What patterns of heterogeneity in the Earth's mantle can be revealed by seismic travel time tomography?

Abstract

We investigate which patterns of seismic heterogeneity in the mantle would be returned reliably by a tomographic inversion in which the model mantle is parametrized by a set of discrete, non-overlapping voxels. We construct synthetic data sets based on real ray sampling of the mantle by introducing spherical harmonic patterns of velocity heterogeneity and perform inversions of the synthetic data. We expand the resulting voxel model in spherical harmonics and compare the power at each degree and in each model layer with the input spherical harmonics in order to determine which patterns produced by inversions of real data may be deemed reliable and to identify patterns which must be viewed with skepticism. We find that while the power input to a particular pattern of heterogeneity in the 0–200 km layer is generally recovered accurately, the pattern itself is poorly determined in this layer. A pattern in the 200–400 km layer is more precisely determined though the power contained in the pattern is consistently underestimated and more leakage occurs to the layers above and below. The transition zone, 400–670 km, shows similarly strong control of lateral heterogeneity patterns but tests return a more accurate estimate of input powre than for the second layer. The l = 2, 4, and 6 components all show accurate recovery in the 400–670 km layer. This result supports previous findings, from inversions with real data, that l = 2 is a significant pattern of heterogeneity in the mantle's transition zone and that l = 4 is not a significant pattern. For the entire upper mantle, l = 6 would be retrieved reliably and its constructive behavior in upper mantle models derived with real data is confirmed. These tests also demonstrate the inability of our inversion procedure to retrieve shorter wavelength features in the lower mantle. The results for our lowermost layer, D″, must be considered suspect due to the inadequate constraints placed on model values by our ray coverage and the sensitivity of these results to noise in the data.