Seismic tomography using parameter-free Backus-Gilbert inversion

This proof-of-concept study presents a parameter-free, linear Backus-Gilbert inversion scheme, tractable for seismic tomography problems. It leads to efficient computations of unbiased tomographic images, accompanied by meaningful resolution and uncertainty informations. Moreover, as there is no need to parametrize the model space in this parameter-free approach, it enables numerically accurate data sensitivity kernels to be effectively exploited in tomographic inversions. This is a major benefit over discrete tomographic methods, for which data sensitivity kernels are often inaccurate, as they are projected on a given model parametrization prior to be exploited in the inversion, and these parametrizations are usually coarse to limit the number of parameters and keep tractable the problems of model estimation and/or appraisal. Therefore, this new tomographic scheme fuels great hopes on better constraining multiscale seismic heterogeneities in the Earth's interior by exploiting accurate data sensitivity kernels, that is, taking full advantage of known wave-propagation physics, and enabling quantitative appraisals of tomographic features. As a remark, since its computational cost grows as a function of the total number of data squared, it may be better suited to handle moderate-size data sets, typically encountered in regional-scale tomography. Theoretical developments are illustrated within a finite-frequency physical framework. A set of teleseismic S-wave time residuals is inverted, with focus on imaging and appraising shear-wave velocity anomalies lying in the mantle below Southeast Asia, in the 350-1410km depth range.