Bayesian inversion of surface wave data for discontinuities and velocity structure in the upper mantle using Neural Networks. Geologica Ultraiectina (287)

Abstract

We present a neural network approach to invert surface wave data for discontinuities and velocity structure in the upper mantle. We show how such a neural network can be trained on a set of random samples to give a continuous approximation to the inverse relation in a compact and computationally efficient form. The trained networks are applied to a real data set consisting of surface wave dispersion measurements. For each inversion, performed on a global grid, we obtain the a posteriori probability density function (pdf) of crustal thickness and independently of vertically averaged crustal shear wave velocity. From this pdfs, any desired statistic such as mean and variance can be computed. The obtained results are compared with current knowledge of crustal structure. Generally our results are in good agreement with other crustal models. However in certain regions such as central Africa and the back arc of the Rocky Mountains we observe a thinner crust than the other models. We also see evidence for thickening of oceanic crust with increasing age. We show that the resulting model allows to compute global crustal corrections for surface wave tomography for periods T>50 s for phase velocities and T>60 s for group velocities. In a second application we invert surface wave overtone measurements for posterior pdfs of the 400- and 660-km discontinuities as well as the transition zone thickness. We find that posterior uncertainties are almost as large as the observed variations of the mean 400- and 660-km depth as well as the mean transition zone thickness. Nevertheless, the model of the mean 660-km topography agrees reasonably well with other global models constrained by SS precursors, giving further evidence for a well defined and sharp 660-km discontinuity. The situation for the 400-km discontinuity however is more complicated. Agreement between four different 400-km topography models is very poor and we find a global average discontinuity depth of 394 km which is significantly lower than 409 and 418 km reported previously. Surface wave overtones and SS precursors should give similar results for a sharp 400-km discontinuity. If the 400-km discontinuity however is broad, it might be that surface wave overtones map an average depth that differs from the bottom of the discontinuity as mapped by SS precursors. Experimental studies suggest that if water concentrations are sufficiently high the 400-km discontinuity might be as broad as 20 km. The observed difference in the mean depth of the 400-km discontinuity between surface wave overtone measurements and SS precursor studies might therefore be indicative of high water concentrations near 400-km depth on a global scale.