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Inversions of planetary gravity are aimed at constraining the mass distribution within a planet or moon. In many cases, constraints on the interior structure of the planet, such as the depth of density anomalies, must be assumed a priori , to reduce the non-uniqueness inherent in gravity inversions. Here, we propose an alternative approach that embraces the non-uniqueness of gravity inversions and provides a more complete view of related uncertainties. We developed a Transdimensional Hierarchical Bayesian (THB) inversion algorithm that provides an ensemble of mass distribution models compatible with the gravitational field of the body. Using this ensemble of models instead of only one, it is possible to quantify the range of interior parameters that produce a good fit to the gravity acceleration data. To represent the interior structure of the planet or moon, we parametrize mass excess or deficits with point masses. We test this method with synthetic data and, in each test, the algorithm is able to find models that fit the gravity data of the body very well. Three of the target or test models used contain only point mass anomalies. When all the point mass anomalies in the target model produce gravity anomalies of similar magnitudes and the signals from each anomaly are well separated, the algorithm recovers the correct location, number and magnitude of the point mass anomalies. When the gravity acceleration data of a model is produced mostly by a subset of the point mass anomalies in the target model, the algorithm only recovers the dominant anomalies. The fourth target model is composed of spherical caps representing lunar mass concentration (mascons) under major impact basins. The algorithm finds the correct location of the centre of the mascons but fails to find their correct outline or shape. Although the inversion results appear less sharp than the ones obtained by classical inversion methods, our THB algorithm provides an objective way to analyse the interior of planetary bodies that includes epistemic uncertainty. [ABSTRACT FROM AUTHOR]

Low-velocity layers within the crust can indicate the presence of melt and lithologic differences with implications for crustal composition and formation. Seismic wave conversions and reverberations across the base of the crust or intracrustal discontinuities, analysed using the receiver function method, can be used to constrain crustal layering. This is commonly accomplished by inverting receiver functions jointly with surface wave dispersion. Recently, the proliferation of model-space search approaches has made this technique a workhorse of crustal seismology. We show that reverberations from shallow layers such as sedimentary basins produce spurious low-velocity zones when inverted for crustal structure with surface wave data of insufficiently high frequency. Therefore, reports of such layers in the literature based on inversions using receiver function data should be re-evaluated. We demonstrate that a simple resonance-removal filter can suppress these effects and yield reliable estimates of crustal structure, and advocate for its use in receiver-function based inversions. [ABSTRACT FROM AUTHOR]

Inversion of surface wave data for crustal and upper-mantle structure is a staple of passive seismology, particularly since the advent of techniques enabling surface wave dispersion (SWD) and Rayleigh wave ellipticity measurements from ambient noise. Recent development and application of transdimensional Bayesian (TB) seismic inversion offers an approach to quantify model parameter uncertainties and trade-offs with fewer assumptions than traditional methods. Using synthetic tests, we investigate choices in the implementation of TB for the inversion of SWD and Rayleigh wave ellipticity to constrain the structure of Earth's continental lithosphere. We focus on three aspects of the inversion: limitation of data sensitivity, assumed scaling among parameters (compressional wave speed, Vp, shear wave speed, Vs, density and radial anisotropy) and parametrization choices. We show that while surface wave data provide relatively strong constraints on the posterior distribution of Vs and, to a lesser extent, Vp, common parametrization choices can potentially bias structure estimates. This is particularly the case for radial anisotropy (ξ), due to the inability to distinguish variations of Vp and density from those of ξ. Inferred results therefore depend substantially on the parametrization and scaling choices. We illustrate how layered parametrizations can, in the TB framework, recover smoothly varying profiles, and quantify the number of layers recoverable at different levels of measurement uncertainty. Finally, we propose two types of model parametrization for TB inversion involving multiple types of parameters. We demonstrate that by implementing an independent parametrization for different physical quantities, we can avoid imposing identical model geometry across multiple types of model parameters, and obtain better model estimates with reduced trade-offs. We advocate for such a parametrization in TB inversion of radial anisotropy using surface wave data, and when targeting disparate Vp and Vs structures such as those associated with |$\alpha $| - |$\beta $| quartz transtion. [ABSTRACT FROM AUTHOR]