Traveltime tomography in anisotropic media—II. Application

Abstract

Cross‐borehole seismic data have traditionally been analysed by inverting the arrival times for velocity structure (traveltime tomography). The presence of anisotropy requires that tomographic methods be generalized to account for anisotropy. This generalization allows geological structure to be correctly imaged and allows the anisotropy to be evaluated. In a companion paper we developed linear systems for 2‐D traveltime tomography in anisotropic media. In this paper we analyse the properties of the linear system for quasi‐compressional waves and invert both synthetic and real data. Solutions to the linear systems consist of estimates of the spatial distributions of five parameters, each corresponding to a linear combination of a small subset of the 21 elastic, anisotropic velocity parameters. The parameters describe the arrival times in the presence of weak anisotropy with arbitrary symmetries. However, these parameters do not, in general, describe the full nature of the anisotropy. The parameters must be further interpreted using additional information on the symmetry system. In the examples in this paper we assume transverse isotropy (TI) in order to interpret our inversions, but it should be noted that this final interpretation could be reformulated in more general terms. The singular value decomposition of the linear system for traveltime tomography in anisotropic media reveals the (expected) ill‐conditioning of these systems. As in isotropic tomography, ill‐conditioning arises due to the limited directional coverage that can be achieved when sources and receivers are located in vertical boreholes. In contrast to isotropic tomography, the scalelength of the parametrization controls the nature of the parameter space eigenvectors: with a coarse grid all five parameters are required to model the data; with a fine grid some of the parameters appear only in the null space. The linear systems must be regularized using external, a priori information. An important regularization is the expectation that the elastic properties vary smoothly (an ad hoc recognition of the insensitivity of the arrival times to the fine‐grained properties of the medium). The expectation of smoothness is incorporated by using a regularization matrix that penalizes rough solutions using finite difference penalty terms. The roughness penalty sufficiently constrains the solutions to allow the smooth eigenvectors in the null space of the unconstrained problem to contribute to the solutions. Hence, the spatial distribution of all five parameters is recovered. The level of regularization required is difficult to estimate; we advocate the analysis of a suite of solutions. Plots of the solution roughness against the data residuals can be used to find ‘knee points’, but for the fine tuning of the regularization one has little recourse but to examine a suite of images and use geological plausibility as an additional criterion. The application of the regularized numerical scheme to the synthetic data reveals that the roughness penalty should include terms that penalize high gradients addition to penalizing high second derivatives. Only when this constraint was included were the features of the original model recovered. The inversions of the field data yield good images of the expected stratigraphy and confirm previous estimates of the magnitude of the anisotropy and the orientation of the symmetry axis. The solutions further indicate an increase in anisotropy from the top to the bottom of the survey region that was not previously detected. Copyright © 1992, Wiley Blackwell. All rights reserved