Imaging the high-temperature geothermal field at Krafla using vertical seismic profiling

Abstract

Geophysical exploration and in particular active-source seismic imaging of geothermal fields is important to assess and optimize the exploitation of natural heat sources for energy production and direct use. The first multi-offset (moving-source) vertical seismic profiling (VSP) experiment over the high-temperature geothermal field in Krafla (Iceland) was carried out in spring 2014 with the aim to test whether VSP is a suitable method to map volcanic stratigraphy, fractures, dykes, steam zones and magmatic bodies at this site and for volcanic environments in general. In this study, we present a workflow for processing the sparse Krafla VSP dataset recorded with receivers in either of two boreholes. The analysis involved first-arrival traveltime inversion and seismic reflection processing. The seismic velocity model obtained by traveltime tomography reveals structural information between the two boreholes and can be linked to an existing geological model, showing that the seismic velocities are mainly controlled by lithology. The zero-offset seismic reflection data were processed into two corridor stacks. Walk-away VSP reflection data were migrated with a novel multicomponent Kirchhoff migration algorithm that includes P- and S-wave isolation to obtain separate PP, PS and SS migrated images. The reflections imaged in the corridor stacks can be linked to the main lithological units known from borehole logging information. Migrated images from the walk-away data reveal reflectors below and to the sides of the two boreholes. Considering à priori information, such as hypocenter locations from earthquake seismology studies, the reflectors can be related to changes in lithology, fault zones, dykes and possibly the top of the Krafla magma chamber. We found that VSP is potentially a useful method to image the key lithological boundaries and volcanic stratigraphy in the complex magmatic environment at Krafla, but à priori information proved to be essential to constrain the processing and interpretation of the sparse array dataset.