We propose a new approach capable of measuring local seismic anisotropy from 6C (three-component translation and three-component rotation) amplitude observations of ambient seismic noise data. Our recent theory demonstrates that the amplitude ratio of 6C cross-correlation functions (CCFs) enables retrieving the local phase velocity. This differs from conventional velocity extraction methods based on travel time. Its local sensitivity kernel beneath the 6C seismometer allows us to study anisotropy from azimuth-dependent CCFs, avoiding path effects. Such point measurements have great potential in planetary exploration, ocean bottom observations, or volcanology. We apply this approach to a small seismic array at Pinon Flat Observatory (PFO) in southern California, array-deriving retrieves rotational ground motions from microseismic noise data. The stress and structure-induced anisotropy is well resolved and compatible with other tomography results, providing constraints on the origin of depth-dependent seismic anisotropy.

The estimation of crustal structure and thickness is instrumental in understanding the formation and evolution of terrestrial planets. Initial planetary missions with seismic instrumentation on board face the additional challenge of dealing with seismic activity levels that are only poorly constrained a priori. For example, the lack of plate tectonics on Mars leads to low seismicity which could in turn hinder the application of many terrestrial data analysis techniques. Here we propose using a joint inversion of receiver functions and apparent incidence angles, which contain information on absolute S-wave velocities of the subsurface. Since receiver function inversions suffer from a velocity depth trade-off, we in addition exploit a simple relation which defines apparent S-wave velocity as a function of observed apparent P-wave incidence angles to constrain the parameter space. We then use the Neighbourhood Algorithm for the inversion of a suitable joint objective function. The resulting ensemble of models is then used to derive uncertainty estimates for each model parameter. In preparation for analysis of data from the InSight mission, we show the application of our proposed method on Mars synthetics and sparse terrestrial data sets from different geological settings using both single and multiple events. We use information theoretic statistical tests as a model selection criteria and discuss their relevance and implications in a seismological framework.