HR: 1330h
AN: H22C-0947    [PDF]
TI: Autoregressive Extrapolation for Seismic Tomography problems with Applications to Soil and Rock Physics
AU: * Li, C
EM: lcpdq@purdue.edu
AF: Cuiping Li & Robert L Nowack, Purdue University, Dept. of Earth and Atmos. Sci., West Lafayette, IN 47907 United States
AU: Nowack, R L
EM: nowack@purdue.edu
AF: Cuiping Li & Robert L Nowack, Purdue University, Dept. of Earth and Atmos. Sci., West Lafayette, IN 47907 United States
AU: Pyrak-Nolte, L
EM: ljpn@physics.purdue.edu
AF: Laura J Pyrak-Nolte, Purdue University, Dept. of Physics and Dept. of Earth and Atmos Sci., West Lafayette, IN 47907 United States
AB: Seismic tomographic experiments in soil and rock are strongly affected by limited and non-uniform ray coverage. We propose a new method to extrapolate data used for seismic tomography to full coverage. The proposed two-stage autoregressive extrapolation technique can be used to extend the available data and provide better tomographic images. The algorithm is based on the principle that the extrapolated data adds minimal information to the existing data. A two-stage autoregressive (AR) extrapolation scheme is then applied to the seismic tomography problem. The first stage of the extrapolation is to find the optimal prediction-error filter (PE filter). For the second stage, we use the PE filter to find the values for the missing data so that the power out of the PE filter is minimized. At the second stage, we are able to estimate missing data values with the same spectrum as the known data. This is similar to maximizing an entropy criterion. Synthetic tomographic experiments have been conducted and demonstrate that the two-stage AR extrapolation technique is a powerful tool for data extrapolation and can improve the quality of tomographic inversions of experimental and field data. Moreover, the two-stage AR extrapolation technique is tolerant to noise in the data and can still extrapolate the data to obtain overall patterns, which is very important for real data applications. In this study, we have applied AR extrapolation to a series of datasets from laboratory tomographic experiments on synthetic sediments with known structure. In these tomographic experiments, glass beads saturated with de-ionized water were used as the synthetic water-saturated background sediments. The synthetic sediments were packed in plastic cylindrical containers with a diameter of 220 mm. Tomographic experiments were then set up to measure transmitted acoustic waves through the sediment samples from multiple directions. We recorded data for sources and receivers with varying angular coverage and used the data to tomographically reconstruct the internal sediment structures. The new tomographic inversion strategies using AR extrapolation should enable better delineation of structures in soil and rock which is important for characterizing the near-surface. Acknowledgments: LJPN acknowledges Purdue University Faculty Scholar program at Purdue University.
DE: 1899 General or miscellaneous
DE: 7299 General or miscellaneous
SC: Hydrology [H]
MN: 2003 Fall Meeting