Robust array analysis of anisotropic surface waves
Creators: name: Parkes, Lou
Publisher: name: The University of Edinburgh
Year: 2024
Subjects: anisotropy
seismic surface waves
false anisotropy
array response deconvolution

description: This thesis investigates the robust analysis of azimuthal (specifically surface wave) anisotropy using array analysis. Seismic anisotropy can be related to stress, mineral composition, fracturing or layering in rock and is therefore of interest to many disciplines including hydrocarbon exploration, environmental hazard risk and civil engineering. Array analysis of surface waves has been used since early observation of nuclear weapons tests, and the applications are increasingly diverse. It is convenient as it can often be used on data which was not acquired for the purpose of array analysis. Array analysis is now often used for velocity analysis, however, the array design can limit the resolution significantly in the slowness domain. As advances are made in accounting for resolution, the observation of anisotropy becomes possible in more scenarios. Unfortunately, to use array analysis to observe seismic velocity anisotropy is particularly challenging when using arrays with anisotropic sampling and apertures and false observations of anisotropy can be made in the presence of noise. In this thesis, I approach the problem of robustly observing azimuthal anisotropy of surface waves using array analysis. In this thesis, I specifically use slowness-frequency domain analysis (equivalent to fk analysis). I examine the approach of picking slownesses, and identify the lack of consideration of systematic bias. I identify some sources of bias, including noise, parameterisation of the slowness domain, and show that interference between waves, which is not considered in other work, can have a significant biasing effect. I construct a workflow for picking slownesses that allows for robust uncertainty analysis, which is critical to robust observation of anisotropy: without uncertainty analysis there is no way of determining whether the observation is statistically significant. I show that the assumptions are generally too stringent and that this method is not robust to realistic noise. I show that bias from array geometry can be reduced significantly by designing the array to have rotational symmetry, and make other recommendations on parameter choices that can increase the likelihood of successful observation of anisotropy. I construct a workflow that makes fewer assumptions about the noise, using deconvolution of the array response and then inverting directly from the deconvolved power spectrum for anisotropic parameters. I then apply the workflow to synthetic data and show that while this workflow is more robust than the picking inversion can perform poorly when interference of waves is taken into account, since the workflow assumes waves do not interfere. I apply the methods to data. Firstly, I analyse data acquired in an experiment in the North Sea in which anisotropy has been observed with body waves but the geology is complex and the array geometry is challenging. The data used is recordings of ambient noise. I observe anisotropy, stronger at one site than the other, and the results agree with those of shear wave splitting analysis done on the same dataset. Secondly, I present data I acquired in Cornwall, where outcrops demonstrate the presence of a medium with horizontal transverse isotropy (HTI) in the near surface. This experiment was with an active seismic source and so was an independent test with different challenges, including the presence of body wave noise. Both refraction results and array analysis agree that there is strong anisotropy with roughly the expected orientation. The results on real data show that the deconvolution workflow with direct inversion for anisotropic parameters is effective in situations where a slowness picking workflow is not and that deconvolution should be used where possible when using array analysis to observe anisotropy.