DI21B-07:
Constraints on Melt Geometry and Distribution from Seismic Anisotropy

Tuesday, 16 December 2014: 9:30 AM
James O S Hammond, Imperial College London, London, SW7, United Kingdom
Abstract:
Geophysically imaging melt beneath volcanic systems has been a long-standing goal in Earth Science. It presents the only way of taking a snapshot of the magmatic system, and thus offers clues to the eruptive potential of potentially hazardous volcanoes. Techniques such as magnetotellurics or seismic tomography have provided invocative images of the magmatic system, furthering our understanding that a volcano is not simply underlain by a small shallow magma chamber occasionally connected to the surface, but likely lies above a much larger interconnected crystal-melt-mush (CMM) zone, where most of the melt is stored before being transported to shallow depths. However, despite these breakthroughs it has remained difficult to estimate the details of melt storage; in particular the shape and amount of melt stored in the magmatic system.

In most settings melt is likely to retain a preferential orientation, whether through being stored as dikes or sills in the crust, or through the formation of melt bands or preferentially oriented inclusions or channels in the mantle. This will cause significant seismic anisotropy, with the amount and symmetry of the anisotropy dictated by the nature of melt segregation. Thus, measurements of seismic anisotropy offer a richer dataset than simply measuring absolute velocities alone.

Here I will show the typical characteristics of melt induced anisotropy that may be observed in a variety of teleseismic techniques (shear-wave splitting, receiver functions, surface waves, Pn). I apply these observations to datasets from the East-African rift and other magmatic regions showing the nature of partial melt storage in the mantle and deep crust. These datasets offer the potential to better understand the storage characteristics of melt, especially when combined with other geophysical, geodetic and geological datasets such as magnetotellurics, GPS, InSAR and petrology.