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We develop a theoretical framework for framing and solving probabilistic linear(ized) inverse problems in function spaces. This is built on the statistical theory of Gaussian Processes, and allows results to be obtained independent of any basis, avoiding any difficulties associated with the fidelity of representation that can be achieved. We show that the results of Backus–Gilbert theory can be fully understood within our framework, although there is not an exact equivalence due to fundamental differences of philosophy between the two approaches. Nevertheless, our work can be seen to unify several strands of linear inverse theory, and connects it to a large body of work in machine learning. We illustrate the application of our theory using a simple example, involving determination of Earth's radial density structure. [ABSTRACT FROM AUTHOR]

By starting from a general framework for probabilistic continuous inversion (developed in Part I) and introducing discrete basis functions, we obtain the well-known algorithms for probabilistic least-squares inversion set out by Tarantola & Valette. In doing so, we establish a direct equivalence between the spatial covariance function that must be specified in continuous inversion, and the combination of basis functions and prior covariance matrix that must be chosen for discretized inversion. We show that the common choice of Tikhonov regularization (⁠|$\mathbf {C_m^{-1}} = \sigma ^2\mathbf {I}$|⁠) arises from a delta-function spatial covariance, and that this lies behind many of the artefacts commonly associated with discretized inversion. We show that other choices of spatial covariance function can be used to generate regularization matrices yielding substantially better results, and permitting localization of features even if global basis functions are used. We are also able to offer a straightforward explanation for the spectral leakage problem identified by Trampert & Snieder. [ABSTRACT FROM AUTHOR]

Non-linear inverse problems in the geosciences often involve probabilistic sampling of multimodal density functions or global optimization and sometimes both. Efficient algorithmic tools for carrying out sampling or optimization in challenging cases are of major interest. Here results are presented of some numerical experiments with a technique, known as Parallel Tempering, which originated in the field of computational statistics but is finding increasing numbers of applications in fields ranging from Chemical Physics to Astronomy. To date, experience in use of Parallel Tempering within earth sciences problems is very limited. In this paper, we describe Parallel Tempering and compare it to related methods of Simulated Annealing and Simulated Tempering for optimization and sampling, respectively. A key feature of Parallel Tempering is that it satisfies the detailed balance condition required for convergence of Markov chain Monte Carlo (McMC) algorithms while improving the efficiency of probabilistic sampling. Numerical results are presented on use of Parallel Tempering for trans-dimensional inversion of synthetic seismic receiver functions and also the simultaneous fitting of multiple receiver functions using global optimization. These suggest that its use can significantly accelerate sampling algorithms and improve exploration of parameter space in optimization. Parallel Tempering is a meta-algorithm which may be used together with many existing McMC sampling and direct search optimization techniques. It's generality and demonstrated performance suggests that there is significant potential for applications to both sampling and optimization problems in the geosciences. [ABSTRACT FROM AUTHOR]

There is significant seismic activity in the region around Australia, largely due to the plate boundaries to the north and to the east of the mainland. This activity results in serious seismic and tsunami hazard in the coastal areas of Australia. Hence seismicity is and will be monitored in real time by Geoscience Australia (GA), which uses a network of permanent broadband seismometers. Seismic moment tensor (MT) solutions are currently determined using 1-D, radially symmetric models of Earth and this requires augmentation by recording stations located outside of Australia. A 3-D model of the Australian continent developed recently using full waveform tomography now offers the opportunity to significantly improve the determination of MT solutions of earthquakes from tectonically active regions. A complete-waveform, time-domain MT inversion method has been developed using a point-source approximation. A series of synthetic tests using first a 1-D and then a 3-D structural model has been performed. The feasibility of deploying 3-D versus 1-D Earth structure for the inversion of seismic data has been studied and the advantages of using the 3-D structural model were illustrated with examples. The 3-D model is superior to the 1-D model, as a number of sensitivity tests show. The ultimate goal of this work is an automated MT inversion system in Australia relying on GA and other international stations, although more work remains to be done before the full implementation of such a scheme in real time. [ABSTRACT FROM AUTHOR]