HR: 11:35h
AN: S32A-06    [Abstracts]
TI: Adjoint Tomography of Southern California
AU: * Tape, C
EM: carltape@gps.caltech.edu
AF: California Institute of Technology, 1200 E. California Blvd., MC 252-21, Pasadena, CA 91125, United States
AU: Liu, Q
EM: lqy@gps.caltech.edu
AF: California Institute of Technology, 1200 E. California Blvd., MC 252-21, Pasadena, CA 91125, United States
AU: Maggi, A
EM: alessia@sismo.u-strasbg.fr
AF: CNRS (UMR 7516) and Universite Louis Pasteur, 5 rue Rene Descartes, Strasbourg, 67084, France
AU: Tromp, J
EM: jtromp@gps.caltech.edu
AF: California Institute of Technology, 1200 E. California Blvd., MC 252-21, Pasadena, CA 91125, United States
AB: Our goal is to improve the present 3D shear and compressional velocity models of southern California. Our approach involves using adjoint methods to compute the gradient of the misfit function, which is a weighted sum of sensitivity kernels --- or Fréchet derivatives --- which represent the sensitivity of seismograms with respect to the model parameters. A tomographic inversion requires the specification of a misfit function, which we take to be frequency-dependent differences in traveltimes. We collect a set of E × R × C data seismograms, where E = 150 is the number of events, R = 200 is the number of broadband receivers, and C = 3 is the number of components, for a total of approximately 100,000 seismograms. We then compute a corresponding set of synthetic seismograms using a 3D spectral-element method (SEM) based upon a 3D southern California reference model. The differences between the data and synthetics are used in constructing a set of adjoint sources, which are placed at each of the receivers for a simulation of the adjoint wavefield. The interaction between the adjoint wavefield and the forward wavefield for each event forms an event kernel. The sum of the event kernels is the gradient of the misfit function, which illuminates the regions of the reference model that give rise to the discrepancy between data and synthetics. We apply an automated measurement algorithm that allows for a rapid, robust selection of time windows suitable for measurements between data and synthetics generated from a 3D reference model. The total number of measurements for a single event kernel is N = R × C × P, where P is the average number of time windows (per time series pair) selected for measurement by the automated procedure. All measurements are used simultaneously in constructing the combined adjoint source. The resultant event kernel requires two simulations, i.e., it is independent of N. We present several examples of event kernels for the 3D southern California velocity model using crustal phases. The event kernels are the building blocks for a gradient-based, iterative inversion to improve the velocity model while reducing the misfit function. We present preliminary results of our new tomographic model.
DE: 7205 Continental crust (1219)
DE: 7255 Surface waves and free oscillations
DE: 7270 Tomography (6982, 8180)
DE: 7290 Computational seismology
DE: 7299 General or miscellaneous
SC: Seismology [S]
MN: 2007 Fall Meeting