HR: 1340h
AN: S33A-1092    [Abstracts]
TI: Seismic Anisotropy From the Crust to the Core: Measurements and Interpretations with Large Datasets.
AU: * Kendall, J
EM: kendall@earth.leeds.ac.uk
AF: University of Leeds, School of Earth and Environment, Leeds, LS2 9JT United Kingdom
AU: Caddick, J
EM: j.caddick@earth.leeds.ac.uk
AF: University of Leeds, School of Earth and Environment, Leeds, LS2 9JT United Kingdom
AU: Carter, A
EM: a.carter@earth.leeds.ac.uk
AF: University of Leeds, School of Earth and Environment, Leeds, LS2 9JT United Kingdom
AU: Evans, M
EM: m.evans@earth.leeds.ac.uk
AF: University of Leeds, School of Earth and Environment, Leeds, LS2 9JT United Kingdom
AU: Teanby, N
EM: n.teanby@earth.leeds.ac.uk
AF: University of Leeds, School of Earth and Environment, Leeds, LS2 9JT United Kingdom
AB: Seismic anisotropy is caused by the alignment of crystals, shapes and layers, and results from depositional and deformational processes. As such, observations of anisotropy offer insights into the dynamical nature of the Earth on a range of length scales. Perhaps the most unambiguous indicator of anisotropy is shear-wave splitting. There are though other techniques for estimating anisotropy, many of which have been developed for oil-industry reflection data. These include the analysis of variations in reflected amplitudes as a function of offset and azimuth, non-hyperbolic travel-time moveout, and converted-wave amplitude ratios. The onset of large semi-permanent seismic arrays (e.g., USArray) will mean that many techniques for estimating anisotropy in industry datasets can be adapted to global seismic datasets. An example involves the passive seismic monitoring of microseismicity, which is used to monitor stress changes in oil-fields. The resulting datasets can be very large. For example, a recent 18-day survey in a North Sea field produced nearly 10,000 earthquake records. Shear-wave splitting analysis on such a dataset cannot be done manually. Instead we have developed automated techniques for measuring shear-wave splitting and applied it to microseismic datasets. More recently, in a global survey of upper-mantle anisotropy we have applied the same automated methodology to teleseismic datasets archived by various data centres (e.g., IRIS). It can be difficult to determine the cause of anisotropy using individual measurements and one method of analysis. In the upper-mantle, anisotropy is primarily attributed to the lattice-preferred-orientation (LPO) of crystals, but there is increasing evidence that other factors, such as melt-alignment can cause anisotropy. Petrofabric analyses using techniques such as electron back scattered diffraction can provide insight into LPO anisotropy. This has been applied to mantle xenoliths and shows that the degree of olivine alignment can be used as a strain indicator. In contrast, such analysis has shown that LPO-anisotropy in the crust is primarily controlled by mica content. However, in shallow crustal rock, preferred fracture or crack alignment also provides an effective means of generating anisotropy. Using a range of methods it is increasingly possible to unravel LPO-induced anisotropy from fracture-induced anisotropy. Similar approaches can be applied to studying the deep Earth. For example, using more than one technique and large datasets allow anisotropy due to melt alignment to be distinguished from that due to crystal alignment. We will show examples of such analyses for both a sedimentary basin setting and the upper-mantle.
DE: 8100 TECTONOPHYSICS
DE: 8120 Dynamics of lithosphere and mantle--general
DE: 8124 Earth's interior--composition and state (old 8105)
DE: 7200 SEISMOLOGY
SC: Seismology [S]
MN: 2004 AGU Fall Meeting