Adjoint Tomography of the Southern California Crust

Adjoint Tomography of the Southern California Crust Iterative improvement of a three-component seismogram. (A) Cross section of the Vs tomographic models for a path from a Mw 4.5 earthquake (star) on the White Wolf fault to station DAN (triangle) in the eastern Mojave Desert. Upper right is the initial 3D model, m00; lower right is the final 3D model, m16; and lower left is the difference between the two, ln(m16/m00). Faults labeled for reference are San Andreas (SA), Garlock (G), and Camp Rock (CR). (B) Iterative three-component seismogram fits to data for models m00, m01, m04, and m16. Also shown are synthetic seismograms computed for a standard 1D model. Synthetic seismograms (red) and recorded seismograms (black), filtered over the period range 6 to 30 s. Left column, vertical component (Z); center column, radial component (R); right column, transverse component (T). Inset “DT” label indicates the time shift between the two windowed records that provides the maximum cross-correlation.
We iteratively improve a 3D tomographic model of the southern California crust using numerical simulations of seismicwave propagation based on a spectral-element method (SEM) in combination with an adjoint method. The initial 3D model is provided by the Southern California Earthquake Center. The data set comprises three-component seismicwaveforms (i.e. both body and surface waves), filtered over the period range 2-30 s, from 143 local earthquakes recorded by a network of 203 stations. Time windows for measurements are automatically selected by the FLEXWIN algorithm. The misfit function in the tomographic inversion is based on frequency-dependent multitaper traveltime differences. The gradient of the misfit function and related finite-frequency sensitivity kernels for each earthquake are computed using an adjoint technique. The kernels are combined using a source subspace projection method to compute a model update at each iteration of a gradient-based minimization algorithm. The inversion involved 16 iterations, which required 6800 wavefield simulations. The new crustal model, m16, is described in terms of independent shear (Vs) and bulk-sound (Vb) wave speed variations. It exhibits strong heterogeneity, including local changes of +/- 30 percent with respect to the initial 3D model. The model reveals several features that relate to geological observations, such as sedimentary basins, exhumed batholiths, and contrasting lithologies across faults. The quality of the new model is validated by quantifying waveform misfits of full-length seismograms from 91 earthquakes that were not used in the tomographic inversion. The new model provides more accurate synthetic seismograms that will benefit seismic hazard assessment.
</p><p>Tape, C., Liu, Q., Maggi, A., Tromp, J., 2009, Adjoint tomography of the southern California crust, Science, 325, 988–992.
</p><p>Tape, C., Liu, Q., Maggi, A., Tromp, J., 2010, Seismic tomography of the southern California crust based on spectral-element and adjoint methods, Geophys. J. Int., 180, 433-462.
</p><p>Maggi, A., Tape, C., Chen, M., Chao, D., Tromp, J., 2009, An automated time-window selection algorithm for seismic tomography, Geophys.
J. Int., 178, 257–281.
</p><p>Acknowledgements: Seismic waveforms were provided by the data centers listed in Table 2 (IRIS, SCEDC, NCEDC). All earthquake simulations were performed on the CITerra Dell cluster at the Division of Geological & Planetary Sciences (GPS) of the California Institute of Technology. We acknowledge support by the National Science Foundation under grant EAR-0711177. This research was supported by the Southern California Earthquake Center. SCEC is funded by NSF Cooperative Agreement EAR-0106924 and USGS Cooperative Agreement 02HQAG0008.</p>


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