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Second-order moment tensors of microearthquakes at The Geysers geothermal field, California

DEPARTMENT OF GEOLOGY AND MINERALOGY THE OHIO STATE UNIVERSITY, COLUMBUS, OHIO 43210

Abstract

The Geysers geothermal field is the site of intense microseismicity which appears to be associated with steam production. It seems that focal mechanisms of earthquakes at The Geysers vary systematically with depth, but P-wave first-motion focal mechanism studies have been hampered by inadequate resolution. In this study an unconstrained frequency domain moment tensor inversion method is used to over come P-wave first-motion focal sphere distribution problems and to investigate microearthquake source properties. A goal was to investigate the feasibility of using waveforms to invert for the second-order moment tensor of microearthquakes in the complex setting of The Geysers. Derived frequency-domain moment tensors for two earthquakes were verified by mechanisms estimated from P-wave first motions and required far fewer stations. For one event, 19 P-wave first motions were insufficient to distinguish between normal-slip and strike-slip focal mechanisms, but a well-constrained strike-slip solution was obtained from the waveform principal moment inversion using data from six stations. Improved waveform focal mechanism resolution was a direct consequence of using P- and S-wave data together in a progressive velocity-hypocenter inversion to minimize Green function errors. The effects of hypocenter mislocation and velocity model Green function errors on moment tensor estimates were investigated. Synthetic tests indicate that these errors can introduce spurious isotropic and compensated linear vector dipole components as large as 26 per cent for these events, whereas principal moment orientations errors were <8°. In spite of unfavorable recording geometries and large (0.6 km) station elevation differences, the results indicate that waveform moment tensor estimates for microearthquake sources can be robust and constrain source mechanisms using data from a relatively small number of stations.

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