable Neogene to recent stratigraphic sequences. The Inner California Borderland exhibits a similar velocity
Applications to earthquake simulations
A fundamental use for the USR is to provide the most accurate information available (faults and velocity structure) for earthquake simulations. These simulations, in turn, can be used to obtain better estimates of earthquake source models (e.g., Liu et al., 2004). The CVM has been tested with earthquake simulations (Komatitsh et al., 2004; Lovely et al., 2006; Tape et al., 2009, Graves and Aagaard, 2011) and with ambient noise cross correlations (Ma et al., 2008). A second purpose of the
Conclusion
We present a methodology for developing precise and internally consistent descriptions of Earth structure that span the range of wavespeed from low velocity sediments in the shallow subsurface to upper mantle structure. This involves the careful integration of many datasets, including borehole observations, seismic reflection and refraction surveys, and earthquake body and surface wave data. The workflow that we have developed for constructing the USR, involving development of basin
Acknowledgments
The authors thank Walter Mooney, Peter Shearer, and an anonymous reviewer for constructive comments that helped to improve the manuscript. This work was supported by the Southern California Earthquake Center, the National Science Foundation, and the U.S. Geological Survey. SCEC is funded by NSF Cooperative Agreement EAR-1033462 and USGS Cooperative Agreement G12AC20038. This work was also supported through NSF awards EAR-1226343 titled “Geoinformatics: Community Computational Platforms for
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