Double-difference Adjoint Tomography

Abstract:

We introduce the "double-difference" method, hugely popular in source inversion, in adjoint tomography. Differences between seismic observations and simulations may be explained in terms of many factors besides structural heterogeneity, e.g., errors in the source-time function, inaccurate timing, and systematic uncertainties. To alleviate nonuniqueness in the inverse problem, we make a differential measurement between stations, which largely cancels out the source signature and systematic errors. We seek to minimize the difference between differential measurements of observations and simulations at distinct stations. We show how to implement the double-difference concept in adjoint tomography, both theoretically and in practice. In contrast to conventional inversions aiming to maximize absolute agreement between observations and simulations, by differencing pairs of measurements at distinct locations, we obtain gradients of the new differential misfit function with respect to structural perturbations which are relatively insensitive to an incorrect source signature or timing errors. Furthermore, we analyze sensitivities of absolute and differential measurements. The former provide absolute information on structure along the ray paths between stations and sources, whereas the latter explain relative (and thus high-resolution) structural variations in areas close to the stations. In conventional tomography, one earthquake provides very limited structural resolution, as reflected in a misfit gradient consisting of "streaks" between the stations and the source. In double-difference tomography, one earthquake can actually resolve significant details of the structure, i.e., the double-differences provide a hugely powerful constraint on structural variations. Algorithmically, we incorporate the double-difference concept into the conventional adjoint tomography workflow by simply pairing up all regular measurements. Thus, the computational cost of the related adjoint simulations is basically unaffected, i.e., assimilating double-difference measurements does not add to the computational burden; it just modifies the construction of the adjoint sources for data assimilation.