Shear Wave Splitting Inversion in a Complex Crust

Abstract:

Shear wave splitting (SWS) inversion presents a method whereby the upper crust can be interrogated for fracture density. It is caused when a shear wave traverses an area of anisotropy, splits in two, with each wave experiencing a different velocity resulting in an observable separation in arrival times. A SWS observation consists of the first arrival polarization direction and the time delay. Given the large amount of data common in SWS studies, manual inspection for polarization and time delay is considered prohibitively time intensive. All automated techniques used can produce high amounts of observations falsely interpreted as SWS. Thus introducing error into the interpretation. The technique often used for removing these false observations is to manually inspect all SWS observations defined as high quality by the automated routine, and remove false identifications. We investigate the nature of events falsely identified compared to those correctly identified. Once this identification is complete we conduct a inversion for crack density from SWS time delay. The current body of work on linear SWS inversion utilizes an equation that defines the time delay between arriving shear waves with respect to fracture density. This equation makes the assumption that no fluid flow occurs as a result of the passing shear wave, a situation called squirt flow. We show that the assumption is not applicable in all geological situations. When it is not true, its use in an inversion produces a result which is negatively affected by the assumptions. This is shown to be the case at the test case of 6894 SWS observations gathered in a small area at Puna geothermal field, Hawaii. To rectify this situation, a series of new time delay formulae, applicable to linear inversion, are derived from velocity equations presented in literature. The new formula use a ‘fluid influence parameter’ which indicates the degree to which squirt flow is influencing the SWS. It is found that accounting for squirt flow better fits the data and is more applicable. The fluid influence factor that best describes the data can be identified prior to solving the inversion. Implementing this formula in a linear inversion has a significantly improved fit to the time delay observations than that of the current methods.